Comprehensive Quality Analysis of an Organic Alternative Curing Process in Boneless Ham Using Consumer Sensory Evaluation and Artificial Intelligence

Comprehensive Quality Analysis of an Organic Alternative Curing Process in Boneless Ham Using Consumer Sensory Evaluation and Artificial Intelligence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive Quality Analysis of an Organic Alternative Curing Process in Boneless Ham Using Consumer Sensory Evaluation and Artificial Intelligence Siyuan Sheng, Zihan Sun, Steven Ricke, James Claus This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8905083/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract United States regulators and the meat industry have recently called for a shift from conventional natural curing ingredients to organic alternatives. However, the curing efficacy and consumer acceptance of these organic options remain unclear. Fully cooked, water-added boneless ham served as the model for assessing these ingredients in this study. The study evaluated the effects of commercial conventional and organic plant-sourced curing agents on ham quality using sensory evaluation, instrumental analysis, machine learning, and natural language processing (NLP). Five treatments were analyzed: pre-converted celery (CEL), organic celery (OCEL), Swiss chard (SW), organic Swiss chard (OSW), and sodium nitrite (SN). Consumer panels indicated no differences ( p > 0.05) in overall liking or purchase intent across treatments. However, OSW exhibited a greater non-meat aftertaste compared to SW ( p = 0.013) and SN ( p = 0.033). Traditional statistical methods such as principal component analysis (PCA) and correlation studies revealed that non-meat aftertaste was positively correlated ( r = 0.61) with terpenoids and negatively correlated ( r = -0.53) with esters, furans, and sulfur-containing compounds. Novel artificial intelligence tools such as machine learning classification of objective color measurements identified the 650/570 nm absorbance ratio as a key differentiating feature across treatments. Furthermore, NLP analysis of open-ended comments identified structured themes, where 'aftertaste/off note' language showed a significant association with lower overall liking. These findings demonstrate the value of integrating advanced data analytics with traditional meat science methodology. Boneless ham Organic Agriculture Alternative Curing GC-MS/MS Natural Language Processing Artificial Intelligence Consumer Sensory Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Pork is considered the most versatile of meats, with cooking applications ranging from roasted loin to dry-cured ham and bacon (Rogers 2012 ). In the United States, ready-to-eat boneless ham has evolved from traditional cured bone-in cured products into an economical, convenient staple. Typically produced using chunked and formed meat, these products were popularized by innovations such as canned ham and Spam (George A. Hormel & Company), which catered to consumer demands for ease of slicing and serving (Waxman 2017 ). The U.S. Department of Agriculture (USDA) defined boneless ham as a fully cooked and cured product, potentially processed in a casing or can that must comply with minimum protein fat-free (PFF) percentages ranging from 16.0% to 19.5% (USDA 1996 ). Conventionally, ham is cured using synthetic sodium nitrite and sodium erythorbate (Rasmussen and Sullivan 2019 ). These compounds are well-documented for their efficacy in preventing lipid oxidation (Tatiyaborworntham et al. 2022 ), imparting characteristic cured color and flavor, and suppressing the growth of bacteria, including Clostridium botulinum and other spoilage and pathogenic microorganisms (Sheng et al. 2025a ). The global organic food market hit the highest growth rate ever in 2020, exceeding $ 130 billion and the organic meat market has reached an estimated size of $ 1.5 billion (Rizzo et al. 2023 ). Consumers who purchased organic or natural products before the COVID-19 pandemic have maintained or increased their consumption post-pandemic (Brata et al. 2022 ). Consequently, the growing demand for natural food and processed meat has led to the development of alternative curing methods using green leafy vegetable-based nitrate sources, such as celery or Swiss chard juice or powder (Arsenault 2019 ). Celery power has been commonly used in the organic meat industry as an alternative meat-curing ingredient since the inception of the USDA National Organic Program (USDA 2024a ). Alternative curing agents that are derived from vegetable sources are perceived as healthier options by consumers due to their natural origin and absence of synthetic additives. The National Organic Standards Board (NOSB) currently permits the use of non-organic celery powder in organic products while conducting sunset reviews to transition to organic sources of celery powder. This involves evaluating whether non-organic celery powder should remain on the National List of Allowed and Prohibited Substances or be replaced with organic alternatives (NOSB 2023 ). Recent studies have highlighted the impact of different natural source curing agents on the sensory attributes and consumer acceptance of processed meats (Jin et al. 2018 ; Sullivan et al. 2012 ; Pham et al. 2008 ; Sheng et al. 2025c ). Alternative curing process using plant-derived nitrite sources can influence the final product's flavor profile and non-meat aftertaste potentially due to the presence of plant phytochemicals and their fermented derivatives (Siekmann et al. 2021 ). The chlorophyll and carotenoid content in organically grown vegetables can vary depending on cultivation practices (Chausali and Saxena 2021 ; Yu et al. 2018 ), tentatively affecting the color of processed meats (Sheng et al. 2025d ). It is unknown whether the presence of phytochemicals in vegetable-based curing powders could affect the flavor of alternative cured boneless ham. Moreover, it remains unclear whether the development of volatile organic compounds (VOCs) during cooking could further impact consumer perceptions and preferences (Sheng et al. 2025b ). By investigating the influence of both conventional and organic curing ingredients, this research aims to provide insights into how these ingredients derived from different growing practices may affect the sensory properties and overall consumer acceptance of cured boneless ham products. Thus, the objective of this study was to investigate the effects of conventional and organic curing agents on the quality and sensory attributes of cured boneless ham products. Our investigation included physicochemical analysis (proximate analysis, color, and cure efficiency), sensory evaluation using consumer panelists, and the analysis of volatile organic compounds (VOCs) in the finished products. In addition to traditional instrumental methodologies, artificial intelligence techniques, specifically machine learning and natural language processing (NLP), were applied to analyze high-dimensional data. Understanding these distinctions is crucial for government regulators and meat processors aiming to meet consumer demands for both traditional and organic products while ensuring excellence in product quality. 2. Material and Methods 2.1 Experiment design This study employed both instrumental and sensory analyses to evaluate the impact of various plant-sourced nitrites on the quality and consumer acceptability of ready-to-eat (RTE) boneless ham. A total of 5 treatments (TRT)s were incorporated into the experiment design. A nitrite concentration of 200 ppm of equivalent nitrite was formulated to a 13.34 kg meat block of meat ingredients originating from a synthetic chemical curing ingredient (Sure Cure, Excalibur Seasoning, Wichita, KS, USA. Ingredient contains Salt, Sodium Nitrite (SN, 6.25%), FD&C Red #3) and a vegetable source curing powder included conventionally grown celery (CEL) and Swiss chard (SW) as well as organic grown celery (OCEL) and Swiss chard (OSW). Meat curing was accelerated with sodium erythorbate (547 ppm) for SN and cherry powder for the vegetable source nitrite TRTs. The main factorial design was a split plot for measurement over time (day of sampling). Physicochemical properties’ data including cured meat pigments (CMP), total meat pigments (TMP), total myoglobin content (TMC), and proximate analysis data (moisture, fat, and protein) were analyzed on day 0 with duplicate measurements. Objective color, residual nitrite, and pH were assessed on a 15-day interval from day 0 to day 45 with triplicate measurements. Sensory evaluations were conducted between day 14 and 21. The experiment model included the main effects of treatments, day of sampling, and the interaction of treatment × day. 2.2 Product formulation and manufacturing Ready-to-eat boneless ham was formulated to contain 200 ppm sodium nitrite or its equivalent when cured with vegetable-source nitrite curing powder. Prior to formulation, the nitrite concentrations of all commercially available vegetable-source curing powders were quantified using a high-performance liquid chromatography (HPLC) system (ENO-20 NOx Analyzer, Eicom Inc., Kyoto, Japan). The nitrite ion equivalents were determined to be 21,487.1, 18,436.1, 23,145.3, and 21,335.6 ppm for CEL, SW, OCEL, and OSW, respectively. A curing accelerator was added at 547 ppm sodium erythorbate for the SN treatment and at 547 ppm ascorbate equivalent for the vegetable-source nitrite treatments. Frozen boneless ham was prepared using an equal ratio of fresh pork biceps femoris and semimembranosus with attached semitendinosus muscles, sourced from a local supplier with a recent pack date. Frozen meat was stored at -20°C and subsequently thawed in a temperature-controlled cooler (air temperature: 2°C) five days prior to product manufacture. The raw meat was inspected before processing, and excessive silver skin and fat were trimmed to enhance meat binding properties. All treatments were formulated and manufactured in a randomly selected order. The meat ingredients were ground using a commercial meat grinder (Model 538A, The Biro Manufacturing Company, Marblehead, OH, U.S.A.) equipped with a kidney-shaped plate (400 3HK Triumph Kidney Plate, Speco Inc., Schiller Park, IL, U.S.A.). The ground meat was subsequently mixed with a formulated brine (Table 1 ) at a 20% inclusion rate and transferred to vacuum tumblers, where it was tumbled continuously for one hour under vacuum (approximately − 0.75 MPa) at 1°C to achieve adequate protein extraction and brine pick-up. The mixture for each treatment was held overnight in a temperature-controlled cooler (air temperature: 2.2°C), covered with moisture-impermeable butcher paper. The mixtures were then transferred to a rotary vane vacuum stuffing machine (Model VF616 Vacuum Stuffer, Handtmann Inc., Lake Forest, IL, U.S.A.) and stuffed into 105.1 mm moisture-impermeable plastic casings. The stuffed hams were thermally processed using a ramped steam cook schedule of 60.0°C, 65.6°C, 71.1°C, 76.7°C, and 82.2°C until an internal temperature of 70°C was reached. After thermal processing, the hams were transferred to a ready-to-eat area and chilled for 12 hours at 1°C. The hams were peeled, sliced into 3 mm and 20 mm slices, and vacuum-packaged for 20 seconds using a double-chamber vacuum machine (Multivac C 500, The Multivac Group, Kansas City, MO, U.S.A.) with 4 mil barrier bags (Uline S-19920, Uline Inc., Pleasant Prairie, WI, U.S.A.). Packaged ham slices were stored in an LED-lighted cooler at 1°C for a 45-day sampling period for all sensory and physicochemical analyses. Table 1 Formulations 1 for ready to eat (RTE) boneless ham. Treatment 2 Water 3 Curing ingredients Curing accelerator Total NMI (%) (%) (%) (%) SN 17.62 0.32 0.05 20.00 CEL 16.63 0.89 0.46 20.00 OCEL 16.37 1.15 0.46 20.00 SW 16.71 0.81 0.46 20.00 OSW 16.81 0.72 0.46 20.00 1 Formulations: Non-meat ingredients (NMI) were mixed with 11.34 kg of meat ingredients (fresh pork biceps femoris and semimembranosus at equal ratio). NMI percentages were relative to the total meat ingredients weight. All treatments contained: 0.98% salt (sodium chloride), 0.83% dextrose, and 0.2% sodium tripolyphosphate). 2 The treatment abbreviation refers to nitrite source (sodium nitrite, SN; Celery powder, CEL; Organic Celery powder, OCEL; Swiss Chard Powder, SW; Organic Swiss Chard Powder; OSW) 3 Portable water was added in the form of an ice-water mixture. 2.3 Proximate composition Proximate composition was measured on samples from all treatments, including crude protein, fat, and moisture, using Association of Official Analytical Chemists (AOAC) procedures. Fat and moisture content in the samples were analyzed using a meat analyzer (CEM Smart 6 Meat Analyzer, CEM Co., Matthews, NC, U.S.A.) with an automatic calibration function (AOAC Official Method 2008.06). Protein content in the samples was analyzed using a nitrogen analyzer (Leco 828 series, LECO Corporation, St. Joseph, MI, USA) according to the Kjeldahl method (AOAC 981.10). Samples for proximate composition analysis were collected on day 0 for all measurements and kept frozen at -80°C. Frozen samples were homogenized in duplicate and measured on day 0 for all analyses. 2.4 Objective Color Measurement Color measurements were taken using a handheld spectrophotometer (Konica Minolta CM-600d, Konica Minolta Inc., Chiyoda, Tokyo, Japan) with a 2° standard observer. The color was measured using the Commission Internationale de l'Éclairage (CIE) L* (lightness), a* (redness), and b* (yellowness) system. The colorimeter was calibrated using a white calibration cap (CM-A177, Konica Minolta Inc., Chiyoda, Tokyo, Japan) through a vacuum pouch identical to the sample storage vacuum pouch. Five measurements of the white calibration caps were automatically completed using a pre-installed program on the spectrophotometer. Eight measurements were taken through sealed packages on three randomly selected packages from each TRT measurements and calibration were conducted in a temperature-controlled lab space maintained at the optimal measuring temperature of 23°C. Four random locations on each of the three 20 mm slices were evaluated on days 0, 15, 30, and 45. 2.5 Cure color ratio Cure color ratios were measured using a spectrometer set to visible reflectance measurement (Shimadzu UV2600 UV-Vis Spectrophotometer coupled with UPC-2600 multipurpose large sample component, Shimadzu Inc., Nakagyō-ku, Kyoto, Japan). The spectrometer was calibrated with a standard white plate compatible with the Shimadzu UPC-2600 through a vacuum pouch identical to the one used to store meat samples. Two slices of 20 mm thick sample stored in a vacuum pouch were measured in the visible wavelength range from 400 to 700 nm. The cured meat ratio was determined from the reflectance readings at 650 nm and 570 nm (King et al. 2023 ). 2.6 pH measurement pH measurements were conducted according to a method developed by Neo et al. (2006). Five grams of meat sample were blended with ultrapure water (resistivity of 18.2 MΩ.cm) at a 1:9 ratio using a polytron blender at 15,000 rpm. The mixture was then filtered through Whatman #3 filter paper and measured with a pH meter (Fisherbrand™ Accumet™ model 13-620-AE6; Fisher Scientific, Waltham, MA, USA). Calibration of the pH meter was performed using NIST-certified potassium biphthalate buffer (pH = 4.0) and potassium monobasic and sodium hydroxide buffer (pH = 7.0). 2.7 Residual nitrite and nitrate measurements Residual NO 2 − and NO 3 − were analyzed using HPLC equipment (ENO-20 NOx Analyzer, Eicom Inc, Kyoto, Japan) coupled with a temperature-controlled autosampler (AS-700, Amuza Inc., San Diego, C.A., U.S.A.) according to the method described by Sheng et al. (2025f?), with modifications based on sample size. The HPLC analysis for NO x − was designed based on the Griess nitrite test adopted by the AOAC. Absorption was measured at 540 nm by the UV-Vis detector preinstalled in the nitrite analyzer. Samples were powdered in liquid nitrogen and stored at − 80°C until analysis. Samples (5 grams) were weighed into 45 mL of pH 7.4 phosphate-buffered saline (PBS) followed by splitting into two equal volumes of slurries and centrifuged at 3,500 × g at 4°C for 5 min (J6-MI centrifuge equipped with a JA-25.50 rotor; Beckman Coulter, Indianapolis, IN, U.S.A.). After centrifugation, supernatants (500 µL) from each slurry and 500 µL of 100% methanol were mixed, transferred to a 1.5-mL snap cap centrifuge tube (Catalog number: 3453 Snap cap low retention microcentrifuge tubes, ThermoFisher Inc., Waltham, MA, U.S.A.), and vortexed for 10 seconds at 3,000 rpm with a digital vortex mixer (cat. no. 0215370, Fisher Scientific Inc, Hanover Park, IL U.S.A). The samples were then centrifuged for 16 min at 15,000 × g at 4°C (Eppendorf 5424 centrifuge, Brinkmann Instruments, Westburg, NY, U.S.A.). Supernatants (200-uL) were pipetted into 96-well plates for quantification with the HPLC equipment described in the previous section. Quantitative data (area under the curve) were analyzed with PowerChrom (version 16.0, New South Wales, Australia). The HPLC carrier pump speed was set at 40 mL/hour and reactor pump speed was set at 13.2 mL/hour. A calibration curve was created using 2, 4, 8, and 16 ppm of HPLC-grade sodium nitrite and sodium nitrate. A sodium nitrite standard (8 ppm) was tested at the start and end of each run. 2.8 Total and cured meat pigments measurement Total pigments, cured pigments, and cure efficacy measurements and calculations were conducted according to the meat pigments measurement guidelines, with modifications based on the AMSA Color Measurement guidelines (King et al. 2023 ). A 150-gram meat sample (fully cooked boneless ham) was minced using a commercial mixer (Robot Coupe BLIXER2 Blixer Vertical Commercial Blender with a 2.37 L stainless steel bowl, Robot Coupe Inc., Vincennes, France) for 10 seconds. For nitrosohemechrome determination, minced samples (10 grams) were weighed into a 100 mL beaker containing 43 mL of a solution comprising 40 mL acetone and 3 mL MilliQ water. After intermittent mixing for 5 minutes, the sample was filtered through Whatman #3 filter paper (Cytiva Inc., Marlborough, MA, USA) into a 50 mL polyethylene tube. A 1.5 mL aliquot of the filtrate was transferred into a 1-cm quartz cuvette for measuring absorbance at 540 nm using a UV-Vis spectrometer (Shimadzu UV-1208, Shimadzu Inc., Nakagyo-ku, Kyoto, Japan). Nitrosoheme content, expressed as NO-hematin, was calculated based on the formulation: sample A540 × 290. All measurements were conducted in duplicate. For total heme pigment determination, minced samples (10 grams) were weighed into a 100 mL beaker with acidified acetone (40 mL acetone, 2 mL MilliQ water, and 1 mL of 37% hydrochloric acid). The mixture was stored at room temperature for one hour with intermittent stirring, then filtered into a 1 cm quartz cuvette using Whatman #3 filter paper. Optical density was measured at 640 nm to determine total heme content, based on the formulation: sample A 640 × 680. All measurements were duplicated. All steps were conducted in a laboratory with LED lighting that does not emit UV-A/B. Curing efficacy was calculated as the percentage of nitrosohemochrome (expressed as ppm acid hematin) divided by total pigments (expressed as ppm acid hematin), multiplied by 100. 2.9 Consumer Sensory Panel Three untrained consumer sensory panels, conducted over three consecutive days at the University of Wisconsin-Madison Meat Science and Animal Biologics (MSABD) building, were approved by the University of Wisconsin-Madison Institutional Review Boards (IRB approval #2023 − 1195). These panels took place between days 14 and 21 to simulate the quality attributes of processed meats as presented to consumers in the commercial supply chain. Voluntary participants included university faculty, staff, students, and members of the general public aged 18 and over, recruited through mass email distribution (approximately 89,738 invitations per study) and an IRB-approved poster (IRB approval #2023 − 1195). Panelists were served samples in individual booths separated from the sample preparation area by a one-way glass window. The light intensity in each booth, maintained between 1,614 to 2,153 lux to simulate ideal meat-display lighting, was measured at the beginning and end of each full testing day using a portable industrial handheld color meter (Sekonic C-7000 Spectrometer, Sekonic US, North White Plains, NY, USA). Each panelist was served four treatments randomly selected from five treatment options, each representing a different source of nitrite. The random selection process included all possible combinations, utilizing 10 base designs to ensure that each control and treatment was sampled equally by the panelists. Samples were served one at a time, with potable water provided to cleanse the palate between tastings. A half slice of RTE boneless ham (3 mm thickness) samples were served cold (3.7–4.7°C) in a sample cup (100 ml volume) with a clear plastic lid. 2.10 Volatile Compounds Analysis Volatile compounds (VOCs) analysis was conducted according to a method described by Wettasinghe et al. (2001) with modifications accordingly based on sample weight and size. Multiple studies have been conducted on extraction methods and confirmed that steam distillation generally extracts more VOCs than solid-phase microextraction methods (Hong et al. 2018 ; Madruga et al. 2009 ; Watkins et al. 2012 ; Xie et al. 2016 ) Samples (200 grams) were ground by a commercial meat blender (Robot Coupe BLIXER2 Blixer Vertical Commercial Blender with 2.5 Quarts stainless steel bowl, Robot Coupe Inc., Vincennes, France). Ground samples (10 grams) were mixed with 10 grams of sodium chloride in a 200 mL volumetric tube specifically designed to fit in a fast steam distillation system (SCP DigiPREP Distillation System, SCP Science, Baie-d'urfe, Canada). The distillation was conducted at 60% strength for 300 seconds. 100 mL of distillate was mixed with 150 mL of methylene chloride in a 500 separatory flask mixed vigorously and allowed to remain at ambient temperature (23° C) for 2 hours. The methylene chloride layer was vaporized under vacuum using a rotary evaporator (Rotavapor, Buchi, Flawil, Switzerland) at 39.6°C. When the condensate reached a volume of approximately 5 mL, they were removed from the rotary evaporator meticulously and mixed with 5 grams of sodium formate to remove residual water content in the solution. The concentrate was subsequently transferred into a dark glass vial with a screw-on lid and stored at -80°C until further analysis. Gas chromatography with mass spectrometry tandem (GC-MS/MS) analysis was conducted using a GC-MS/MS system coupled with autosampler (Shimadzu GCMS-TQ 8040NX with AOC-20 plus autosampler, Shimadzu Inc., Nakagyo-ku, Kyoto, Japan) supplied with helium gas with a smart switch. Separation of VOCs was conducted on a general-purpose fused silica low polarity, crosslinked diphenyl dimethyl polysiloxane phase column (Shimadzu SH-I-5MS Capillary Column, 30m x 0.25mm x 0.25um, Shimadzu Inc., Nakagyoku, Kyoto, Japan) for semi-volatiles, phenols, amines, residual solvents, drugs of abuse, pesticides, PCB congeners with operation temperature range (-60 to 330/350°C). The oven temperature was programmed from 47°C to 240°C at a rate of 5°C/min with an initial and final hold time of 5 minutes and 10 minutes respectively. The total running time was 60 minutes. For the mass spectrometry detector, the electron ionization energy was set at 70eV. Mass range, electron multiplier voltage, and scan rate were set at m/z 33–330, 2000v, and 20,000 u/sec, respectively and the ionization source temperature has been maintained at 230°C (Li et al., 2023). Identification of VOCs was accomplished by matching the mass spectral data of sample compounds with an Electron Spray (EI) NIST database (NIST 23 Tandem Mass Spectral Libraries). The area under the curve (AUC) was integrated using the Savitzky-Golay method (smoothness setting at 25) with a width, setting of 0.040 minutes. Each integrated area was compared with the EI database based on spectrum similarity and then manually analyzed based on fragmentation patterns. The results of each sample TRT were integrated for comparison using Python (Python version 3.12.7, The Python Software Foundation) on Spyder (The Scientific Python Development Environment, version 6.0.1, Spyder-IDE.org) as an Integrated Development Environment (IDE). 2.11 Traditional Statistical analysis Physiochemical property data including cured meat pigments (CMP), total meat pigments (TMP), salt, and proximate analysis (moisture, fat, and protein) data were analyzed on day 0 with measurements done in duplicate. Objective color (Commission Interational de I’Eclairage [CIE] L* [Lightness], a* [redness], and b*[yellowness]), cured color ratio, residual nitrite (NO 2 − ), and pH were assessed on 15-day intervals from day 0 to day 45 with measurements conducted in triplicate. Since the TRTs were only manufactured once, for the physiochemical analysis data, descriptive statistical analyses were performed to determine means and a measure of variation associated with the repeated measures. One way Analysis of Variance (ANOVA) was performed to study data from sensory evaluations. Data from consumer sensory and physiochemical analysis on finished products were analyzed using R (R version 4.3.3; R Core Team 2024). Least square means were used when sample sizes were unequal, or values were missing. Normal tests (Kolmogorov-Smirnov test and adherence test) were conducted to verify normal distribution before conducting statistical tests. One-way Analysis of Variance (ANOVA) was used to detect significant differences between TRTs for physicochemical and sensory data during one sampling period. The Tukey and Dunnet Multiple Comparison Test was used if significant differences were detected. Pearson correlation and principal component analysis (PCA) were used to analyze sensory attributes and volatile compounds. The correlation coefficient (“r”) indicates the positive or negative of the relationships. PCA dimensionally reduced all variables into two principal components, PC1 and PC2 to describe data relationships based on eigenvalues (from parallel analysis). A Scree plot was utilized to validate PCA by determining the number of principal components to retain. 2.12 Artificial Intelligence applications Supervised classification models were employed to evaluate whether instrumental color variables could distinguish among all curing treatments. Analyses were conducted using Python (Python version 3.12.7, The Python Software Foundation) using the scikit-learn library (version. 1.8.0). Three classifiers were selected to accommodate moderate sample sizes and correlated predictors: (1) Multinomial Logistic Regression, chosen for its interpretable coefficients; (2) Random Forest, utilized for its ability to model nonlinear interactions; and (3) Histogram-based Gradient Boosting, which is highly effective for tabular data (Couronné et al. 2018 ; Darwish 2025 ). To prevent data leakage, continuous predictors were standardized (z-score normalization) within each training fold using a scikit-learn Pipeline. Repeated measurements were obtained from the same experimental unit over time; thus, observations were not independent. Consequently, a group-aware cross-validation strategy (GroupKFold) was employed to ensure that all observations from a given replicate remained distinctively within either the training or the test split. Model performance was assessed using balanced accuracy and the macro-average F1-score to account for class imbalance (Cardoso et al. 2024 ). The model's generalizability was validated using an independent 80/20 train-test split, where 20% of the data was reserved to confirm predictive accuracy and prevent overfitting (Asim Shahid et al. 2023 ). Consumer comments were analyzed to identify recurring sensory language themes and to examine their association with hedonic ratings. Comments were preprocessed by removing punctuation, normalizing whitespace, and excluding English stop-words. The text was subsequently represented using a bag-of-words model, which captures term frequency while disregarding word order (Dai et al. 2024 ). This process produced a document–term matrix, X, where rows correspond to comments and columns correspond to terms. Non-negative Matrix Factorization (NMF) was selected for topic modeling for its interpretable topic–word loadings that facilitate the labeling of topics within a sensory context (Si et al. 2022 ; Khan 2025 ). NMF factorizes X into non-negative matrices W (document–topic weights) and H (topic–word weights) such that: $$\:X\approx\:WH$$ 1 A six-topic solution was selected as it yielded distinct, interpretable themes corresponding to the primary sensory dimensions within the comment corpus (Nijs et al. 2021 ). To evaluate whether topic emphasis in comments was associated with ratings, while accounting for repeated measures from the same individuals, linear mixed-effects models were fitted with a random intercept for each consumer (Roberts et al. 2016 ) $$\:\text{R}\text{a}\text{t}\text{i}\text{n}{\text{g}}_{ij}\text{}={{\beta\:}}_{0}\text{}+\text{k}=1\sum\:_{\text{k}=1}^{\text{K}}{{\beta\:}}_{\text{k}}{\:\text{W}}_{ijk}\text{}+{\text{u}}_{\text{i}}\text{}+{\text{ϵ}}_{i\text{j}}\text{}$$ 2 where \(\:i\) indexes consumers, \(\:j\:\) indexes comments, \(\:{W}_{ijk}\:\) is the NMF topic weight for topic \(\:k\:\) in observation \(\:j\:\) from consumer \(\:i\) , \(\:{u}_{i}\sim\:N\left(0,{\sigma\:}_{u}^{2}\right)\) captures each consumer’s baseline rating tendency, and \(\:{ϵ}_{ij}\sim\:N\left(0,{\sigma\:}^{2}\right)\) is the residual error. Models were fit separately for Overall Liking and Non-meat aftertaste. Fixed-effect coefficients are reported with 95% confidence intervals. 3. Results and Discussion 3.1 Proximate composition and physiochemical analysis Proximate analysis of boneless ham cured with various nitrite sources on Day 0 is presented in Table 2 . The compositional profiles (moisture, protein, fat, and salt) of boneless ham cured with either conventional or organic vegetable-sourced nitrite were similar to those of the control made with sodium nitrite (SN) (p > 0.05). Boneless ham moisture protein ratio was between 3.15:1 and 3.30:1 which not only meets but is beyond the recommended value from the USDA Food database indicating a good value for consumers (USDA 2019 ). The total moisture percentage and protein in finished products for all TRTs were between 74.31 to 75.62, and 22.75 to 23.58 percent, respectively, with no observational difference among TRTs. Protein fat-free (PFF) in all treatments was above the regulation limit for common and usual chunked (or chopped) and formed pork products (USDA 2024b ). Cured meat pigments were 33.50 ± 0.04, 29.40 ± 0.50, 43.86 ± 0.57, 58.40 ± 0.07, and 54.09 ± 0.01 ppm acid hematin equivalent, while total meat pigments were 92.48 ± 0.07, 94.78 ± 0.99, 92.17a ± 0.15, 105.40 ± 0.00, and 102.00 ± 0.61 ppm acid hematin equivalent, respectively, for SN, CEL, OCEL, SW, and OSW (Table 2 ). Cure meat efficiency in treatments (TRTs) ranged from 31.03 ± 0.53% to 55.41 ± 0.72%, with no observational significant differences between vegetable source TRTs except for CEL with a conversion rate of 31.03 ± 0.53 ( p < 0.05). A survey conducted on boneless ham reported that the average cure efficiency of naturally cured ham ranged from 26.5% to 37.3%, while sodium nitrite-cured ham ranged from 31.8% to 46.0% (Sullivan et al. 2012 ). The ideal conversion rate for heme pigments to the nitrosohemechrome form during curing is considered to be approximately 80% (Pearson & Tauber, 1984). The overall cure efficiency in TRTs was in alignment and slightly higher than the value reported in a previous study (Sullivan et al. 2012 ) possibly due to the application of the vacuum tumbling process (Marriott et al. 1984 ). Across all TRTs, pH was determined to be in the range of 6.46 to 6.51, with no differences among them (p > 0.05) (Table 2 ). The results were found to be higher than those reported in a previous survey on commercially available boneless ham products (Sullivan et al. 2012 ), possibly due to the freshness of the finished products in this study compared with commercial samples. Table 2 Means of proximate analysis cured meat pigments. Total meat pigments, cured efficiency, and pH of treatments of ready-to-eat (RTE) boneless ham at day 0. Treatment SN CEL OCEL SW OSW Moisture (%) 75.62 a ± 0.09 74.67 a ± 0.03 74.69 a ± 0.03 74.34 a ± 0.01 74.31 a ± 0.00 Protein (%) 22.75 a ± 0.00 23.24 a ± 0.03 23.45 a ± 0.05 23.47 a ± 0.07 23.58 a ± 0.07 Fat (%) 2.02 a ± 0.04 1.78 ab ± 0.10 1.72 ab ± 0.03 2.01 a ± 0.03 1.94 a ± 0.02 Moisture/Protein Ratio 3.3 a ± 0.02 3.22 a ± 0.02 3.19 a ± 0.02 3.17 a ± 0.03 3.15 a ± 0.03 Cured Meat pigments* 33.50 c ± 0.04 29.40 c ± 0.50 43.86 b ± 0.57 58.40 a ± 0.07 54.09 a ± 0.01 Total Meat Pigments* 92.48 a ± 0.07 94.78 a ± 0.99 92.17 a ± 0.15 105.40 a ± 0.00 102.00 a ± 0.61 Cured efficiency (%) 36.22 b ± 0.84 31.03 b ± 0.53 45.59 a ± 7.17 55.41 a ± 0.72 53.11 a ± 4.24 pH 6.51 a ± 0.01 6.49 a ± 0.01 6.46 a ± 0.01 6.47 a ± 0.01 6.49 a ± 0.01 a−c means without common superscript are different (P < 0.05). Standard errors (SEM) are displayed following ± for each cell. SN: sodium nitrite; CEL: celery; OCEL: Organic Celery juice; SW: Swiss chard; OSW: organic Swiss chard. * Expressed as ppm acid hematin 3.2 Objective color and residual nitrite The results of nitrite depletion along with color change during the 45-day study period are summarized in Fig. 1 . In the current study, the amount of residual nitrite (NO 2 − ) initially ranged from 54.37 to 64.43 ppm on day 0, gradually decreasing to a range of 8.30 to 11.90 ppm over time by day 45 after manufacture. These results were in align with a recent national nitrite and nitrate depletion study (Sheng et al. 2025e ). On day 0, SN was found to contain the highest amount (p < 0.05) compared to the plant nitrite source TRTs. On day 30, NO 2 − in OSW decreased to a level significantly lower than CEL ( p = 0.034) and SN ( p = 0.0046). On day 45, the residual nitrite (NO 2 − ) in organic nitrite source TRTs decreased to a level lower than in conventional TRTs, with OCEL and OSW lower ( p < 0.001) than CEL and SW. NO 2 − is considered a reservoir that retains the color by continually replenishing the cured meat color lost from oxidation and photooxidation in processed meats (Mancini 2013 ). During the sampling period, the lower amount of NO 2 − in plant-sourced nitrite-cured boneless ham is possibly due to the presence of polyphenols and other active compounds that interact with nitrite during curing (Niu et al. 2021 ). NO 2 − potentially can be reduced by polyphenols in plant-source curing powders to nitric oxide (Rocha et al. 2009b ). Additionally, some phenolic compounds can undergo nitrosation at certain conditions, further reducing the amount of NO 2 − (Rocha et al. 2009a ). Residual NO 2 − in all TRTs during the course of the study were not completely depleted and considered sufficient for controlling bacteria growth and product color attributes (Davidson et al. 2005 ). The CIE L* value is an objective measure of meat lightness. It ranges from 0 (black) to 100 (white), with higher values indicating lighter meat and lower values indicating darker meat color (King et al. 2023 ). There were no significant differences detected on sampling days 0 and days 30. On day 15, OCEL and SW exhibited a lower ( p < 0.05) CIE L* value compared with the remainder of the TRTs, and on day 45, SN had the highest CIE L* (p < 0.05) compared with all vegetable sources nitrite TRTs. The CIE a* value indicates the redness of meat. It measures on a scale from negative values (greenish) to positive values (redness), with higher positive values indicating redder meat and lower values (or negative values) indicating greener meat (King et al. 2023 ). There were no differences detected among all TRTs on sampling day 0, and 15. On day 30, SW displayed reduced redness compared with the rest of the TRTs, and on day 45, organic source nitrite TRTs (OCEL and OSW) had a higher redness compared with the rest of the TRTs. The overall CIE a* value was similar but overall lower than the a* value from a previous study on boneless ham products. In the referenced study, naturally cured ham’s CIE a* value ranged from 14.2 to 17.9, and sodium nitrite cured ham’s CIE a* value ranged from 13.9 to 17.2. The potential cause for this difference could be the inclusion of bone-in-ham products in the referenced study, which possess higher redness values (Sullivan et al. 2012 ). The cured meat color ratio (measured by the reflectance of 650/570 nm) in CEL was higher than the other plant-sourced nitrite TRTs at day 0. The initial cured meat color of CEL was over 2.1, indicating an excellent cure color followed by OCEL ( x̅ = 2.10), CEL ( x̅ = 2.09), OSW ( x̅ = 2.08), and SW ( x̅ = 2.07). The trends remained stable during sampling. By day 45, CEL had the highest cured meat ratio, which was higher (p < 0.05) than OCEL (x̅ = 1.92), OSW (x̅ = 1.93), and SN (x̅ = 1.89). Moreover, SW (x̅ = 1.86) was lower than the rest of the TRTs. The hue angle signifies the color shift from redness (lower degree angle) to yellowness (higher degree angle) (King et al. 2023 ). The overall trends in the 45-day sample indicated that SN consistently decreased in Hue value over time. At day 0, the Hue angle of SN showed no difference (p > 0.05) compared to CEL, OCEL, and OSW, but was lower than SW (p < 0.05). By day 45, SN had the lowest Hue angle among all TRTs (p < 0.001). Among vegetable source nitrite treatments (TRTs), OCEL had the lowest Hue angle on day 0 (p < 0.05). By day 45, OSW exhibited a lower Hue angle (p < 0.05) compared to its conventional nitrite source TRTs. The Chroma C* value is directly calculated using the CIE a and b* values, indicating colorfulness relative to the brightness of the surroundings (Tomasević et al. 2019 ). It is better correlated with human visual color perception. On day 0, SN had the lowest Chroma (C*) compared to the vegetable source nitrite TRTs and maintained this trend throughout the sampling period. Among the vegetable source nitrite treatments, SW exhibited the highest mean saturation being significantly higher than OCEL throughout the entire sampling period. In a study conducted by Posthuma et al ( 2018 ), they reported that processed meats cured with sodium nitrite exhibited higher CIE a* values and lower CIE b* values compared to those treated with celery juice powder containing the same level of nitrite ( p < 0.05). Sodium nitrite leads to a more intense red color and reduced yellowness (Posthuma et al. 2018 ). The main reason for the decrease in Chroma C* is believed to be due to oxidation, which flattens the shape of the color spectrum and reduces the vividness of the color (Hernández Salueña et al., 2019). The variation in plant pigment profile could explain the differences in saturation observed during the sampling period. Ivanović et al ( 2019 ) conducted a study on the phytochemical content of Swiss chard and found that Swiss chard grown under different fertilization treatments was found to be a good source of total chlorophyll (47.13 mg/100 grams fresh weight), carotenoids (9.85 mg/100 grams fresh weight), minerals, and vitamin C (26.88 mg/100 grams fresh weight). Furthermore, they noted that different fertilization regimes significantly affected the content of phosphorous, protein, chlorophyll a, chlorophyll b, and vitamin C, while irrigation treatments did not have a significant effect (Ivanović et al. 2019 ). Additionally, carotenoids, another category of plant pigments, can contribute to yellow to orange hues and may influence the color attributes of cured meats. Several studies (Indu et al. 2024 ; Dhakal et al. 2021 ; Søltoft et al. 2011 ) have demonstrated that organic farming practices, which forego pesticides and utilize different fertilizers, can enhance the content of secondary metabolites such as carotenoids. The variations of plant pigments could explain the intricate relationship between growing practice and the resulting color properties of cured meat in this study. 3.3 Sensory evaluation A total of 123 panelists representing a diversity in age, gender, and consumption frequency were recruited to attend one of the sensory evaluation sessions held at the MSABD sensory lab. The age distribution of the participants was as follows: 24% were aged 18 to 24, 28% were aged 25 to 34, 18% were aged 35 to 44, 19% were aged 45 to 54, 8% were aged 55 to 64, and 4% were over 64. Regarding boneless ham consumption, 13% of the consumers indicated that they consumed boneless ham at least once per week, 29% consumed it several times per month, 24% consumed it once per month, and 33% consumed it less than once a month. Sensory evaluation revealed no differences (p > 0.05) for color, aroma, overall liking, and purchase intent among boneless ham cured with different curing ingredients, whether from organic or conventional vegetable curing powders (Fig. 2 c). Furthermore, PCA analysis indicated that color and aroma were strongly positively correlated with overall liking while did not correlate with non-meat aftertaste perception (Fig. 2 a). Pearson’s correlation analysis indicated that overall liking was strongly correlated with purchase intent (Fig. 2 b). These findings were consistent with previous studies which demonstrated that the addition of plant-source curing or antioxidant ingredients had no negative effects on the sensory attributes of processed and cured meat, using cherry and lime powder (Xi et al. 2012 ), celery powder (Jin et al. 2018 ), grape seeds extract (Parrini et al. 2019 ), and Ocimum. gratissimum leaf extract (Akwetey et al. 2021 ). Apart from the overall lack of difference in sensory evaluation results, a significant difference was identified in the non-meat aftertaste. OSW exhibited a notably higher non-meat aftertaste compared to SN (p = 0.03) and SW (p = 0.013). Furthermore, PCA analysis indicated that non-meat aftertaste was moderately negatively correlated with overall liking (r = -0.44) and weakly negatively correlated with purchase intent (r = -0.33). This finding aligns with previous studies that formulated processed meats with plant-sourced nitrites, which reported undesirable aftertastes in turkey bologna (Djeri and Williams 2014 ) and in reduced sodium boneless ham (Pietrasik et al. 2016 ), and regular boneless ham (Sindelar et al. 2007 ). Sindelar et al. ( 2007 ) reported that vegetable aroma is detectable by a trained sensory panelist at an inclusion above 0.35% in boneless ham products. Furthermore, a high concentration inclusion of vegetable powder significantly decreased ham aroma while increasing vegetable aroma (p < 0.05). Pietrasik et al. ( 2016 ) reported that celery-cured ham exhibited a significantly lower (p < 0.05) flavor and aftertaste compared to sodium nitrite-cured traditional ham in their study on the use of high-pressure processing to enhance the quality and shelf life of cooked ham. The age groups in the consumer sensory panels demonstrated different perceptions of non-meat aftertaste and overall liking of the finished products. Only the 18 to 24 and 45 to 54 age groups demonstrated a significantly higher (p < 0.05) perception of non-meat aftertaste compared to other age groups. The OSW TRT was perceived with higher (p < 0.05) non-meat aftertaste than OCEL TRT by these two age groups. In the 45 to 54 age group, a significantly lower (p < 0.05) purchase intent was identified for OSW TRT. The possible reason could be the significantly reduced plant aroma in OCEL compared to the rest of the alternative curing powders. A study conducted by Sheng et al. (Sheng et al. 2025b ) on volatile compounds in alternative meat curing powders indicated that OCEL contains considerably fewer amount of VOCs compared to other alternative TRTs due to the potential deodorization process during manufacture. Furthermore, the difference in perception of non-meat aftertaste could be attributed to age-related sensory perception differences. Age-related decline in olfaction is generally more pronounced than taste loss (Seiberling and Conley 2004 ), and the degeneration of olfactory neurons, reduced blood flow to the olfactory bulb, and changes in mucus production could possibly explain the cause of olfactory decline (Marin et al. 2018 ). The 55 and older age group did not indicate any sensory attribute differences, which could be explained by age-related sensory-specific satiety, where the elderly have a diminished pleasantness of the taste of eaten food (Rolls and McDermott 1991 ). Furthermore, consumption frequency impacts non-meat aftertaste and overall liking of boneless ham products. The least and most frequent consumption groups did not indicate any significant difference in non-meat aftertaste or overall liking (p > 0.05). However, the middle consumption frequency group clearly indicated a higher perception of non-meat aftertaste in OSW TRT compared to CEL TRT. Additionally, the once-per-month group indicated a significantly lower overall liking of OSW. This could likely be explained by sensory-specific satiety, where repeated consumption of a specific food leads to a decline in its perceived pleasantness and the ability to distinguish differences (Rolls et al. 1981 ; Hetherington 1996 ). 3.4 Volatile Compounds Analysis From the sensory evaluation results (Fig. 2 ), the organic-produced plant source curing powder (OSW) developed a noticeably stronger (p < 0.05) non-meat aftertaste, which was associated with reduced overall liking and corresponding purchase intent. To further understand the underlying reasons, we conducted a VOC analysis on all TRTs using steam distillation and gas chromatography technologies. A total of 781 VOCs were identified, of which 168 compounds exhibited a spectrum fragmentation pattern with at least 78% similarity to a compound in the current National Institute of Standards and Technology (NIST) electron ionization (EI) database. Correlation studies (Fig. 3 ) and a distinct aromatic compound profile were established from identifiable VOCs among all TRTs (Fig. 4 ). Lipolysis, proteolysis, and Maillard reactions are believed to be the main biochemical reactions involved in the generation of these compounds (García-González et al. 2013 ). Alkanes, alkenes, alcohols, and aromatic hydrocarbons were the most abundant volatile compounds. Additionally, aldehydes, ketones, cycloalkanes, amines, esters, sulfur-containing compounds, terpenoids, and furans were identified and relatively quantified using the area under the curve (AUC) approach (Fig. 3 a and Fig. 4 ). There was a relative much greater number of alkanes, and alkenes developed in conventional and organic celery-cured TRTs than Swiss chard-cured boneless ham (Fig. 3 a). Furthermore, alkanes, alkenes, alcohols, and aromatic hydrocarbons were strongly positively correlated (r > 0.7) with each other based on Pearson’s r analysis (Fig. 4 b). This potentially caused by their similarity of chemical structures as hydrocarbons, leading to comparable behaviors during thermal processing and interaction with meat components. Furthermore, the formation pathways of these compounds during meat processing are interconnected. The breakdown of fats and proteins are known to lead to the formation of alcohols, aldehydes, and ketones, which can further react to form alkanes, alkenes, and aromatic hydrocarbons (Dickey et al. 2021 ). Terpenoids compounds exhibited a strong positive correlation with non-meat aftertaste (r = 0.61). The correlation is potentially attributed to the citronellal compounds found in OSW that exhibited higher (p < 0.05) non-meat aftertaste scores than the remainder of the TRTs. Citronella was identified at a relatively higher concentration in OSW but not found in other TRTs (Fig. 4 ). Citronella is a monoterpenoid and aldehyde giving a distinctive lemon aroma and is regarded as one of the most important terpenes (Information 2024; Lenardão et al. 2007 ). Although a lemon-like scent is generally considered a pleasant fragrance may not contribute to a desirable aroma for meat consumers. A cognition-based framework study indicated that consumer’s perception of odor is associated with individuals’ episodic memories and past experiences (Morrin and Ratneshwar 2000 ). Familiarity is one of the basic dimensions through which individuals perceive smells (Rabin and Cain 1984 ). The terpenoid notes (typically herbal, woody), which are not typically associated with the flavor of processed meat, may explain the perception of non-meat aftertaste in some TRTs (Šojić et al. 2023 ). This non-meat aftertaste was negatively associated with overall liking and purchase intent. Esters were moderately negatively associated with non-meat aftertaste (r=-0.53). Esters tend to provide fruity and floral aroma and are associated with free amino acids in processed pork products (Tian et al. 2022 ). A wider variety of esters were identified in SN, OCEL, and SW than OSW (Fig. 4 ) could potentially explain the negative association between esters and non-meat aftertaste. Chen et al. ( 2021 ) reported 5 esters among the 49 flavor substances found on the surface of Mian-ning ham, a traditional Chinese dry-cured ham. They concluded that the presence of esters contributed to the characteristic aroma of the ham. Li et al. (2020) also asserted that esters as important flavor compounds. The study categorized esters in contributing to the unique flavor profiles of ham in a study on volatile profiles of Chinese dry-cured hams from different regions. Furans (C 4 H 4 O) were also a group of compounds found negatively associated with non-meat aftertaste (r = − 0.41). Furans tend to exhibit sweet, caramel-like, and sometimes smoky notes (Mu et al. 2020 ). They were most abundant in SN but not identified in plant-source nitrite TRTs (Fig. 4 ). Furan has been documented as a “possible human carcinogen (group 2b)” by International Agency for Research on Cancer (IARC) (Commission 2013 ). Possible pathways for furans formation include thermal degradation and rearrangement of carbohydrates and amino acids, and oxidation of amino acids and ascorbic acids (Seok et al. 2015 ). Certain phytochemicals may reduce the formation of furan by inhibiting oxidative reactions during thermal processing (Dhakal et al. 2017 ). Oral et al. reported that total furan formation was inhibited by plant-source antioxidants such as caffeic acid, punicalagin, and epicatechin in a glucose-glycine and asparagine-glucose Maillard reaction model system (Oral et al. 2014 ). However, it remains unclear whether the inhibitory effects on furan formation derive from the plant nitrite source curing powder, the cherry powder, which contains a high concentration of ascorbic acids, or the combined effects of both. The synergistic interaction between these components could potentially enhance the overall inhibition of furan formation more effectively than either component alone. Future research should aim to delineate the individual and combined effects of these compounds to fully understand their roles in mitigating furan formation in processed meats. 3.5 Integrating artificial intelligence into color assessment. Machine learning (ML) has emerged as an effective computational framework to augment traditional linear and multilinear regression methods, particularly in identifying complex, non-linear relationships within high-dimensional datasets (Raschka et al. 2022 ). While classical frequentist models often struggle with multicollinearity and non-monotonic associations, ML-based methodologies such as Support Vector Machines and Gradient Boosting were utilized in this study to provide enhanced granularity and predictive resolution (Fig. 5a). By leveraging these algorithmic approaches, we were able to capture subtle interactions between variables that traditional statistical tools might overlook, thereby offering a more robust interpretation of the gathered data. Objective color measurements as the most important quality parameter were utilized as predictors in this study. Grouped cross-validation indicated that multinomial logistic regression provided the best overall generalization among the tested models, achieving the highest macro-F1 score. Held-out evaluation yielded approximately 0.75 accuracy and 0.74 macro-F1, indicating that objective color features contain treatment-discriminating information significantly beyond chance. The confusion matrix (Fig. 5b) revealed that treatment separability was non-uniform. SN and SW were classified with high reliability achieving near-perfect accuracy in the held-out test set and suggesting that their objective color profiles occupy distinct regions within the feature space. In contrast, the remaining treatments exhibited mutual misclassification, indicating overlaps in objective color signatures consistent with their similar visual attributes. Feature importance summaries from the linear model (Fig. 5c) indicated that the cured meat ratio and Chroma C (and to a lesser extent CIE b* and hue angle) contributed most strongly to treatment separation, whereas CIE L* and CIE a* were less influential in this dataset. Collectively, Figs. 5 (b–c) support the conclusion that treatment identity is reflected in objective color measurements, driven primarily by pigment-related and saturation-related metrics rather than variations in lightness. . Figure 5. Classification performance and feature analysis of different treatments using colorimetric parameters. (a) Diagram of Machine Learning Approaches to Classifying Nonlinear Relations (b) Normalized confusion matrix showing classification accuracy for five treatment types: sodium nitrite (SN), conventional celery powder (CEL), organic celery powder (OCEL), Swiss chard (SW), and organic Swiss chard (OSW). Rows represent true classes and columns represent predicted classes. Values indicate the proportion of samples from each true class assigned to predicted classes. Diagonal values represent correct classification rates. Model: Logistic Regression classifier trained on L*a*b* and spectral features. Overall accuracy: 74.5%; macro F1-score: 0.738. Test set: n = 55 samples (11 per class). (c) Permutation importance analysis quantifying the contribution of each colorimetric feature to classification performance. Horizontal bars indicate the decrease in model accuracy when feature values are randomized (Δ accuracy), with error bars representing standard deviation across 30 permutations. Percentage values on bars indicate relative contribution of each feature to overall model performance. 3.6 Natural Language Processing on Consumer panel’s comments Traditional sensory evaluation has long relied on structured quantitative scales such as 9-point hedonic tests (Wichchukit and O'Mahony 2015 ) or just about right tests (Gacula Jr et al. 2007 ) to assess consumer liking and attribute intensity. While these metrics provide a statistical baseline, they often fail to capture the nuanced, qualitative "why" behind consumer preferences (Nunes et al. 2023 ; Phillips and Smit 2021 ). Open-ended comments offer a rich repository of this latent information. However, the unstructured nature of human language fraught with idiosyncratic terminology, varied syntax, and varying levels of detail has historically made large-scale manual analysis labor-intensive and susceptible to subjective bias (Alam et al. 2025 ; Mani et al. 2025 ). The integration of AI and NLP potentially can represent a paradigm shift in food science, transforming these qualitative narratives into actionable, high-dimensional data. This study utilizes these cutting-edge methodologies to provide a deeper, data-driven understanding of how alternative curing processes influence the holistic consumer experience. Based on the highest-loading terms within each topic, we identified six primary themes: Topic 0 (Ham Identity/Appearance) (e.g., “ham,” “like ham,” “looks”); Topic 1 (Positive Appraisal) (e.g., “good,” “really good,” “good flavor/texture”); Topic 2 (General Sensory Descriptors) (e.g., “taste,” “aroma,” “appearance,” “smell”); Topic 3 (Flavor/Color/Salt Notes) (e.g., “flavor,” “color,” “pink/red,” “saltiness”); Topic 4 (Texture/Mouthfeel) (e.g., “texture,” “chewy,” “rubbery”); and Topic 5 (Aftertaste/Off-notes) (e.g., “aftertaste,” “non-meat,” “strange/weird,” “strong aftertaste”). Across formulations, consumers utilized a consistent set of sensory dimensions, indicating that the six-topic NMF solution effectively captured a shared “sensory language space” across the dataset. However, the relative emphasis of these themes varied by treatment. Figure 6 a summarizes mean topic weights by treatment highlighting treatment-specific language signatures identifying which attributes consumers prioritized when describing each formulation. To relate comment themes to ratings while accounting for repeated measurements, we utilized mixed-effects models with a random intercept for each consumer. This specification separates inter-consumer variability (stable differences in scale usage, such as "harsh" vs. "generous" raters) from intra-consumer variation across samples. Consequently, the fixed-effect topic coefficients in Figs. 6 (a-b) represent within consumer associations: holding a consumer’s baseline rating constant, observations where their comments contain higher weights of a given topic are associated with systematically higher or lower ratings. Within consumers, a higher weight in the Positive Appraisal topic was significantly associated with higher Overall Liking, confirming that this topic captures explicitly favorable language (Fig. 6 a). Conversely, a higher Aftertaste/Off-notes weight was associated with lower Overall Liking, indicating that when a consumer’s comment was dominated by off-note language, their liking scores decreased. General Sensory Descriptors also trended negative, suggesting that more generic descriptive language—after controlling for consumer baseline—reflected more mixed or less enthusiastic evaluations. For Non-meat Aftertaste ratings, the Aftertaste/Off-notes topic showed a strong positive correlation with the outcome (Fig. 6 b). Interpreted within-consumer, this indicates that when a participant’s comments placed greater emphasis on off-notes, they systematically assigned higher non-meat aftertaste intensity ratings. In contrast, the remaining topics (Topics 0–4) exhibited negligible effects with greater uncertainty relative to this dominant signal. Fig 6c summarizes these fixed effect estimates with 95% confidence intervals. A distinct and variable pattern emerged across the six NMF-derived topics: specifically, OCEL and SN were characterized by fewer descriptive terms, while OSW was more strongly associated with General Sensory Descriptors. These findings using NLP confirm that consumer comments—a rich data source that has long been underutilized—should be integrated into sensory research. This approach can effectively complement traditional scaled tests or serve as an independent tool for assessing the nuances of consumer perception. 4. Conclusions The process of meat curing plays a vital role in the development of the color and sensory attributes of cured meats such as boneless ham, with results indicating that instrumental and consumer sensory attributes remain consistent whether using conventional or organic plant-based nitrite sources. While organic and conventional growing practices influence aroma formation and the development of distinct VOC profiles that subsequently impact consumer perceptions of non-meat aftertaste, these differences do not significantly alter purchase decisions. However, consumer age and consumption frequency do emerge as variables that modulate the perception of aftertaste and purchase intent. These findings suggest that the presence of bioactive plant compounds in alternative curing powders warrants further investigation into the effects of long-term storage on meat quality, antioxidant benefits, and broader health outcomes. Furthermore, the application of artificial intelligence technologies including machine learning and natural language processing in this research provides superior analytical value compared to traditional statistical tools, advocating for their broader integration into food science methodologies. Declarations Contributions Siyuan Sheng: Conceptualization, Writing – Original draft, Visualization, Methodology, Investigation, Supervision, Formal analysis, Data curation, software, Validation, Writing – review & editing. Zihan Sun: Methodology, Investigation, Formal analysis, Resources. Steven Ricke: Resources, Writing – review and editing. James Claus: Conceptualization, Resources, Supervision, Project administration, Writing – review and editing, Validation. Funding Declaration S .S. received funding from Organic Research and Extension Initiative program of the U.S. Department of Agriculture, National Institute of Food and Agriculture (award # 2019-51300-30243). Acknowledgments The Sensory evaluation study was conducted under IRB approval 2023-1195 at University of Wisconsin-Madison Meat Science and Animal Discovery building. Competing interests The authors declare no competing interests. Data Availability The datasets generated and/or analyzed in this study are available from the corresponding author on reasonable request. References Akwetey, W., Adzitey, F., & Teye, G. (2021). Cured Characteristics, Physicochemical Properties and Sensory Profile of Frankfurters Produced with Ocimum Gratissimum Extract Leaf Extracts. Food Sci Nutr Res , 4 (1), 1–5. Alam, M. S., Mrida, M. S. H., & Rahman, M. A. (2025). Sentiment analysis in social media: How data science impacts public opinion knowledge integrates natural language processing (NLP) with artificial intelligence (AI). American Journal of Scholarly Research and Innovation , 4 (01), 63–100. Arsenault, D. M. (2019). RE: Celery Powder (Sunset 2021). Asim Shahid, M., Alam, M. M., & Mohd Su’ud M. (2023). Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm. Plos one . 18(4), e0284209. Brata, A. M., Chereji, A. I., Brata, V. D., Morna, A. A., Tirpe, O. P., Popa, A., Arion, F. H., Banszki, L. I., Chereji, I., Popa, D., & Muresan, I. C. (2022). Consumers' Perception towards Organic Products before and after the COVID-19 Pandemic: A Case Study in Bihor County, Romania. International Journal of Environmental Research and Public Health. 19(19). Cardoso, W., Barros, T., Gonçalves, G., & Premebida, C. (2024). & Nunes UJ Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies. In: 2024 7th Iberian Robotics Conference (ROBOT), IEEE, pp 1–6. Chausali, N., & Saxena, J. (2021). Conventional versus organic farming: Nutrient status. Advances in Organic Farming (pp. 241–254). Elsevier. Chen, L., Wang, Z., Ji, L., Zhang, J., Zhao, Z., Zhang, R., Bai, T., Hou, B., & Wang, W. (2021). Flavor Composition and Microbial Community Structure of Mianning Ham. Frontiers in Microbiology. 11. Commission, E. (2013). Furan-01: Analysis of furan in baby food. In: Centre JR (Ed.). p^pp. Couronné, R., Probst, P., & Boulesteix, A-L. (2018). Random forest versus logistic regression: a large-scale benchmark experiment. Bmc Bioinformatics , 19 (1), 270. Dai, S., Li, K., Luo, Z., Zhao, P., Hong, B., Zhu, A., & Liu, J. (2024). AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006–4023. 5(1), 13–21. Darwish, J. A. (2025). Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning. Alexandria Engineering Journal , 127 , 586–594. Davidson, P. M., Sofos, J. N., & Branen, A. L. (2005). Antimicrobials in food . CRC. Dhakal, K., Ravi, R., & Nandwani, D. (2021). Comparative study of sensory attributes of leafy green vegetables grown under organic and conventional management. International Journal on Food Agriculture and Natural Resources , 2 (3), 29–45. Dhakal, S., Balasubramaniam, V. M., Cocuron, J-C., Alonso, A. P., Agcam, E., & Kamat, S. (2017). Pressure-Thermal Kinetics of Furan Formation in Selected Fruit and Vegetable Juices. Food and Bioprocess Technology , 10 (11), 1959–1969. Dickey, R. M., Forti, A. M., & Kunjapur, A. M. (2021). Advances in engineering microbial biosynthesis of aromatic compounds and related compounds. Bioresources and Bioprocessing , 8 (1), 91. Djeri, N., & Williams, S. (2014). Celery Juice Powder Used as Nitrite Substitute in Sliced Vacuum-Packaged Turkey Bologna Stored at 4C for 10 Weeks Under Retail Display Light. Journal of Food Quality . 37. GaculaJr, M., Rutenbeck, S., Pollack, L., Resurreccion, A. V., & Moskowitz, H. R. (2007). The Just-About‐Right intensity scale: functional analyses and relation to hedonics. Journal of Sensory Studies , 22 (2), 194–211. García-González, D. L., Aparicio, R., & Aparicio-Ruiz, R. (2013). Volatile and amino acid profiling of dry cured hams from different swine breeds and processing methods. Molecules , 18 (4), 3927–3947. Hetherington, M. (1996). Sensory-specific satiety and its importance in meal termination. Neuroscience & Biobehavioral Reviews , 20 (1), 113–117. Hong, Y. S., Son, J. H., Jeong, J. Y., Song, O. Y., Lee, J. H., & Kim, K. S. (2018). Comparison of volatile flavor compounds of Artemisia annua L. extracted by simultaneous steam distillation extraction and solid-phase micro extraction. Korean Journal of Food Preservation , 25 (3), 321–329. Indu, S., Neeraj, K., & Archana, R. (2024). Carotenoids: Sources, Bioavailability and Their Role in Human Nutrition. In R. Akkinapally Venketeshwer, & R. Leticia (Eds.), Dietary Carotenoids. p^pp Ch. 5 . IntechOpen. Information NCfB (2024). PubChem Compound Summary for CID 7794, Citronellal. In. p^pp. Ivanović, L., Milašević, I., Topalović, A., Ðurović, D., Mugoša, B., Knežević, M., & Vrvić, M. (2019). Nutritional and phytochemical content of Swiss chard from Montenegro, under different fertilization and irrigation treatments. British Food Journal , 121 (2), 411–425. Jin, S-K., Choi, J. S., Yang, H-S., Park, T-S., & Yim, D-G. (2018). Natural curing agents as nitrite alternatives and their effects on the physicochemical, microbiological properties and sensory evaluation of sausages during storage. Meat Science , 146 , 34–40. Khan, A. A. (2025). Meta-Unsupervised Learning: Application to Non-Negative Matrix Factorization. King, D. A., Hunt, M. C., Barbut, S., Claus, J. R., Cornforth, D. P., Joseph, P., Kim, Y. H. B., Lindahl, G., Mancini, R. A., & Nair, M. N. (2023). American Meat Science Association guidelines for meat color measurement. Meat and Muscle Biology 6(4). Lenardão, E. J., Botteselle, G. V., de Azambuja, F., Perin, G., & Jacob, R. G. (2007). Citronellal as key compound in organic synthesis. Tetrahedron , 63 (29), 6671–6712. Madruga, M. S., Elmore, J. S., Dodson, A. T., & Mottram, D. S. (2009). Volatile flavour profile of goat meat extracted by three widely used techniques. Food Chemistry , 115 (3), 1081–1087. Mancini, R. (2013). Meat Color. In: The Science of Meat Quality. p^pp 177–198. Mani, R. T., Palimar, V., Pai, M. S., Shwetha, T., & Krishnan, M. N. (2025). An evolution of forensic linguistics: From manual analysis to machine learning–A narrative review (p. 100417). Forensic Science International. Marin, C., Vilas, D., Langdon, C., Alobid, I., López-Chacón, M., Haehner, A., Hummel, T., & Mullol, J. (2018). Olfactory dysfunction in neurodegenerative diseases. Current allergy and asthma reports , 18 , 1–19. Marriott, N. G., Graham, P. P., Boling, J. W., & Collins, W. F. (1984). Vacuum Tumbling of Dry-Cured Hams. Journal of Animal Science , 58 (6), 1376–1381. Morrin, M., & Ratneshwar, S. (2000). The impact of ambient scent on evaluation, attention, memory for familiar and unfamiliar brands. Journal of Business Research , 49 (2), 157–165. Mu, Y., Su, W., Mu, Y., & Jiang, L. (2020). Combined Application of High-Throughput Sequencing and Metabolomics Reveals Metabolically Active Microorganisms During Panxian Ham Processing. Frontiers in Microbiology . 10. Nijs, M., Smets, T., Waelkens, E., & De Moor, B. (2021). A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data. Rapid Communications In Mass Spectrometry 35(21), e9181. Niu, P., Wang, F., Yuan, K., Li, X., Yang, X., & Guo, Y. (2021). Alkaline-extracted thinned young apple polyphenols as an effective scavenger against nitrite in pickles: A comparative study with ethanol-extracted polyphenols. Food Control , 130 , 108387. NOSB (2023). National Organic Standards Board Meeting - Atlanta,GA. In. p^pp 104–105. Nunes, C. A., Ribeiro, M. N., de Carvalho, T. C., Ferreira, D. D., de Oliveira, L. L., & Pinheiro, A. C. (2023). Artificial intelligence in sensory and consumer studies of food products. Current Opinion in Food Science , 50 , 101002. Oral, R. A., Dogan, M., & Sarioglu, K. (2014). Effects of certain polyphenols and extracts on furans and acrylamide formation in model system, and total furans during storage. Food Chemistry , 142 , 423–429. Parrini, S., Sirtori, F., Acciaioli, A., Becciolini, V., Crovetti, A., Franci, O., Romani, A., Scardigli, A., & Bozzi, R. (2019). Effect of Replacement of Synthetic vs. Natural Curing Agents on Quality Characteristics of Cinta Senese Frankfurter-Type Sausage. Animals (Basel) . 10(1). Pham, A., Schilling, M., Mikel, W., Williams, J., Martin, J., & Coggins, P. (2008). Relationships between sensory descriptors, consumer acceptability and volatile flavor compounds of American dry-cured ham. Meat Science , 80 (3), 728–737. Phillips, D., & Smit, M. (2021). Toward best practices for unstructured descriptions of research data. Proceedings of the Association for Information Science and Technology. 58(1), 303–314. Pietrasik, Z., Gaudette, N. J., & Johnston, S. P. (2016). The use of high pressure processing to enhance the quality and shelf life of reduced sodium naturally cured restructured cooked hams. Meat Science , 116 , 102–109. Posthuma, J. A., Rasmussen, F. D., & Sullivan, G. A. (2018). Effects of nitrite source, reducing compounds, and holding time on cured color development in a cured meat model system. LWT , 95 , 47–50. Rabin, M. D., & Cain, W. S. (1984). Odor recognition: familiarity, identifiability, and encoding consistency. Journal of experimental psychology Learning memory and cognition , 10 (2), 316–325. Raschka, S., Liu, Y. H., & Mirjalili, V. (2022). Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python . Packt Publishing Ltd. Rasmussen, F. D., & Sullivan, G. A. (2019). Comparison of Traditional and Alternative Curing Ingredients on Curing Reactions in a Model Meat System. Rizzo, G., Testa, R., Schifani, G., & Migliore, G. (2023). The Value of Organic plus. Analysing Consumers’ Preference for Additional Ethical Attributes of Organic food Products . Social Indicators Research. Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A Model of Text for Experimentation in the Social Sciences. Journal of the American Statistical Association , 111 (515), 988–1003. Rocha, B. S., Gago, B., Barbosa, R. M., & Laranjinha, J. (2009a). Dietary polyphenols generate nitric oxide from nitrite in the stomach and induce smooth muscle relaxation. Toxicology , 265 (1–2), 41–48. Rocha, B. S., Gago, B., Barbosa, R. M., & Laranjinha, J. (2009b). Dietary polyphenols generate nitric oxide from nitrite in the stomach and induce smooth muscle relaxation. Toxicology , 265 (1–2), 41–48. Rogers, K. M. (2012). Pork: A global history . Reaktion Books. Rolls, B. J., & McDermott, T. M. (1991). Effects of age on sensory-specific satiety. The American Journal of Clinical Nutrition , 54 (6), 988–996. Rolls, B. J., Rolls, E. T., Rowe, E. A., & Sweeney, K. (1981). Sensory specific satiety in man. Physiology & behavior , 27 (1), 137–142. Seiberling, K. A., & Conley, D. B. (2004). Aging and olfactory and taste function. Otolaryngologic Clinics of North America , 37 (6), 1209–1228. Seok, Y-J., Her, J-Y., Kim, Y-G., Kim, M. Y., Jeong, S. Y., Kim, M. K., Lee, J., Kim, C., Yoon, H-J., & Lee, K-G. (2015). Furan in Thermally Processed Foods - A Review. Toxicological Research , 31 (3), 241–253. Sheng, S., Ricke, S. C., Silva, E. M., & Claus, J. R. (2025a). Impact of Organic and Conventional Vegetable-Based Curing Ingredients on Frankfurter Quality and Sensory Attributes. Food Science & Nutrition 13(4), e70148. Sheng, S., Silva, E. M., Ricke, S. C., & Claus, J. R. (2025b). Characterization of Volatilized Compounds in Conventional and Organic Vegetable-Source Alternative Meat-Curing Ingredients. Molecules , 30 (4), 835. Sheng, S., Silva, E. M., Ricke, S. C., & Claus, J. R. (2025c). Consumer sensory preferences for processed meats with synthetic, conventional, organic, and deodorized curing ingredients. npj Science of Food , 9 (1), 185. Sheng, S., Silva, E. M., Ricke, S. C., & Claus, J. R. (2025d). Organic and conventional alternative curing ingredients effects on quality and sensory attributes of deli-style Turkey. Poultry Science , 104 (8), 105370. Sheng, S., Silva, E. M., Tarté, R., & Claus, J. R. (2025e). Residual nitrite and nitrate in processed meats and meat analogues in the United States. Scientific Reports , 15 (1), 3269. Si, S., Wang, J., Zhang, R., & Su, Q. (2022). & Xiao J Federated non-negative matrix factorization for short texts topic modeling with mutual information. In: 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–7. Siekmann, L., Plötz, M., & Krischek, C. (2021). Alternative curing methods. Current Clinical Microbiology Reports , 8 , 40–48. Sindelar, J. J., Cordray, J. C., Sebranek, J. G., Love, J. A., & Ahn, D. U. (2007). Effects of Varying Levels of Vegetable Juice Powder and Incubation Time on Color, Residual Nitrate and Nitrite, Pigment, pH, and Trained Sensory Attributes of Ready-to-Eat Uncured Ham. Journal of Food Science , 72 (6), S388–S395. Šojić, B., Milošević, S., Savanović, D., Zeković, Z., Tomović, V., & Pavlić, B. (2023). Isolation, Bioactive Potential, and Application of Essential Oils and Terpenoid-Rich Extracts as Effective Antioxidant and Antimicrobial Agents in Meat and Meat Products. Molecules , 28 (5), 2293. Søltoft, M., Bysted, A., Madsen, K. H., Mark, A. B., Bügel, S. G., Nielsen, J., & Knuthsen, P. (2011). Effects of organic and conventional growth systems on the content of carotenoids in carrot roots, and on intake and plasma status of carotenoids in humans. Journal of the Science of Food and Agriculture , 91 (4), 767–775. Sullivan, G. A., Jackson-Davis, A. L., Schrader, K. D., Xi, Y., Kulchaiyawat, C., Sebranek, J. G., & Dickson, J. S. (2012). Survey of naturally and conventionally cured commercial frankfurters, ham, and bacon for physio-chemical characteristics that affect bacterial growth. Meat Science , 92 (4), 808–815. Tatiyaborworntham, N., Oz, F., Richards, M. P., & Wu, H. (2022). Paradoxical effects of lipolysis on the lipid oxidation in meat and meat products. Food Chemistry: X , 14 , 100317. Tian, Z., Zhu, Q., Chen, Y., Zhou, Y., Hu, K., Li, H., Lu, K., Zhou, J., Liu, Y., & Chen, X. (2022). Studies on Flavor Compounds and Free Amino Acid Dynamic Characteristics of Fermented Pork Loin Ham with a Complex Starter. Foods , 11 (10), 1501. Tomasević, I., Tomović, V., Barba, F. J., Vasilev, D., Jokanović, M., Sojić, B., Manuel Lorenzo, J., & Đekić, I. (2019). How the color of game meat should be measured. Fleischwirtschaft , 99 (1), 85–89. USDA. (1996). Institutional Meat Purchase Specifications . Fresh Beef Series 100–PD F version. USDA. (2019). In. p^pp, Agricultural Research Service . Beltsville Human Nutrition Research Center. 7 CFR 205.606. In. USDA, & p^pp (2024a). U.S.A. USDA (2024b). 9 CFR 319.104. In. p^pp. Watkins, P., Rose, G., Warner, R., Dunshea, F., & Pethick, D. (2012). A comparison of solid-phase microextraction (SPME) with simultaneous distillation–extraction (SDE) for the analysis of volatile compounds in heated beef and sheep fats. Meat Science , 91 (2), 99–107. Waxman, O. B. (2017). Spam Is Turning 80. Here’s How the Canned Meat Took Over the World . In: Time. p^pp. Wichchukit, S., & O'Mahony, M. (2015). The 9-point hedonic scale and hedonic ranking in food science: some reappraisals and alternatives. Journal of the Science of Food and Agriculture , 95 (11), 2167–2178. Xi, Y., Sullivan, G., Jackson, A., Zhou, G., & Sebranek, J. (2012). Effects of natural antimicrobials on inhibition of Listeria monocytogenes and on chemical, physical and sensory attributes of naturally-cured frankfurters. Meat Science , 90 (1), 130–138. Xie, Y., He, Z., Zhang, E., & Li, H. (2016). Characterization of key volatile odorants in rabbit meat using gas chromatography mass spectrometry with simultaneous distillation extraction. World Rabbit Science , 24 (4), 313–320. Yu, X., Guo, L., Jiang, G., Song, Y., & Muminov, M. A. (2018). Advances of organic products over conventional productions with respect to nutritional quality and food security. Acta Ecologica Sinica , 38 (1), 53–60. Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterialCode.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8905083","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593402947,"identity":"37d285fc-f445-41f6-a619-f80febf91684","order_by":0,"name":"Siyuan Sheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACZihtACI+MEiAORJEa2GcQZQWGABpYeaBcvBqkW/nMZP4uYPB3py99/Br2zYLe/4G5oO3efBoMTjMYybZe4YhcWfPuTTr3DaJxBkH2JKt8WphBtrC28aQYHAjx8w4d5tEggEDj5k0Pi3yzUBb/rYx2Bvcf2NmbLlNwt6Agf8bXi0MQIdJA21h3HCDx/gx4zYJxg0MPGx4tRgcZiu2lgV6YcOZHDPG3n9AvxxmM7acg89h/Yc33nzbZmNvcPyM8YcfZ+rs+dubH954g89hDBygGAFHBBskOpjxqQYD9gcwFvMHgopHwSgYBaNgRAIASqlByRIOsuIAAAAASUVORK5CYII=","orcid":"","institution":"Tennessee State University","correspondingAuthor":true,"prefix":"","firstName":"Siyuan","middleName":"","lastName":"Sheng","suffix":""},{"id":593402948,"identity":"f800db0f-d483-454a-86dd-81ef060f5a68","order_by":1,"name":"Zihan Sun","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Sun","suffix":""},{"id":593402949,"identity":"af4a72d0-453c-4fbb-ac3b-e3329a1c4d29","order_by":2,"name":"Steven Ricke","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"","lastName":"Ricke","suffix":""},{"id":593402950,"identity":"7b316e31-4680-459b-b5c2-512cbf321a26","order_by":3,"name":"James Claus","email":"","orcid":"","institution":"University of Wisconsin–Madison","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Claus","suffix":""}],"badges":[],"createdAt":"2026-02-18 02:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8905083/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8905083/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102966623,"identity":"0dff10a0-29b5-400f-9833-a81989320277","added_by":"auto","created_at":"2026-02-19 04:37:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":452577,"visible":true,"origin":"","legend":"\u003cp\u003eMeans for the main effects of nitrite source (sodium nitrite, SN, celery powder, CEL, organic celery juice, OCEL, Swiss chard powder, SW, and organic Swiss chard powder, OSW) on residual nitrite (measured in ppm), L, a, b*, cured meat color ratio (measured at a visual wavelength of 650/570 nm), Hue angle, and Chroma C. Commission \u003cem\u003eInternationale de l'Eclairage\u003c/em\u003e (CIE) \u003cem\u003eL, a, b\u003c/em\u003e* color space, where CIE \u003cem\u003eL\u003c/em\u003e* represents lightness on a scale from 0 (black) to 100 (white), CIE \u003cem\u003ea\u003c/em\u003e* represents the red-green axis (positive values indicate redness, negative values indicate greenness), CIE \u003cem\u003eb\u003c/em\u003e* represents the yellow-blue axis (positive values indicate yellowness, negative values indicate blueness). The CIE Chroma C* represents the distance from the lightness axis and starts at 0 in the center, indicating color saturation. CIE hue angle, starting at the + a axis, is expressed in degrees, where 0° corresponds to +a* (red) and 90° corresponds to +b* (yellow).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/3dd5c9cabef7dd1773c65179.png"},{"id":102966707,"identity":"c603fad1-e0d9-4883-88f1-91b81552794a","added_by":"auto","created_at":"2026-02-19 04:37:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":405872,"visible":true,"origin":"","legend":"\u003cp\u003eSensory attributes analysis of all treatments (TRTs) of ready to eat (RTE) boneless ham (a) Biplot of boneless ham sensory attributes using PCA analysis. (b) Correlation analysis of all sensory attributes of boneless ham (c) Boxplot of sensory attributes of all TRTs. In Fig (c), different letters denote a significant difference (p \u0026lt; 0.05), and + indicates the mean value.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/4d37e2b1b0ddfdac33539c11.png"},{"id":102966705,"identity":"1b704c85-0917-420c-b646-b75bf9fabf3c","added_by":"auto","created_at":"2026-02-19 04:37:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":561384,"visible":true,"origin":"","legend":"\u003cp\u003eVolatile compounds in RTE boneless ham formulated with different sources of nitrite. (a) Relative concentrations of key volatile compounds in control and all treatments of RTE boneless ham. (b) Biplot of RTE boneless ham VOCs and sensory attributes (non-meat aftertaste and purchase intent). (c) Correlation analysis of RTE boneless ham and non-meat aftertaste.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/3e33c9340129db6b941e8719.png"},{"id":102966708,"identity":"ba0f8ef6-8014-4916-a082-0c7313295c3b","added_by":"auto","created_at":"2026-02-19 04:37:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":961350,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of volatile compounds (VOCs) in ready to eat (RTE) boneless ham. SN: sodium nitrite; CEL: celery; OCEL: Organic Celery juice; SW: Swiss chard; OSW: organic Swiss chard.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/c2a5041a9df1de8342bd7df5.png"},{"id":102966603,"identity":"eff87281-4411-4e72-8316-9e675ff0306c","added_by":"auto","created_at":"2026-02-19 04:37:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":407604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClassification performance and feature analysis of different treatments using colorimetric parameters.\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003e(a) \u003c/strong\u003eDiagram of Machine Learning Approaches to Classifying Nonlinear Relations \u003cstrong\u003e(b) \u003c/strong\u003eNormalized confusion matrix showing classification accuracy for five treatment types: sodium nitrite (SN), conventional celery powder (CEL), organic celery powder (OCEL), Swiss chard (SW), and organic Swiss chard (OSW). Rows represent true classes and columns represent predicted classes. Values indicate the proportion of samples from each true class assigned to predicted classes. Diagonal values represent correct classification rates. Model: Logistic Regression classifier trained on L*a*b* and spectral features. Overall accuracy: 74.5%; macro F1-score: 0.738. Test set: n = 55 samples (11 per class). \u003cstrong\u003e(c) \u003c/strong\u003ePermutation importance analysis quantifying the contribution of each colorimetric feature to classification performance. Horizontal bars indicate the decrease in model accuracy when feature values are randomized (Δ accuracy), with error bars representing standard deviation across 30 permutations. Percentage values on bars indicate relative contribution of each feature to overall model performance.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/a73d7c10e2d645d01860a4b7.png"},{"id":102966670,"identity":"14e925ce-66c6-4e33-b315-6d20d3287036","added_by":"auto","created_at":"2026-02-19 04:37:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":317895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTopic modeling analysis of consumer sensory panels comments and their association with sensory ratings.\u003c/strong\u003e \u003cstrong\u003e(a) \u003c/strong\u003eNLP architecture illustration and topic impact on overall liking \u003cstrong\u003e(b)\u003c/strong\u003e Horizontal bar plots showing coefficients (with 95% confidence intervals) from linear mixed-effects models assessing the association between topic emphasis in consumer comments and non-meat aftertaste ratings. Topics were derived from Non-negative Matrix Factorization (NMF) analysis of consumer comments (6-topic solution). Positive appraisal topics were associated with higher overall liking (β = +3.04, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while Comments focusing on aftertaste/off-notes were strongly associated with lower overall liking scores (β = -5.22, p \u0026lt; 0.001). \u003cstrong\u003e(c)\u003c/strong\u003e Faceted radar charts showing treatment-specific topic emphasis profiles derived from consumer comments. Radar axes represent the six NMF-derived topics, with values indicating the relative emphasis of each topic in comments about each treatment. Significance levels: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, †\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10 (trend).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/fddd1896f31de8730147baf5.png"},{"id":103510510,"identity":"7d56212b-d58c-4d67-80c0-7616df9a35b9","added_by":"auto","created_at":"2026-02-26 14:05:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4371461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/7c92a5ac-d90e-416d-8acc-9b099e94d510.pdf"},{"id":102966746,"identity":"f216b116-dfff-4fe2-b52f-3ccbc397ce86","added_by":"auto","created_at":"2026-02-19 04:38:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14417,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterialCode.docx","url":"https://assets-eu.researchsquare.com/files/rs-8905083/v1/94b6b8309b41dd02f9167a03.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Quality Analysis of an Organic Alternative Curing Process in Boneless Ham Using Consumer Sensory Evaluation and Artificial Intelligence","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePork is considered the most versatile of meats, with cooking applications ranging from roasted loin to dry-cured ham and bacon (Rogers \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the United States, ready-to-eat boneless ham has evolved from traditional cured bone-in cured products into an economical, convenient staple. Typically produced using chunked and formed meat, these products were popularized by innovations such as canned ham and Spam (George A. Hormel \u0026amp; Company), which catered to consumer demands for ease of slicing and serving (Waxman \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The U.S. Department of Agriculture (USDA) defined boneless ham as a fully cooked and cured product, potentially processed in a casing or can that must comply with minimum protein fat-free (PFF) percentages ranging from 16.0% to 19.5% (USDA \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Conventionally, ham is cured using synthetic sodium nitrite and sodium erythorbate (Rasmussen and Sullivan \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These compounds are well-documented for their efficacy in preventing lipid oxidation (Tatiyaborworntham et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), imparting characteristic cured color and flavor, and suppressing the growth of bacteria, including \u003cem\u003eClostridium botulinum\u003c/em\u003e and other spoilage and pathogenic microorganisms (Sheng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe global organic food market hit the highest growth rate ever in 2020, exceeding \u003cspan\u003e$\u003c/span\u003e130\u0026nbsp;billion and the organic meat market has reached an estimated size of \u003cspan\u003e$\u003c/span\u003e1.5\u0026nbsp;billion (Rizzo et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consumers who purchased organic or natural products before the COVID-19 pandemic have maintained or increased their consumption post-pandemic (Brata et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the growing demand for natural food and processed meat has led to the development of alternative curing methods using green leafy vegetable-based nitrate sources, such as celery or Swiss chard juice or powder (Arsenault \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Celery power has been commonly used in the organic meat industry as an alternative meat-curing ingredient since the inception of the USDA National Organic Program (USDA \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Alternative curing agents that are derived from vegetable sources are perceived as healthier options by consumers due to their natural origin and absence of synthetic additives. The National Organic Standards Board (NOSB) currently permits the use of non-organic celery powder in organic products while conducting sunset reviews to transition to organic sources of celery powder. This involves evaluating whether non-organic celery powder should remain on the National List of Allowed and Prohibited Substances or be replaced with organic alternatives (NOSB \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have highlighted the impact of different natural source curing agents on the sensory attributes and consumer acceptance of processed meats (Jin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pham et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sheng et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e). Alternative curing process using plant-derived nitrite sources can influence the final product's flavor profile and non-meat aftertaste potentially due to the presence of plant phytochemicals and their fermented derivatives (Siekmann et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The chlorophyll and carotenoid content in organically grown vegetables can vary depending on cultivation practices (Chausali and Saxena \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), tentatively affecting the color of processed meats (Sheng et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025d\u003c/span\u003e). It is unknown whether the presence of phytochemicals in vegetable-based curing powders could affect the flavor of alternative cured boneless ham. Moreover, it remains unclear whether the development of volatile organic compounds (VOCs) during cooking could further impact consumer perceptions and preferences (Sheng et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). By investigating the influence of both conventional and organic curing ingredients, this research aims to provide insights into how these ingredients derived from different growing practices may affect the sensory properties and overall consumer acceptance of cured boneless ham products.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThus, the objective of this study was to investigate the effects of conventional and organic curing agents on the quality and sensory attributes of cured boneless ham products. Our investigation included physicochemical analysis (proximate analysis, color, and cure efficiency), sensory evaluation using consumer panelists, and the analysis of volatile organic compounds (VOCs) in the finished products. In addition to traditional instrumental methodologies, artificial intelligence techniques, specifically machine learning and natural language processing (NLP), were applied to analyze high-dimensional data. Understanding these distinctions is crucial for government regulators and meat processors aiming to meet consumer demands for both traditional and organic products while ensuring excellence in product quality.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experiment design\u003c/h2\u003e \u003cp\u003eThis study employed both instrumental and sensory analyses to evaluate the impact of various plant-sourced nitrites on the quality and consumer acceptability of ready-to-eat (RTE) boneless ham. A total of 5 treatments (TRT)s were incorporated into the experiment design. A nitrite concentration of 200 ppm of equivalent nitrite was formulated to a 13.34 kg meat block of meat ingredients originating from a synthetic chemical curing ingredient (Sure Cure, Excalibur Seasoning, Wichita, KS, USA. Ingredient contains Salt, Sodium Nitrite (SN, 6.25%), FD\u0026amp;C Red #3) and a vegetable source curing powder included conventionally grown celery (CEL) and Swiss chard (SW) as well as organic grown celery (OCEL) and Swiss chard (OSW). Meat curing was accelerated with sodium erythorbate (547 ppm) for SN and cherry powder for the vegetable source nitrite TRTs.\u003c/p\u003e \u003cp\u003eThe main factorial design was a split plot for measurement over time (day of sampling). Physicochemical properties\u0026rsquo; data including cured meat pigments (CMP), total meat pigments (TMP), total myoglobin content (TMC), and proximate analysis data (moisture, fat, and protein) were analyzed on day 0 with duplicate measurements. Objective color, residual nitrite, and pH were assessed on a 15-day interval from day 0 to day 45 with triplicate measurements. Sensory evaluations were conducted between day 14 and 21. The experiment model included the main effects of treatments, day of sampling, and the interaction of treatment \u0026times; day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Product formulation and manufacturing\u003c/h2\u003e \u003cp\u003eReady-to-eat boneless ham was formulated to contain 200 ppm sodium nitrite or its equivalent when cured with vegetable-source nitrite curing powder. Prior to formulation, the nitrite concentrations of all commercially available vegetable-source curing powders were quantified using a high-performance liquid chromatography (HPLC) system (ENO-20 NOx Analyzer, Eicom Inc., Kyoto, Japan). The nitrite ion equivalents were determined to be 21,487.1, 18,436.1, 23,145.3, and 21,335.6 ppm for CEL, SW, OCEL, and OSW, respectively. A curing accelerator was added at 547 ppm sodium erythorbate for the SN treatment and at 547 ppm ascorbate equivalent for the vegetable-source nitrite treatments. Frozen boneless ham was prepared using an equal ratio of fresh pork \u003cem\u003ebiceps femoris\u003c/em\u003e and \u003cem\u003esemimembranosus\u003c/em\u003e with attached \u003cem\u003esemitendinosus\u003c/em\u003e muscles, sourced from a local supplier with a recent pack date. Frozen meat was stored at -20\u0026deg;C and subsequently thawed in a temperature-controlled cooler (air temperature: 2\u0026deg;C) five days prior to product manufacture. The raw meat was inspected before processing, and excessive silver skin and fat were trimmed to enhance meat binding properties.\u003c/p\u003e \u003cp\u003eAll treatments were formulated and manufactured in a randomly selected order. The meat ingredients were ground using a commercial meat grinder (Model 538A, The Biro Manufacturing Company, Marblehead, OH, U.S.A.) equipped with a kidney-shaped plate (400 3HK Triumph Kidney Plate, Speco Inc., Schiller Park, IL, U.S.A.). The ground meat was subsequently mixed with a formulated brine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) at a 20% inclusion rate and transferred to vacuum tumblers, where it was tumbled continuously for one hour under vacuum (approximately\u0026thinsp;\u0026minus;\u0026thinsp;0.75 MPa) at 1\u0026deg;C to achieve adequate protein extraction and brine pick-up. The mixture for each treatment was held overnight in a temperature-controlled cooler (air temperature: 2.2\u0026deg;C), covered with moisture-impermeable butcher paper. The mixtures were then transferred to a rotary vane vacuum stuffing machine (Model VF616 Vacuum Stuffer, Handtmann Inc., Lake Forest, IL, U.S.A.) and stuffed into 105.1 mm moisture-impermeable plastic casings. The stuffed hams were thermally processed using a ramped steam cook schedule of 60.0\u0026deg;C, 65.6\u0026deg;C, 71.1\u0026deg;C, 76.7\u0026deg;C, and 82.2\u0026deg;C until an internal temperature of 70\u0026deg;C was reached. After thermal processing, the hams were transferred to a ready-to-eat area and chilled for 12 hours at 1\u0026deg;C. The hams were peeled, sliced into 3 mm and 20 mm slices, and vacuum-packaged for 20 seconds using a double-chamber vacuum machine (Multivac C 500, The Multivac Group, Kansas City, MO, U.S.A.) with 4 mil barrier bags (Uline S-19920, Uline Inc., Pleasant Prairie, WI, U.S.A.). Packaged ham slices were stored in an LED-lighted cooler at 1\u0026deg;C for a 45-day sampling period for all sensory and physicochemical analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFormulations\u003csup\u003e1\u003c/sup\u003e for ready to eat (RTE) boneless ham.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCuring ingredients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCuring accelerator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal NMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Formulations: Non-meat ingredients (NMI) were mixed with 11.34 kg of meat ingredients (fresh pork biceps femoris and semimembranosus at equal ratio). NMI percentages were relative to the total meat ingredients weight. All treatments contained: 0.98% salt (sodium chloride), 0.83% dextrose, and 0.2% sodium tripolyphosphate).\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003e The treatment abbreviation refers to nitrite source (sodium nitrite, SN; Celery powder, CEL; Organic Celery powder, OCEL; Swiss Chard Powder, SW; Organic Swiss Chard Powder; OSW)\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003ePortable water was added in the form of an ice-water mixture.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Proximate composition\u003c/h2\u003e \u003cp\u003eProximate composition was measured on samples from all treatments, including crude protein, fat, and moisture, using Association of Official Analytical Chemists (AOAC) procedures. Fat and moisture content in the samples were analyzed using a meat analyzer (CEM Smart 6 Meat Analyzer, CEM Co., Matthews, NC, U.S.A.) with an automatic calibration function (AOAC Official Method 2008.06). Protein content in the samples was analyzed using a nitrogen analyzer (Leco 828 series, LECO Corporation, St. Joseph, MI, USA) according to the Kjeldahl method (AOAC 981.10). Samples for proximate composition analysis were collected on day 0 for all measurements and kept frozen at -80\u0026deg;C. Frozen samples were homogenized in duplicate and measured on day 0 for all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Objective Color Measurement\u003c/h2\u003e \u003cp\u003eColor measurements were taken using a handheld spectrophotometer (Konica Minolta CM-600d, Konica Minolta Inc., Chiyoda, Tokyo, Japan) with a 2\u0026deg; standard observer. The color was measured using the Commission Internationale de l'\u0026Eacute;clairage (CIE) L* (lightness), a* (redness), and b* (yellowness) system. The colorimeter was calibrated using a white calibration cap (CM-A177, Konica Minolta Inc., Chiyoda, Tokyo, Japan) through a vacuum pouch identical to the sample storage vacuum pouch. Five measurements of the white calibration caps were automatically completed using a pre-installed program on the spectrophotometer. Eight measurements were taken through sealed packages on three randomly selected packages from each TRT measurements and calibration were conducted in a temperature-controlled lab space maintained at the optimal measuring temperature of 23\u0026deg;C. Four random locations on each of the three 20 mm slices were evaluated on days 0, 15, 30, and 45.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cure color ratio\u003c/h2\u003e \u003cp\u003eCure color ratios were measured using a spectrometer set to visible reflectance measurement (Shimadzu UV2600 UV-Vis Spectrophotometer coupled with UPC-2600 multipurpose large sample component, Shimadzu Inc., Nakagyō-ku, Kyoto, Japan). The spectrometer was calibrated with a standard white plate compatible with the Shimadzu UPC-2600 through a vacuum pouch identical to the one used to store meat samples. Two slices of 20 mm thick sample stored in a vacuum pouch were measured in the visible wavelength range from 400 to 700 nm. The cured meat ratio was determined from the reflectance readings at 650 nm and 570 nm (King et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 pH measurement\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003epH measurements were conducted according to a method developed by Neo et al. (2006). Five grams of meat sample were blended with ultrapure water (resistivity of 18.2 MΩ.cm) at a 1:9 ratio using a polytron blender at 15,000 rpm. The mixture was then filtered through Whatman #3 filter paper and measured with a pH meter (Fisherbrand\u0026trade; Accumet\u0026trade; model 13-620-AE6; Fisher Scientific, Waltham, MA, USA). Calibration of the pH meter was performed using NIST-certified potassium biphthalate buffer (pH\u0026thinsp;=\u0026thinsp;4.0) and potassium monobasic and sodium hydroxide buffer (pH\u0026thinsp;=\u0026thinsp;7.0).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Residual nitrite and nitrate measurements\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResidual NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e were analyzed using HPLC equipment (ENO-20 NOx Analyzer, Eicom Inc, Kyoto, Japan) coupled with a temperature-controlled autosampler (AS-700, Amuza Inc., San Diego, C.A., U.S.A.) according to the method described by Sheng et al. (2025f?), with modifications based on sample size. The HPLC analysis for NO\u003csub\u003ex\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e was designed based on the Griess nitrite test adopted by the AOAC. Absorption was measured at 540 nm by the UV-Vis detector preinstalled in the nitrite analyzer. Samples were powdered in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis. Samples (5 grams) were weighed into 45 mL of pH 7.4 phosphate-buffered saline (PBS) followed by splitting into two equal volumes of slurries and centrifuged at 3,500 \u0026times; g at 4\u0026deg;C for 5 min (J6-MI centrifuge equipped with a JA-25.50 rotor; Beckman Coulter, Indianapolis, IN, U.S.A.). After centrifugation, supernatants (500 \u0026micro;L) from each slurry and 500 \u0026micro;L of 100% methanol were mixed, transferred to a 1.5-mL snap cap centrifuge tube (Catalog number: 3453 Snap cap low retention microcentrifuge tubes, ThermoFisher Inc., Waltham, MA, U.S.A.), and vortexed for 10 seconds at 3,000 rpm with a digital vortex mixer (cat. no. 0215370, Fisher Scientific Inc, Hanover Park, IL U.S.A). The samples were then centrifuged for 16 min at 15,000 \u0026times; g at 4\u0026deg;C (Eppendorf 5424 centrifuge, Brinkmann Instruments, Westburg, NY, U.S.A.). Supernatants (200-uL) were pipetted into 96-well plates for quantification with the HPLC equipment described in the previous section. Quantitative data (area under the curve) were analyzed with PowerChrom (version 16.0, New South Wales, Australia). The HPLC carrier pump speed was set at 40 mL/hour and reactor pump speed was set at 13.2 mL/hour. A calibration curve was created using 2, 4, 8, and 16 ppm of HPLC-grade sodium nitrite and sodium nitrate. A sodium nitrite standard (8 ppm) was tested at the start and end of each run.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Total and cured meat pigments measurement\u003c/h2\u003e \u003cp\u003e Total pigments, cured pigments, and cure efficacy measurements and calculations were conducted according to the meat pigments measurement guidelines, with modifications based on the AMSA Color Measurement guidelines (King et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A 150-gram meat sample (fully cooked boneless ham) was minced using a commercial mixer (Robot Coupe BLIXER2 Blixer Vertical Commercial Blender with a 2.37 L stainless steel bowl, Robot Coupe Inc., Vincennes, France) for 10 seconds. For nitrosohemechrome determination, minced samples (10 grams) were weighed into a 100 mL beaker containing 43 mL of a solution comprising 40 mL acetone and 3 mL MilliQ water. After intermittent mixing for 5 minutes, the sample was filtered through Whatman #3 filter paper (Cytiva Inc., Marlborough, MA, USA) into a 50 mL polyethylene tube. A 1.5 mL aliquot of the filtrate was transferred into a 1-cm quartz cuvette for measuring absorbance at 540 nm using a UV-Vis spectrometer (Shimadzu UV-1208, Shimadzu Inc., Nakagyo-ku, Kyoto, Japan). Nitrosoheme content, expressed as NO-hematin, was calculated based on the formulation: sample A540 \u0026times; 290. All measurements were conducted in duplicate.\u003c/p\u003e \u003cp\u003eFor total heme pigment determination, minced samples (10 grams) were weighed into a 100 mL beaker with acidified acetone (40 mL acetone, 2 mL MilliQ water, and 1 mL of 37% hydrochloric acid). The mixture was stored at room temperature for one hour with intermittent stirring, then filtered into a 1 cm quartz cuvette using Whatman #3 filter paper. Optical density was measured at 640 nm to determine total heme content, based on the formulation: sample A\u003csub\u003e640\u003c/sub\u003e \u0026times; 680. All measurements were duplicated. All steps were conducted in a laboratory with LED lighting that does not emit UV-A/B. Curing efficacy was calculated as the percentage of nitrosohemochrome (expressed as ppm acid hematin) divided by total pigments (expressed as ppm acid hematin), multiplied by 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Consumer Sensory Panel\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e Three untrained consumer sensory panels, conducted over three consecutive days at the University of Wisconsin-Madison Meat Science and Animal Biologics (MSABD) building, were approved by the University of Wisconsin-Madison Institutional Review Boards (IRB approval #2023\u0026thinsp;\u0026minus;\u0026thinsp;1195). These panels took place between days 14 and 21 to simulate the quality attributes of processed meats as presented to consumers in the commercial supply chain. Voluntary participants included university faculty, staff, students, and members of the general public aged 18 and over, recruited through mass email distribution (approximately 89,738 invitations per study) and an IRB-approved poster (IRB approval #2023\u0026thinsp;\u0026minus;\u0026thinsp;1195).\u003c/p\u003e\u003cp\u003ePanelists were served samples in individual booths separated from the sample preparation area by a one-way glass window. The light intensity in each booth, maintained between 1,614 to 2,153 lux to simulate ideal meat-display lighting, was measured at the beginning and end of each full testing day using a portable industrial handheld color meter (Sekonic C-7000 Spectrometer, Sekonic US, North White Plains, NY, USA).\u003c/p\u003e\u003cp\u003eEach panelist was served four treatments randomly selected from five treatment options, each representing a different source of nitrite. The random selection process included all possible combinations, utilizing 10 base designs to ensure that each control and treatment was sampled equally by the panelists. Samples were served one at a time, with potable water provided to cleanse the palate between tastings. A half slice of RTE boneless ham (3 mm thickness) samples were served cold (3.7\u0026ndash;4.7\u0026deg;C) in a sample cup (100 ml volume) with a clear plastic lid.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Volatile Compounds Analysis\u003c/h2\u003e \u003cp\u003eVolatile compounds (VOCs) analysis was conducted according to a method described by Wettasinghe et al. (2001) with modifications accordingly based on sample weight and size. Multiple studies have been conducted on extraction methods and confirmed that steam distillation generally extracts more VOCs than solid-phase microextraction methods (Hong et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Madruga et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Watkins et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Xie et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSamples (200 grams) were ground by a commercial meat blender (Robot Coupe BLIXER2 Blixer Vertical Commercial Blender with 2.5 Quarts stainless steel bowl, Robot Coupe Inc., Vincennes, France). Ground samples (10 grams) were mixed with 10 grams of sodium chloride in a 200 mL volumetric tube specifically designed to fit in a fast steam distillation system (SCP DigiPREP Distillation System, SCP Science, Baie-d'urfe, Canada). The distillation was conducted at 60% strength for 300 seconds. 100 mL of distillate was mixed with 150 mL of methylene chloride in a 500 separatory flask mixed vigorously and allowed to remain at ambient temperature (23\u0026deg; C) for 2 hours. The methylene chloride layer was vaporized under vacuum using a rotary evaporator (Rotavapor, Buchi, Flawil, Switzerland) at 39.6\u0026deg;C. When the condensate reached a volume of approximately 5 mL, they were removed from the rotary evaporator meticulously and mixed with 5 grams of sodium formate to remove residual water content in the solution. The concentrate was subsequently transferred into a dark glass vial with a screw-on lid and stored at -80\u0026deg;C until further analysis.\u003c/p\u003e \u003cp\u003eGas chromatography with mass spectrometry tandem (GC-MS/MS) analysis was conducted using a GC-MS/MS system coupled with autosampler (Shimadzu GCMS-TQ 8040NX with AOC-20 plus autosampler, Shimadzu Inc., Nakagyo-ku, Kyoto, Japan) supplied with helium gas with a smart switch. Separation of VOCs was conducted on a general-purpose fused silica low polarity, crosslinked diphenyl dimethyl polysiloxane phase column (Shimadzu SH-I-5MS Capillary Column, 30m x 0.25mm x 0.25um, Shimadzu Inc., Nakagyoku, Kyoto, Japan) for semi-volatiles, phenols, amines, residual solvents, drugs of abuse, pesticides, PCB congeners with operation temperature range (-60 to 330/350\u0026deg;C). The oven temperature was programmed from 47\u0026deg;C to 240\u0026deg;C at a rate of 5\u0026deg;C/min with an initial and final hold time of 5 minutes and 10 minutes respectively. The total running time was 60 minutes. For the mass spectrometry detector, the electron ionization energy was set at 70eV. Mass range, electron multiplier voltage, and scan rate were set at m/z 33\u0026ndash;330, 2000v, and 20,000 u/sec, respectively and the ionization source temperature has been maintained at 230\u0026deg;C (Li et al., 2023).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIdentification of VOCs was accomplished by matching the mass spectral data of sample compounds with an Electron Spray (EI) NIST database (NIST 23 Tandem Mass Spectral Libraries). The area under the curve (AUC) was integrated using the Savitzky-Golay method (smoothness setting at 25) with a width, setting of 0.040 minutes. Each integrated area was compared with the EI database based on spectrum similarity and then manually analyzed based on fragmentation patterns. The results of each sample TRT were integrated for comparison using Python (Python version 3.12.7, The Python Software Foundation) on Spyder (The Scientific Python Development Environment, version 6.0.1, Spyder-IDE.org) as an Integrated Development Environment (IDE).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Traditional Statistical analysis\u003c/h2\u003e \u003cp\u003ePhysiochemical property data including cured meat pigments (CMP), total meat pigments (TMP), salt, and proximate analysis (moisture, fat, and protein) data were analyzed on day 0 with measurements done in duplicate. Objective color (Commission Interational de I\u0026rsquo;Eclairage [CIE] L* [Lightness], a* [redness], and b*[yellowness]), cured color ratio, residual nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), and pH were assessed on 15-day intervals from day 0 to day 45 with measurements conducted in triplicate. Since the TRTs were only manufactured once, for the physiochemical analysis data, descriptive statistical analyses were performed to determine means and a measure of variation associated with the repeated measures. One way Analysis of Variance (ANOVA) was performed to study data from sensory evaluations. Data from consumer sensory and physiochemical analysis on finished products were analyzed using R (R version 4.3.3; R Core Team 2024). Least square means were used when sample sizes were unequal, or values were missing. Normal tests (Kolmogorov-Smirnov test and adherence test) were conducted to verify normal distribution before conducting statistical tests. One-way Analysis of Variance (ANOVA) was used to detect significant differences between TRTs for physicochemical and sensory data during one sampling period. The Tukey and Dunnet Multiple Comparison Test was used if significant differences were detected. Pearson correlation and principal component analysis (PCA) were used to analyze sensory attributes and volatile compounds. The correlation coefficient (\u0026ldquo;r\u0026rdquo;) indicates the positive or negative of the relationships. PCA dimensionally reduced all variables into two principal components, PC1 and PC2 to describe data relationships based on eigenvalues (from parallel analysis). A Scree plot was utilized to validate PCA by determining the number of principal components to retain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Artificial Intelligence applications\u003c/h2\u003e \u003cp\u003eSupervised classification models were employed to evaluate whether instrumental color variables could distinguish among all curing treatments. Analyses were conducted using Python (Python version 3.12.7, The Python Software Foundation) using the scikit-learn library (version. 1.8.0). Three classifiers were selected to accommodate moderate sample sizes and correlated predictors: (1) Multinomial Logistic Regression, chosen for its interpretable coefficients; (2) Random Forest, utilized for its ability to model nonlinear interactions; and (3) Histogram-based Gradient Boosting, which is highly effective for tabular data (Couronn\u0026eacute; et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Darwish \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To prevent data leakage, continuous predictors were standardized (z-score normalization) within each training fold using a scikit-learn Pipeline. Repeated measurements were obtained from the same experimental unit over time; thus, observations were not independent. Consequently, a group-aware cross-validation strategy (GroupKFold) was employed to ensure that all observations from a given replicate remained distinctively within either the training or the test split. Model performance was assessed using balanced accuracy and the macro-average F1-score to account for class imbalance (Cardoso et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The model's generalizability was validated using an independent 80/20 train-test split, where 20% of the data was reserved to confirm predictive accuracy and prevent overfitting (Asim Shahid et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsumer comments were analyzed to identify recurring sensory language themes and to examine their association with hedonic ratings. Comments were preprocessed by removing punctuation, normalizing whitespace, and excluding English stop-words. The text was subsequently represented using a bag-of-words model, which captures term frequency while disregarding word order (Dai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This process produced a document\u0026ndash;term matrix, X, where rows correspond to comments and columns correspond to terms.\u003c/p\u003e \u003cp\u003eNon-negative Matrix Factorization (NMF) was selected for topic modeling for its interpretable topic\u0026ndash;word loadings that facilitate the labeling of topics within a sensory context (Si et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). NMF factorizes X into non-negative matrices W (document\u0026ndash;topic weights) and H (topic\u0026ndash;word weights) such that:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:X\\approx\\:WH$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eA six-topic solution was selected as it yielded distinct, interpretable themes corresponding to the primary sensory dimensions within the comment corpus (Nijs et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo evaluate whether topic emphasis in comments was associated with ratings, while accounting for repeated measures from the same individuals, linear mixed-effects models were fitted with a random intercept for each consumer (Roberts et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{R}\\text{a}\\text{t}\\text{i}\\text{n}{\\text{g}}_{ij}\\text{}={{\\beta\\:}}_{0}\\text{}+\\text{k}=1\\sum\\:_{\\text{k}=1}^{\\text{K}}{{\\beta\\:}}_{\\text{k}}{\\:\\text{W}}_{ijk}\\text{}+{\\text{u}}_{\\text{i}}\\text{}+{\\text{ϵ}}_{i\\text{j}}\\text{}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e indexes consumers, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003eindexes comments, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{ijk}\\:\\)\u003c/span\u003e\u003c/span\u003eis the NMF topic weight for topic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\:\\)\u003c/span\u003e\u003c/span\u003ein observation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003efrom consumer \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\sim\\:N\\left(0,{\\sigma\\:}_{u}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003ecaptures each consumer\u0026rsquo;s baseline rating tendency, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{ij}\\sim\\:N\\left(0,{\\sigma\\:}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003eis the residual error. Models were fit separately for Overall Liking and Non-meat aftertaste. Fixed-effect coefficients are reported with 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Proximate composition and physiochemical analysis\u003c/h2\u003e \u003cp\u003eProximate analysis of boneless ham cured with various nitrite sources on Day 0 is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The compositional profiles (moisture, protein, fat, and salt) of boneless ham cured with either conventional or organic vegetable-sourced nitrite were similar to those of the control made with sodium nitrite (SN) (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Boneless ham moisture protein ratio was between 3.15:1 and 3.30:1 which not only meets but is beyond the recommended value from the USDA Food database indicating a good value for consumers (USDA \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The total moisture percentage and protein in finished products for all TRTs were between 74.31 to 75.62, and 22.75 to 23.58 percent, respectively, with no observational difference among TRTs. Protein fat-free (PFF) in all treatments was above the regulation limit for common and usual chunked (or chopped) and formed pork products (USDA \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCured meat pigments were 33.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, 29.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50, 43.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57, 58.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, and 54.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 ppm acid hematin equivalent, while total meat pigments were 92.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, 94.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99, 92.17a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, 105.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00, and 102.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 ppm acid hematin equivalent, respectively, for SN, CEL, OCEL, SW, and OSW (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cure meat efficiency in treatments (TRTs) ranged from 31.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53% to 55.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72%, with no observational significant differences between vegetable source TRTs except for CEL with a conversion rate of 31.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A survey conducted on boneless ham reported that the average cure efficiency of naturally cured ham ranged from 26.5% to 37.3%, while sodium nitrite-cured ham ranged from 31.8% to 46.0% (Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The ideal conversion rate for heme pigments to the nitrosohemechrome form during curing is considered to be approximately 80% (Pearson \u0026amp; Tauber, 1984). The overall cure efficiency in TRTs was in alignment and slightly higher than the value reported in a previous study (Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) possibly due to the application of the vacuum tumbling process (Marriott et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross all TRTs, pH was determined to be in the range of 6.46 to 6.51, with no differences among them (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results were found to be higher than those reported in a previous survey on commercially available boneless ham products (Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), possibly due to the freshness of the finished products in this study compared with commercial samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans of proximate analysis cured meat pigments. Total meat pigments, cured efficiency, and pH of treatments of ready-to-eat (RTE) boneless ham at day 0.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOCEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOSW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.62\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.67\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.69\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.34\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.31\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.75\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.24\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.45\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.47\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.58\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003csup\u003eab\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture/Protein Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.19\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.15\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCured Meat pigments*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.50\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.40\u003csup\u003ec\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.86\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.40\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.09\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Meat Pigments*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.48\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.78\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.17\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.40\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.00\u003csup\u003ea\u003c/sup\u003e\u0026plusmn; 0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCured efficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.22\u003csup\u003eb\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.03\u003csup\u003eb\u003c/sup\u003e\u0026plusmn; 0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.59\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.41\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.11\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.51\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.49\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.47\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.49\u003csup\u003ea\u003c/sup\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u0026minus;c\u003c/sup\u003e means without common superscript are different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eStandard errors (SEM) are displayed following\u0026thinsp;\u0026plusmn;\u0026thinsp;for each cell.\u003c/p\u003e \u003cp\u003eSN: sodium nitrite; CEL: celery; OCEL: Organic Celery juice; SW: Swiss chard; OSW: organic Swiss chard.\u003c/p\u003e \u003cp\u003e* Expressed as ppm acid hematin\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Objective color and residual nitrite\u003c/h2\u003e \u003cp\u003eThe results of nitrite depletion along with color change during the 45-day study period are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the current study, the amount of residual nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) initially ranged from 54.37 to 64.43 ppm on day 0, gradually decreasing to a range of 8.30 to 11.90 ppm over time by day 45 after manufacture. These results were in align with a recent national nitrite and nitrate depletion study (Sheng et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025e\u003c/span\u003e). On day 0, SN was found to contain the highest amount (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the plant nitrite source TRTs. On day 30, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in OSW decreased to a level significantly lower than CEL (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) and SN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0046). On day 45, the residual nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) in organic nitrite source TRTs decreased to a level lower than in conventional TRTs, with OCEL and OSW lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than CEL and SW. NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e is considered a reservoir that retains the color by continually replenishing the cured meat color lost from oxidation and photooxidation in processed meats (Mancini \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). During the sampling period, the lower amount of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in plant-sourced nitrite-cured boneless ham is possibly due to the presence of polyphenols and other active compounds that interact with nitrite during curing (Niu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e potentially can be reduced by polyphenols in plant-source curing powders to nitric oxide (Rocha et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). Additionally, some phenolic compounds can undergo nitrosation at certain conditions, further reducing the amount of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (Rocha et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e). Residual NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e in all TRTs during the course of the study were not completely depleted and considered sufficient for controlling bacteria growth and product color attributes (Davidson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CIE L* value is an objective measure of meat lightness. It ranges from 0 (black) to 100 (white), with higher values indicating lighter meat and lower values indicating darker meat color (King et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There were no significant differences detected on sampling days 0 and days 30. On day 15, OCEL and SW exhibited a lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) CIE L* value compared with the remainder of the TRTs, and on day 45, SN had the highest CIE L* (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared with all vegetable sources nitrite TRTs.\u003c/p\u003e \u003cp\u003eThe CIE \u003cem\u003ea*\u003c/em\u003e value indicates the redness of meat. It measures on a scale from negative values (greenish) to positive values (redness), with higher positive values indicating redder meat and lower values (or negative values) indicating greener meat (King et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There were no differences detected among all TRTs on sampling day 0, and 15. On day 30, SW displayed reduced redness compared with the rest of the TRTs, and on day 45, organic source nitrite TRTs (OCEL and OSW) had a higher redness compared with the rest of the TRTs. The overall CIE a* value was similar but overall lower than the a* value from a previous study on boneless ham products. In the referenced study, naturally cured ham\u0026rsquo;s CIE a* value ranged from 14.2 to 17.9, and sodium nitrite cured ham\u0026rsquo;s CIE a* value ranged from 13.9 to 17.2. The potential cause for this difference could be the inclusion of bone-in-ham products in the referenced study, which possess higher redness values (Sullivan et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe cured meat color ratio (measured by the reflectance of 650/570 nm) in CEL was higher than the other plant-sourced nitrite TRTs at day 0. The initial cured meat color of CEL was over 2.1, indicating an excellent cure color followed by OCEL (\u003cem\u003ex̅\u003c/em\u003e = 2.10), CEL (\u003cem\u003ex̅\u003c/em\u003e = 2.09), OSW (\u003cem\u003ex̅\u003c/em\u003e = 2.08), and SW (\u003cem\u003ex̅\u003c/em\u003e = 2.07). The trends remained stable during sampling. By day 45, CEL had the highest cured meat ratio, which was higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than OCEL (x̅ = 1.92), OSW (x̅ = 1.93), and SN (x̅ = 1.89). Moreover, SW (x̅ = 1.86) was lower than the rest of the TRTs.\u003c/p\u003e \u003cp\u003eThe hue angle signifies the color shift from redness (lower degree angle) to yellowness (higher degree angle) (King et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The overall trends in the 45-day sample indicated that SN consistently decreased in Hue value over time. At day 0, the Hue angle of SN showed no difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) compared to CEL, OCEL, and OSW, but was lower than SW (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By day 45, SN had the lowest Hue angle among all TRTs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among vegetable source nitrite treatments (TRTs), OCEL had the lowest Hue angle on day 0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By day 45, OSW exhibited a lower Hue angle (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to its conventional nitrite source TRTs.\u003c/p\u003e \u003cp\u003eThe Chroma C* value is directly calculated using the CIE a and b* values, indicating colorfulness relative to the brightness of the surroundings (Tomasević et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is better correlated with human visual color perception. On day 0, SN had the lowest Chroma (C*) compared to the vegetable source nitrite TRTs and maintained this trend throughout the sampling period. Among the vegetable source nitrite treatments, SW exhibited the highest mean saturation being significantly higher than OCEL throughout the entire sampling period. In a study conducted by Posthuma et al (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), they reported that processed meats cured with sodium nitrite exhibited higher CIE a* values and lower CIE b* values compared to those treated with celery juice powder containing the same level of nitrite (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Sodium nitrite leads to a more intense red color and reduced yellowness (Posthuma et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The main reason for the decrease in Chroma C* is believed to be due to oxidation, which flattens the shape of the color spectrum and reduces the vividness of the color (Hern\u0026aacute;ndez Salue\u0026ntilde;a et al., 2019). The variation in plant pigment profile could explain the differences in saturation observed during the sampling period. Ivanović et al (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted a study on the phytochemical content of Swiss chard and found that Swiss chard grown under different fertilization treatments was found to be a good source of total chlorophyll (47.13 mg/100 grams fresh weight), carotenoids (9.85 mg/100 grams fresh weight), minerals, and vitamin C (26.88 mg/100 grams fresh weight). Furthermore, they noted that different fertilization regimes significantly affected the content of phosphorous, protein, chlorophyll a, chlorophyll b, and vitamin C, while irrigation treatments did not have a significant effect (Ivanović et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, carotenoids, another category of plant pigments, can contribute to yellow to orange hues and may influence the color attributes of cured meats. Several studies (Indu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dhakal et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; S\u0026oslash;ltoft et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have demonstrated that organic farming practices, which forego pesticides and utilize different fertilizers, can enhance the content of secondary metabolites such as carotenoids. The variations of plant pigments could explain the intricate relationship between growing practice and the resulting color properties of cured meat in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensory evaluation\u003c/h2\u003e \u003cp\u003eA total of 123 panelists representing a diversity in age, gender, and consumption frequency were recruited to attend one of the sensory evaluation sessions held at the MSABD sensory lab. The age distribution of the participants was as follows: 24% were aged 18 to 24, 28% were aged 25 to 34, 18% were aged 35 to 44, 19% were aged 45 to 54, 8% were aged 55 to 64, and 4% were over 64. Regarding boneless ham consumption, 13% of the consumers indicated that they consumed boneless ham at least once per week, 29% consumed it several times per month, 24% consumed it once per month, and 33% consumed it less than once a month.\u003c/p\u003e \u003cp\u003eSensory evaluation revealed no differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for color, aroma, overall liking, and purchase intent among boneless ham cured with different curing ingredients, whether from organic or conventional vegetable curing powders (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Furthermore, PCA analysis indicated that color and aroma were strongly positively correlated with overall liking while did not correlate with non-meat aftertaste perception (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Pearson\u0026rsquo;s correlation analysis indicated that overall liking was strongly correlated with purchase intent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). These findings were consistent with previous studies which demonstrated that the addition of plant-source curing or antioxidant ingredients had no negative effects on the sensory attributes of processed and cured meat, using cherry and lime powder (Xi et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), celery powder (Jin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), grape seeds extract (Parrini et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and \u003cem\u003eOcimum. gratissimum\u003c/em\u003e leaf extract (Akwetey et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eApart from the overall lack of difference in sensory evaluation results, a significant difference was identified in the non-meat aftertaste. OSW exhibited a notably higher non-meat aftertaste compared to SN (p\u0026thinsp;=\u0026thinsp;0.03) and SW (p\u0026thinsp;=\u0026thinsp;0.013). Furthermore, PCA analysis indicated that non-meat aftertaste was moderately negatively correlated with overall liking (r = -0.44) and weakly negatively correlated with purchase intent (r = -0.33). This finding aligns with previous studies that formulated processed meats with plant-sourced nitrites, which reported undesirable aftertastes in turkey bologna (Djeri and Williams \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and in reduced sodium boneless ham (Pietrasik et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and regular boneless ham (Sindelar et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Sindelar et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported that vegetable aroma is detectable by a trained sensory panelist at an inclusion above 0.35% in boneless ham products. Furthermore, a high concentration inclusion of vegetable powder significantly decreased ham aroma while increasing vegetable aroma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pietrasik et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that celery-cured ham exhibited a significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) flavor and aftertaste compared to sodium nitrite-cured traditional ham in their study on the use of high-pressure processing to enhance the quality and shelf life of cooked ham.\u003c/p\u003e \u003cp\u003eThe age groups in the consumer sensory panels demonstrated different perceptions of non-meat aftertaste and overall liking of the finished products. Only the 18 to 24 and 45 to 54 age groups demonstrated a significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) perception of non-meat aftertaste compared to other age groups. The OSW TRT was perceived with higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) non-meat aftertaste than OCEL TRT by these two age groups. In the 45 to 54 age group, a significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) purchase intent was identified for OSW TRT. The possible reason could be the significantly reduced plant aroma in OCEL compared to the rest of the alternative curing powders. A study conducted by Sheng et al. (Sheng et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e) on volatile compounds in alternative meat curing powders indicated that OCEL contains considerably fewer amount of VOCs compared to other alternative TRTs due to the potential deodorization process during manufacture. Furthermore, the difference in perception of non-meat aftertaste could be attributed to age-related sensory perception differences. Age-related decline in olfaction is generally more pronounced than taste loss (Seiberling and Conley \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and the degeneration of olfactory neurons, reduced blood flow to the olfactory bulb, and changes in mucus production could possibly explain the cause of olfactory decline (Marin et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The 55 and older age group did not indicate any sensory attribute differences, which could be explained by age-related sensory-specific satiety, where the elderly have a diminished pleasantness of the taste of eaten food (Rolls and McDermott \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, consumption frequency impacts non-meat aftertaste and overall liking of boneless ham products. The least and most frequent consumption groups did not indicate any significant difference in non-meat aftertaste or overall liking (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the middle consumption frequency group clearly indicated a higher perception of non-meat aftertaste in OSW TRT compared to CEL TRT. Additionally, the once-per-month group indicated a significantly lower overall liking of OSW. This could likely be explained by sensory-specific satiety, where repeated consumption of a specific food leads to a decline in its perceived pleasantness and the ability to distinguish differences (Rolls et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Hetherington \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Volatile Compounds Analysis\u003c/h2\u003e \u003cp\u003eFrom the sensory evaluation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the organic-produced plant source curing powder (OSW) developed a noticeably stronger (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) non-meat aftertaste, which was associated with reduced overall liking and corresponding purchase intent. To further understand the underlying reasons, we conducted a VOC analysis on all TRTs using steam distillation and gas chromatography technologies. A total of 781 VOCs were identified, of which 168 compounds exhibited a spectrum fragmentation pattern with at least 78% similarity to a compound in the current National Institute of Standards and Technology (NIST) electron ionization (EI) database. Correlation studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and a distinct aromatic compound profile were established from identifiable VOCs among all TRTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Lipolysis, proteolysis, and Maillard reactions are believed to be the main biochemical reactions involved in the generation of these compounds (Garc\u0026iacute;a-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Alkanes, alkenes, alcohols, and aromatic hydrocarbons were the most abundant volatile compounds. Additionally, aldehydes, ketones, cycloalkanes, amines, esters, sulfur-containing compounds, terpenoids, and furans were identified and relatively quantified using the area under the curve (AUC) approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was a relative much greater number of alkanes, and alkenes developed in conventional and organic celery-cured TRTs than Swiss chard-cured boneless ham (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Furthermore, alkanes, alkenes, alcohols, and aromatic hydrocarbons were strongly positively correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) with each other based on Pearson\u0026rsquo;s r analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This potentially caused by their similarity of chemical structures as hydrocarbons, leading to comparable behaviors during thermal processing and interaction with meat components. Furthermore, the formation pathways of these compounds during meat processing are interconnected. The breakdown of fats and proteins are known to lead to the formation of alcohols, aldehydes, and ketones, which can further react to form alkanes, alkenes, and aromatic hydrocarbons (Dickey et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTerpenoids compounds exhibited a strong positive correlation with non-meat aftertaste (r\u0026thinsp;=\u0026thinsp;0.61). The correlation is potentially attributed to the citronellal compounds found in OSW that exhibited higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) non-meat aftertaste scores than the remainder of the TRTs. Citronella was identified at a relatively higher concentration in OSW but not found in other TRTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Citronella is a monoterpenoid and aldehyde giving a distinctive lemon aroma and is regarded as one of the most important terpenes (Information 2024; Lenard\u0026atilde;o et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Although a lemon-like scent is generally considered a pleasant fragrance may not contribute to a desirable aroma for meat consumers. A cognition-based framework study indicated that consumer\u0026rsquo;s perception of odor is associated with individuals\u0026rsquo; episodic memories and past experiences (Morrin and Ratneshwar \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Familiarity is one of the basic dimensions through which individuals perceive smells (Rabin and Cain \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The terpenoid notes (typically herbal, woody), which are not typically associated with the flavor of processed meat, may explain the perception of non-meat aftertaste in some TRTs (Šojić et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This non-meat aftertaste was negatively associated with overall liking and purchase intent.\u003c/p\u003e \u003cp\u003eEsters were moderately negatively associated with non-meat aftertaste (r=-0.53). Esters tend to provide fruity and floral aroma and are associated with free amino acids in processed pork products (Tian et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A wider variety of esters were identified in SN, OCEL, and SW than OSW (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) could potentially explain the negative association between esters and non-meat aftertaste. Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported 5 esters among the 49 flavor substances found on the surface of Mian-ning ham, a traditional Chinese dry-cured ham. They concluded that the presence of esters contributed to the characteristic aroma of the ham. Li et al. (2020) also asserted that esters as important flavor compounds. The study categorized esters in contributing to the unique flavor profiles of ham in a study on volatile profiles of Chinese dry-cured hams from different regions.\u003c/p\u003e \u003cp\u003eFurans (C\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eO) were also a group of compounds found negatively associated with non-meat aftertaste (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.41). Furans tend to exhibit sweet, caramel-like, and sometimes smoky notes (Mu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They were most abundant in SN but not identified in plant-source nitrite TRTs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furan has been documented as a \u0026ldquo;possible human carcinogen (group 2b)\u0026rdquo; by International Agency for Research on Cancer (IARC) (Commission \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Possible pathways for furans formation include thermal degradation and rearrangement of carbohydrates and amino acids, and oxidation of amino acids and ascorbic acids (Seok et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Certain phytochemicals may reduce the formation of furan by inhibiting oxidative reactions during thermal processing (Dhakal et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Oral et al. reported that total furan formation was inhibited by plant-source antioxidants such as caffeic acid, punicalagin, and epicatechin in a glucose-glycine and asparagine-glucose Maillard reaction model system (Oral et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, it remains unclear whether the inhibitory effects on furan formation derive from the plant nitrite source curing powder, the cherry powder, which contains a high concentration of ascorbic acids, or the combined effects of both. The synergistic interaction between these components could potentially enhance the overall inhibition of furan formation more effectively than either component alone. Future research should aim to delineate the individual and combined effects of these compounds to fully understand their roles in mitigating furan formation in processed meats.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Integrating artificial intelligence into color assessment.\u003c/h2\u003e \u003cp\u003eMachine learning (ML) has emerged as an effective computational framework to augment traditional linear and multilinear regression methods, particularly in identifying complex, non-linear relationships within high-dimensional datasets (Raschka et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While classical frequentist models often struggle with multicollinearity and non-monotonic associations, ML-based methodologies such as Support Vector Machines and Gradient Boosting were utilized in this study to provide enhanced granularity and predictive resolution (Fig.\u0026nbsp;5a). By leveraging these algorithmic approaches, we were able to capture subtle interactions between variables that traditional statistical tools might overlook, thereby offering a more robust interpretation of the gathered data.\u003c/p\u003e \u003cp\u003eObjective color measurements as the most important quality parameter were utilized as predictors in this study. Grouped cross-validation indicated that multinomial logistic regression provided the best overall generalization among the tested models, achieving the highest macro-F1 score. Held-out evaluation yielded approximately 0.75 accuracy and 0.74 macro-F1, indicating that objective color features contain treatment-discriminating information significantly beyond chance. The confusion matrix (Fig.\u0026nbsp;5b) revealed that treatment separability was non-uniform. SN and SW were classified with high reliability achieving near-perfect accuracy in the held-out test set and suggesting that their objective color profiles occupy distinct regions within the feature space. In contrast, the remaining treatments exhibited mutual misclassification, indicating overlaps in objective color signatures consistent with their similar visual attributes. Feature importance summaries from the linear model (Fig.\u0026nbsp;5c) indicated that the cured meat ratio and \u003cem\u003eChroma C\u003c/em\u003e (and to a lesser extent \u003cem\u003eCIE b*\u003c/em\u003e and hue angle) contributed most strongly to treatment separation, whereas \u003cem\u003eCIE L*\u003c/em\u003e and \u003cem\u003eCIE a*\u003c/em\u003e were less influential in this dataset. Collectively, Figs.\u0026nbsp;5 (b\u0026ndash;c) support the conclusion that treatment identity is reflected in objective color measurements, driven primarily by pigment-related and saturation-related metrics rather than variations in lightness.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5. Classification performance and feature analysis of different treatments using colorimetric parameters.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(a)\u003c/b\u003e Diagram of Machine Learning Approaches to Classifying Nonlinear Relations \u003cb\u003e(b)\u003c/b\u003e Normalized confusion matrix showing classification accuracy for five treatment types: sodium nitrite (SN), conventional celery powder (CEL), organic celery powder (OCEL), Swiss chard (SW), and organic Swiss chard (OSW). Rows represent true classes and columns represent predicted classes. Values indicate the proportion of samples from each true class assigned to predicted classes. Diagonal values represent correct classification rates. Model: Logistic Regression classifier trained on L*a*b* and spectral features. Overall accuracy: 74.5%; macro F1-score: 0.738. Test set: n\u0026thinsp;=\u0026thinsp;55 samples (11 per class). \u003cb\u003e(c)\u003c/b\u003e Permutation importance analysis quantifying the contribution of each colorimetric feature to classification performance. Horizontal bars indicate the decrease in model accuracy when feature values are randomized (Δ accuracy), with error bars representing standard deviation across 30 permutations. Percentage values on bars indicate relative contribution of each feature to overall model performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Natural Language Processing on Consumer panel\u0026rsquo;s comments\u003c/h2\u003e \u003cp\u003eTraditional sensory evaluation has long relied on structured quantitative scales such as 9-point hedonic tests (Wichchukit and O'Mahony \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or just about right tests (Gacula Jr et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to assess consumer liking and attribute intensity. While these metrics provide a statistical baseline, they often fail to capture the nuanced, qualitative \"why\" behind consumer preferences (Nunes et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Phillips and Smit \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Open-ended comments offer a rich repository of this latent information. However, the unstructured nature of human language fraught with idiosyncratic terminology, varied syntax, and varying levels of detail has historically made large-scale manual analysis labor-intensive and susceptible to subjective bias (Alam et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mani et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of AI and NLP potentially can represent a paradigm shift in food science, transforming these qualitative narratives into actionable, high-dimensional data. This study utilizes these cutting-edge methodologies to provide a deeper, data-driven understanding of how alternative curing processes influence the holistic consumer experience. Based on the highest-loading terms within each topic, we identified six primary themes: Topic 0 (Ham Identity/Appearance) (e.g., \u0026ldquo;ham,\u0026rdquo; \u0026ldquo;like ham,\u0026rdquo; \u0026ldquo;looks\u0026rdquo;); Topic 1 (Positive Appraisal) (e.g., \u0026ldquo;good,\u0026rdquo; \u0026ldquo;really good,\u0026rdquo; \u0026ldquo;good flavor/texture\u0026rdquo;); Topic 2 (General Sensory Descriptors) (e.g., \u0026ldquo;taste,\u0026rdquo; \u0026ldquo;aroma,\u0026rdquo; \u0026ldquo;appearance,\u0026rdquo; \u0026ldquo;smell\u0026rdquo;); Topic 3 (Flavor/Color/Salt Notes) (e.g., \u0026ldquo;flavor,\u0026rdquo; \u0026ldquo;color,\u0026rdquo; \u0026ldquo;pink/red,\u0026rdquo; \u0026ldquo;saltiness\u0026rdquo;); Topic 4 (Texture/Mouthfeel) (e.g., \u0026ldquo;texture,\u0026rdquo; \u0026ldquo;chewy,\u0026rdquo; \u0026ldquo;rubbery\u0026rdquo;); and Topic 5 (Aftertaste/Off-notes) (e.g., \u0026ldquo;aftertaste,\u0026rdquo; \u0026ldquo;non-meat,\u0026rdquo; \u0026ldquo;strange/weird,\u0026rdquo; \u0026ldquo;strong aftertaste\u0026rdquo;).\u003c/p\u003e \u003cp\u003eAcross formulations, consumers utilized a consistent set of sensory dimensions, indicating that the six-topic NMF solution effectively captured a shared \u0026ldquo;sensory language space\u0026rdquo; across the dataset. However, the relative emphasis of these themes varied by treatment. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea summarizes mean topic weights by treatment highlighting treatment-specific language signatures identifying which attributes consumers prioritized when describing each formulation.\u003c/p\u003e \u003cp\u003eTo relate comment themes to ratings while accounting for repeated measurements, we utilized mixed-effects models with a random intercept for each consumer. This specification separates inter-consumer variability (stable differences in scale usage, such as \"harsh\" vs. \"generous\" raters) from intra-consumer variation across samples. Consequently, the fixed-effect topic coefficients in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e (a-b) represent within consumer associations: holding a consumer\u0026rsquo;s baseline rating constant, observations where their comments contain higher weights of a given topic are associated with systematically higher or lower ratings.\u003c/p\u003e \u003cp\u003eWithin consumers, a higher weight in the Positive Appraisal topic was significantly associated with higher Overall Liking, confirming that this topic captures explicitly favorable language (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Conversely, a higher Aftertaste/Off-notes weight was associated with lower Overall Liking, indicating that when a consumer\u0026rsquo;s comment was dominated by off-note language, their liking scores decreased. General Sensory Descriptors also trended negative, suggesting that more generic descriptive language\u0026mdash;after controlling for consumer baseline\u0026mdash;reflected more mixed or less enthusiastic evaluations.\u003c/p\u003e \u003cp\u003eFor Non-meat Aftertaste ratings, the Aftertaste/Off-notes topic showed a strong positive correlation with the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Interpreted within-consumer, this indicates that when a participant\u0026rsquo;s comments placed greater emphasis on off-notes, they systematically assigned higher non-meat aftertaste intensity ratings. In contrast, the remaining topics (Topics 0\u0026ndash;4) exhibited negligible effects with greater uncertainty relative to this dominant signal.\u003c/p\u003e\u003cp\u003eFig 6c summarizes these fixed effect estimates with 95% confidence intervals. A distinct and variable pattern emerged across the six NMF-derived topics: specifically, OCEL and SN were characterized by fewer descriptive terms, while OSW was more strongly associated with General Sensory Descriptors. These findings using NLP confirm that consumer comments\u0026mdash;a rich data source that has long been underutilized\u0026mdash;should be integrated into sensory research. This approach can effectively complement traditional scaled tests or serve as an independent tool for assessing the nuances of consumer perception.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe process of meat curing plays a vital role in the development of the color and sensory attributes of cured meats such as boneless ham, with results indicating that instrumental and consumer sensory attributes remain consistent whether using conventional or organic plant-based nitrite sources. While organic and conventional growing practices influence aroma formation and the development of distinct VOC profiles that subsequently impact consumer perceptions of non-meat aftertaste, these differences do not significantly alter purchase decisions. However, consumer age and consumption frequency do emerge as variables that modulate the perception of aftertaste and purchase intent. These findings suggest that the presence of bioactive plant compounds in alternative curing powders warrants further investigation into the effects of long-term storage on meat quality, antioxidant benefits, and broader health outcomes. Furthermore, the application of artificial intelligence technologies including machine learning and natural language processing in this research provides superior analytical value compared to traditional statistical tools, advocating for their broader integration into food science methodologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSiyuan Sheng: Conceptualization, Writing \u0026ndash; Original draft, Visualization, Methodology, Investigation, Supervision, Formal analysis, Data curation, software, Validation, Writing \u0026ndash; review \u0026amp; editing. Zihan Sun: Methodology, Investigation, Formal analysis, Resources. Steven Ricke: Resources, Writing \u0026ndash; review and editing. James Claus: Conceptualization, Resources, Supervision, Project administration, Writing \u0026ndash; review and editing, Validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e.S. received funding from\u0026nbsp;Organic Research and Extension Initiative program of the U.S. Department of Agriculture, National Institute of Food and Agriculture (award # 2019-51300-30243).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sensory evaluation study was conducted under IRB approval 2023-1195 at University of Wisconsin-Madison Meat Science and Animal Discovery building.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed in this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkwetey, W., Adzitey, F., \u0026amp; Teye, G. (2021). Cured Characteristics, Physicochemical Properties and Sensory Profile of Frankfurters Produced with Ocimum Gratissimum Extract Leaf Extracts. \u003cem\u003eFood Sci Nutr Res\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam, M. S., Mrida, M. S. H., \u0026amp; Rahman, M. A. (2025). Sentiment analysis in social media: How data science impacts public opinion knowledge integrates natural language processing (NLP) with artificial intelligence (AI). \u003cem\u003eAmerican Journal of Scholarly Research and Innovation\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(01), 63\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArsenault, D. M. (2019). RE: Celery Powder (Sunset 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsim Shahid, M., Alam, M. M., \u0026amp; Mohd Su\u0026rsquo;ud M. (2023). Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm. \u003cem\u003ePlos one\u003c/em\u003e. 18(4), e0284209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrata, A. M., Chereji, A. I., Brata, V. D., Morna, A. A., Tirpe, O. P., Popa, A., Arion, F. H., Banszki, L. I., Chereji, I., Popa, D., \u0026amp; Muresan, I. C. (2022). Consumers' Perception towards Organic Products before and after the COVID-19 Pandemic: A Case Study in Bihor County, Romania. International Journal of Environmental Research and Public Health. 19(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardoso, W., Barros, T., Gon\u0026ccedil;alves, G., \u0026amp; Premebida, C. (2024). \u0026amp; Nunes UJ Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies. In: 2024 7th Iberian Robotics Conference (ROBOT), IEEE, pp 1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChausali, N., \u0026amp; Saxena, J. (2021). Conventional versus organic farming: Nutrient status. \u003cem\u003eAdvances in Organic Farming\u003c/em\u003e (pp. 241\u0026ndash;254). Elsevier.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L., Wang, Z., Ji, L., Zhang, J., Zhao, Z., Zhang, R., Bai, T., Hou, B., \u0026amp; Wang, W. (2021). Flavor Composition and Microbial Community Structure of Mianning Ham. Frontiers in Microbiology. 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommission, E. (2013). Furan-01: Analysis of furan in baby food. In: Centre JR (Ed.). p^pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouronn\u0026eacute;, R., Probst, P., \u0026amp; Boulesteix, A-L. (2018). Random forest versus logistic regression: a large-scale benchmark experiment. \u003cem\u003eBmc Bioinformatics\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai, S., Li, K., Luo, Z., Zhao, P., Hong, B., Zhu, A., \u0026amp; Liu, J. (2024). AI-based NLP section discusses the application and effect of bag-of-words models and TF-IDF in NLP tasks. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006\u0026ndash;4023. 5(1), 13\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarwish, J. A. (2025). Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning. \u003cem\u003eAlexandria Engineering Journal\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e, 586\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavidson, P. M., Sofos, J. N., \u0026amp; Branen, A. L. (2005). \u003cem\u003eAntimicrobials in food\u003c/em\u003e. CRC.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal, K., Ravi, R., \u0026amp; Nandwani, D. (2021). Comparative study of sensory attributes of leafy green vegetables grown under organic and conventional management. \u003cem\u003eInternational Journal on Food Agriculture and Natural Resources\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), 29\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal, S., Balasubramaniam, V. M., Cocuron, J-C., Alonso, A. P., Agcam, E., \u0026amp; Kamat, S. (2017). Pressure-Thermal Kinetics of Furan Formation in Selected Fruit and Vegetable Juices. \u003cem\u003eFood and Bioprocess Technology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(11), 1959\u0026ndash;1969.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickey, R. M., Forti, A. M., \u0026amp; Kunjapur, A. M. (2021). Advances in engineering microbial biosynthesis of aromatic compounds and related compounds. \u003cem\u003eBioresources and Bioprocessing\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDjeri, N., \u0026amp; Williams, S. (2014). Celery Juice Powder Used as Nitrite Substitute in Sliced Vacuum-Packaged Turkey Bologna Stored at 4C for 10 Weeks Under Retail Display Light. \u003cem\u003eJournal of Food Quality\u003c/em\u003e. 37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaculaJr, M., Rutenbeck, S., Pollack, L., Resurreccion, A. V., \u0026amp; Moskowitz, H. R. (2007). The Just-About‐Right intensity scale: functional analyses and relation to hedonics. \u003cem\u003eJournal of Sensory Studies\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 194\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Gonz\u0026aacute;lez, D. L., Aparicio, R., \u0026amp; Aparicio-Ruiz, R. (2013). Volatile and amino acid profiling of dry cured hams from different swine breeds and processing methods. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 3927\u0026ndash;3947.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHetherington, M. (1996). Sensory-specific satiety and its importance in meal termination. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 113\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong, Y. S., Son, J. H., Jeong, J. Y., Song, O. Y., Lee, J. H., \u0026amp; Kim, K. S. (2018). Comparison of volatile flavor compounds of \u0026lt;\u0026thinsp;i\u0026gt;Artemisia annua\u0026thinsp;L. extracted by simultaneous steam distillation extraction and solid-phase micro extraction. \u003cem\u003eKorean Journal of Food Preservation\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 321\u0026ndash;329.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIndu, S., Neeraj, K., \u0026amp; Archana, R. (2024). Carotenoids: Sources, Bioavailability and Their Role in Human Nutrition. In R. Akkinapally Venketeshwer, \u0026amp; R. Leticia (Eds.), \u003cem\u003eDietary Carotenoids. p^pp Ch. 5\u003c/em\u003e. IntechOpen.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInformation NCfB (2024). PubChem Compound Summary for CID 7794, Citronellal. In. p^pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvanović, L., Milašević, I., Topalović, A., \u0026ETH;urović, D., Mugoša, B., Knežević, M., \u0026amp; Vrvić, M. (2019). Nutritional and phytochemical content of Swiss chard from Montenegro, under different fertilization and irrigation treatments. \u003cem\u003eBritish Food Journal\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(2), 411\u0026ndash;425.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin, S-K., Choi, J. S., Yang, H-S., Park, T-S., \u0026amp; Yim, D-G. (2018). Natural curing agents as nitrite alternatives and their effects on the physicochemical, microbiological properties and sensory evaluation of sausages during storage. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e146\u003c/em\u003e, 34\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, A. A. (2025). Meta-Unsupervised Learning: Application to Non-Negative Matrix Factorization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing, D. A., Hunt, M. C., Barbut, S., Claus, J. R., Cornforth, D. P., Joseph, P., Kim, Y. H. B., Lindahl, G., Mancini, R. A., \u0026amp; Nair, M. N. (2023). American Meat Science Association guidelines for meat color measurement. \u003cem\u003eMeat and Muscle Biology\u003c/em\u003e 6(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenard\u0026atilde;o, E. J., Botteselle, G. V., de Azambuja, F., Perin, G., \u0026amp; Jacob, R. G. (2007). Citronellal as key compound in organic synthesis. \u003cem\u003eTetrahedron\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(29), 6671\u0026ndash;6712.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadruga, M. S., Elmore, J. S., Dodson, A. T., \u0026amp; Mottram, D. S. (2009). Volatile flavour profile of goat meat extracted by three widely used techniques. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(3), 1081\u0026ndash;1087.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMancini, R. (2013). Meat Color. In: The Science of Meat Quality. p^pp 177\u0026ndash;198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMani, R. T., Palimar, V., Pai, M. S., Shwetha, T., \u0026amp; Krishnan, M. N. (2025). \u003cem\u003eAn evolution of forensic linguistics: From manual analysis to machine learning\u0026ndash;A narrative review\u003c/em\u003e (p. 100417). Forensic Science International.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarin, C., Vilas, D., Langdon, C., Alobid, I., L\u0026oacute;pez-Chac\u0026oacute;n, M., Haehner, A., Hummel, T., \u0026amp; Mullol, J. (2018). Olfactory dysfunction in neurodegenerative diseases. \u003cem\u003eCurrent allergy and asthma reports\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarriott, N. G., Graham, P. P., Boling, J. W., \u0026amp; Collins, W. F. (1984). Vacuum Tumbling of Dry-Cured Hams. \u003cem\u003eJournal of Animal Science\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(6), 1376\u0026ndash;1381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrin, M., \u0026amp; Ratneshwar, S. (2000). The impact of ambient scent on evaluation, attention, memory for familiar and unfamiliar brands. \u003cem\u003eJournal of Business Research\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(2), 157\u0026ndash;165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu, Y., Su, W., Mu, Y., \u0026amp; Jiang, L. (2020). Combined Application of High-Throughput Sequencing and Metabolomics Reveals Metabolically Active Microorganisms During Panxian Ham Processing. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e. 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNijs, M., Smets, T., Waelkens, E., \u0026amp; De Moor, B. (2021). A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data. \u003cem\u003eRapid Communications In Mass Spectrometry\u003c/em\u003e 35(21), e9181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu, P., Wang, F., Yuan, K., Li, X., Yang, X., \u0026amp; Guo, Y. (2021). Alkaline-extracted thinned young apple polyphenols as an effective scavenger against nitrite in pickles: A comparative study with ethanol-extracted polyphenols. \u003cem\u003eFood Control\u003c/em\u003e, \u003cem\u003e130\u003c/em\u003e, 108387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOSB (2023). National Organic Standards Board Meeting - Atlanta,GA. In. \u003cem\u003ep^pp\u003c/em\u003e 104\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunes, C. A., Ribeiro, M. N., de Carvalho, T. C., Ferreira, D. D., de Oliveira, L. L., \u0026amp; Pinheiro, A. C. (2023). Artificial intelligence in sensory and consumer studies of food products. \u003cem\u003eCurrent Opinion in Food Science\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e, 101002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOral, R. A., Dogan, M., \u0026amp; Sarioglu, K. (2014). Effects of certain polyphenols and extracts on furans and acrylamide formation in model system, and total furans during storage. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, 423\u0026ndash;429.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParrini, S., Sirtori, F., Acciaioli, A., Becciolini, V., Crovetti, A., Franci, O., Romani, A., Scardigli, A., \u0026amp; Bozzi, R. (2019). Effect of Replacement of Synthetic vs. Natural Curing Agents on Quality Characteristics of Cinta Senese Frankfurter-Type Sausage. \u003cem\u003eAnimals (Basel)\u003c/em\u003e. 10(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham, A., Schilling, M., Mikel, W., Williams, J., Martin, J., \u0026amp; Coggins, P. (2008). Relationships between sensory descriptors, consumer acceptability and volatile flavor compounds of American dry-cured ham. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(3), 728\u0026ndash;737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips, D., \u0026amp; Smit, M. (2021). Toward best practices for unstructured descriptions of research data. Proceedings of the Association for Information Science and Technology. 58(1), 303\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePietrasik, Z., Gaudette, N. J., \u0026amp; Johnston, S. P. (2016). The use of high pressure processing to enhance the quality and shelf life of reduced sodium naturally cured restructured cooked hams. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 102\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePosthuma, J. A., Rasmussen, F. D., \u0026amp; Sullivan, G. A. (2018). Effects of nitrite source, reducing compounds, and holding time on cured color development in a cured meat model system. \u003cem\u003eLWT\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e, 47\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabin, M. D., \u0026amp; Cain, W. S. (1984). Odor recognition: familiarity, identifiability, and encoding consistency. \u003cem\u003eJournal of experimental psychology Learning memory and cognition\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 316\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaschka, S., Liu, Y. H., \u0026amp; Mirjalili, V. (2022). \u003cem\u003eMachine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python\u003c/em\u003e. Packt Publishing Ltd.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasmussen, F. D., \u0026amp; Sullivan, G. A. (2019). Comparison of Traditional and Alternative Curing Ingredients on Curing Reactions in a Model Meat System.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizzo, G., Testa, R., Schifani, G., \u0026amp; Migliore, G. (2023). \u003cem\u003eThe Value of Organic plus. Analysing Consumers\u0026rsquo; Preference for Additional Ethical Attributes of Organic food Products\u003c/em\u003e. Social Indicators Research.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts, M. E., Stewart, B. M., \u0026amp; Airoldi, E. M. (2016). A Model of Text for Experimentation in the Social Sciences. \u003cem\u003eJournal of the American Statistical Association\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(515), 988\u0026ndash;1003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha, B. S., Gago, B., Barbosa, R. M., \u0026amp; Laranjinha, J. (2009a). Dietary polyphenols generate nitric oxide from nitrite in the stomach and induce smooth muscle relaxation. \u003cem\u003eToxicology\u003c/em\u003e, \u003cem\u003e265\u003c/em\u003e(1\u0026ndash;2), 41\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha, B. S., Gago, B., Barbosa, R. M., \u0026amp; Laranjinha, J. (2009b). Dietary polyphenols generate nitric oxide from nitrite in the stomach and induce smooth muscle relaxation. \u003cem\u003eToxicology\u003c/em\u003e, \u003cem\u003e265\u003c/em\u003e(1\u0026ndash;2), 41\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers, K. M. (2012). \u003cem\u003ePork: A global history\u003c/em\u003e. Reaktion Books.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRolls, B. J., \u0026amp; McDermott, T. M. (1991). Effects of age on sensory-specific satiety. \u003cem\u003eThe American Journal of Clinical Nutrition\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(6), 988\u0026ndash;996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRolls, B. J., Rolls, E. T., Rowe, E. A., \u0026amp; Sweeney, K. (1981). Sensory specific satiety in man. \u003cem\u003ePhysiology \u0026amp; behavior\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 137\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeiberling, K. A., \u0026amp; Conley, D. B. (2004). Aging and olfactory and taste function. \u003cem\u003eOtolaryngologic Clinics of North America\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(6), 1209\u0026ndash;1228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeok, Y-J., Her, J-Y., Kim, Y-G., Kim, M. Y., Jeong, S. Y., Kim, M. K., Lee, J., Kim, C., Yoon, H-J., \u0026amp; Lee, K-G. (2015). Furan in Thermally Processed Foods - A Review. \u003cem\u003eToxicological Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(3), 241\u0026ndash;253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, S., Ricke, S. C., Silva, E. M., \u0026amp; Claus, J. R. (2025a). Impact of Organic and Conventional Vegetable-Based Curing Ingredients on Frankfurter Quality and Sensory Attributes. \u003cem\u003eFood Science \u0026amp; Nutrition\u003c/em\u003e 13(4), e70148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, S., Silva, E. M., Ricke, S. C., \u0026amp; Claus, J. R. (2025b). Characterization of Volatilized Compounds in Conventional and Organic Vegetable-Source Alternative Meat-Curing Ingredients. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, S., Silva, E. M., Ricke, S. C., \u0026amp; Claus, J. R. (2025c). Consumer sensory preferences for processed meats with synthetic, conventional, organic, and deodorized curing ingredients. \u003cem\u003enpj Science of Food\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, S., Silva, E. M., Ricke, S. C., \u0026amp; Claus, J. R. (2025d). Organic and conventional alternative curing ingredients effects on quality and sensory attributes of deli-style Turkey. \u003cem\u003ePoultry Science\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e(8), 105370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, S., Silva, E. M., Tart\u0026eacute;, R., \u0026amp; Claus, J. R. (2025e). Residual nitrite and nitrate in processed meats and meat analogues in the United States. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 3269.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSi, S., Wang, J., Zhang, R., \u0026amp; Su, Q. (2022). \u0026amp; Xiao J Federated non-negative matrix factorization for short texts topic modeling with mutual information. In: 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiekmann, L., Pl\u0026ouml;tz, M., \u0026amp; Krischek, C. (2021). Alternative curing methods. \u003cem\u003eCurrent Clinical Microbiology Reports\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 40\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSindelar, J. J., Cordray, J. C., Sebranek, J. G., Love, J. A., \u0026amp; Ahn, D. U. (2007). Effects of Varying Levels of Vegetable Juice Powder and Incubation Time on Color, Residual Nitrate and Nitrite, Pigment, pH, and Trained Sensory Attributes of Ready-to-Eat Uncured Ham. \u003cem\u003eJournal of Food Science\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(6), S388\u0026ndash;S395.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠojić, B., Milošević, S., Savanović, D., Zeković, Z., Tomović, V., \u0026amp; Pavlić, B. (2023). Isolation, Bioactive Potential, and Application of Essential Oils and Terpenoid-Rich Extracts as Effective Antioxidant and Antimicrobial Agents in Meat and Meat Products. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(5), 2293.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;ltoft, M., Bysted, A., Madsen, K. H., Mark, A. B., B\u0026uuml;gel, S. G., Nielsen, J., \u0026amp; Knuthsen, P. (2011). Effects of organic and conventional growth systems on the content of carotenoids in carrot roots, and on intake and plasma status of carotenoids in humans. \u003cem\u003eJournal of the Science of Food and Agriculture\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(4), 767\u0026ndash;775.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan, G. A., Jackson-Davis, A. L., Schrader, K. D., Xi, Y., Kulchaiyawat, C., Sebranek, J. G., \u0026amp; Dickson, J. S. (2012). Survey of naturally and conventionally cured commercial frankfurters, ham, and bacon for physio-chemical characteristics that affect bacterial growth. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(4), 808\u0026ndash;815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTatiyaborworntham, N., Oz, F., Richards, M. P., \u0026amp; Wu, H. (2022). Paradoxical effects of lipolysis on the lipid oxidation in meat and meat products. \u003cem\u003eFood Chemistry: X\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 100317.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian, Z., Zhu, Q., Chen, Y., Zhou, Y., Hu, K., Li, H., Lu, K., Zhou, J., Liu, Y., \u0026amp; Chen, X. (2022). Studies on Flavor Compounds and Free Amino Acid Dynamic Characteristics of Fermented Pork Loin Ham with a Complex Starter. \u003cem\u003eFoods\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(10), 1501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomasević, I., Tomović, V., Barba, F. J., Vasilev, D., Jokanović, M., Sojić, B., Manuel Lorenzo, J., \u0026amp; Đekić, I. (2019). How the color of game meat should be measured. \u003cem\u003eFleischwirtschaft\u003c/em\u003e, \u003cem\u003e99\u003c/em\u003e(1), 85\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA. (1996). \u003cem\u003eInstitutional Meat Purchase Specifications\u003c/em\u003e. Fresh Beef Series 100\u0026ndash;PD F version.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA. (2019). \u003cem\u003eIn. p^pp, Agricultural Research Service\u003c/em\u003e. Beltsville Human Nutrition Research Center.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e7 CFR 205.606. In. USDA, \u0026amp; p^pp (2024a). U.S.A.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSDA (2024b). 9 CFR 319.104. In. p^pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatkins, P., Rose, G., Warner, R., Dunshea, F., \u0026amp; Pethick, D. (2012). A comparison of solid-phase microextraction (SPME) with simultaneous distillation\u0026ndash;extraction (SDE) for the analysis of volatile compounds in heated beef and sheep fats. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(2), 99\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaxman, O. B. (2017). \u003cem\u003eSpam Is Turning 80. Here\u0026rsquo;s How the Canned Meat Took Over the World\u003c/em\u003e. In: Time. p^pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWichchukit, S., \u0026amp; O'Mahony, M. (2015). The 9-point hedonic scale and hedonic ranking in food science: some reappraisals and alternatives. \u003cem\u003eJournal of the Science of Food and Agriculture\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(11), 2167\u0026ndash;2178.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi, Y., Sullivan, G., Jackson, A., Zhou, G., \u0026amp; Sebranek, J. (2012). Effects of natural antimicrobials on inhibition of Listeria monocytogenes and on chemical, physical and sensory attributes of naturally-cured frankfurters. \u003cem\u003eMeat Science\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(1), 130\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, Y., He, Z., Zhang, E., \u0026amp; Li, H. (2016). Characterization of key volatile odorants in rabbit meat using gas chromatography mass spectrometry with simultaneous distillation extraction. \u003cem\u003eWorld Rabbit Science\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(4), 313\u0026ndash;320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, X., Guo, L., Jiang, G., Song, Y., \u0026amp; Muminov, M. A. (2018). Advances of organic products over conventional productions with respect to nutritional quality and food security. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 53\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Boneless ham, Organic Agriculture, Alternative Curing, GC-MS/MS, Natural Language Processing, Artificial Intelligence, Consumer Sensory, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8905083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8905083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnited States regulators and the meat industry have recently called for a shift from conventional natural curing ingredients to organic alternatives. However, the curing efficacy and consumer acceptance of these organic options remain unclear. Fully cooked, water-added boneless ham served as the model for assessing these ingredients in this study. The study evaluated the effects of commercial conventional and organic plant-sourced curing agents on ham quality using sensory evaluation, instrumental analysis, machine learning, and natural language processing (NLP). Five treatments were analyzed: pre-converted celery (CEL), organic celery (OCEL), Swiss chard (SW), organic Swiss chard (OSW), and sodium nitrite (SN). Consumer panels indicated no differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in overall liking or purchase intent across treatments. However, OSW exhibited a greater non-meat aftertaste compared to SW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and SN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Traditional statistical methods such as principal component analysis (PCA) and correlation studies revealed that non-meat aftertaste was positively correlated (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61) with terpenoids and negatively correlated (\u003cem\u003er\u003c/em\u003e = -0.53) with esters, furans, and sulfur-containing compounds. Novel artificial intelligence tools such as machine learning classification of objective color measurements identified the 650/570 nm absorbance ratio as a key differentiating feature across treatments. Furthermore, NLP analysis of open-ended comments identified structured themes, where 'aftertaste/off note' language showed a significant association with lower overall liking. These findings demonstrate the value of integrating advanced data analytics with traditional meat science methodology.\u003c/p\u003e","manuscriptTitle":"Comprehensive Quality Analysis of an Organic Alternative Curing Process in Boneless Ham Using Consumer Sensory Evaluation and Artificial Intelligence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 04:25:00","doi":"10.21203/rs.3.rs-8905083/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b36d2444-ebf9-4c8e-b913-76c7a6c0033e","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T04:34:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 04:25:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8905083","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8905083","identity":"rs-8905083","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}