Optimization a method based on headspace-gas chromatography-ion mobility spectrometry for the rapid and visual analyzation of flavor compounds interaction in Baijiu

Optimization a method based on headspace-gas chromatography-ion mobility spectrometry for the rapid and visual analyzation of flavor compounds interaction in Baijiu | 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 Optimization a method based on headspace-gas chromatography-ion mobility spectrometry for the rapid and visual analyzation of flavor compounds interaction in Baijiu Guangnan Wang, Feifei Liu, Huan Cheng, Fuping Zheng, Xingqian Ye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890358/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 The flavor of Baijiu is not only determined by the individual flavors of the compounds but also by the combination of them. The exploration of the interaction effects between flavor compounds remains in the nascent stages. In this study, a method based on headspace-gas chromatography-ion mobility spectrometry has been proposed to swiftly elucidate the interactions among flavor compounds in Baijiu. To optimize the critical factors influencing headspace generation, namely, incubation time, sample quantity, injection volume, and alcohol content, we employ a Box-Behnken design integrated with Response Surface Methodology. Model solutions were created with 13 common flavor compounds typically found in Baijiu and varying concentrations of lactic acid, allowing researchers to evaluate the intensity of the interaction between lactic acid and these compounds by comparing the concentration of flavor compounds in the presence of different levels of lactic acid. The findings revealed that lactic acid had a strong correlation with the majority of the flavor compounds, and the disparities among the model solutions with flavor compounds augmented as the lactic acid content rose. This research presents a novel analytical approach, offering rapid insights into the correlation between flavor components in Baijiu and other complex food matrices. GC-IMS Baijiu flavor interaction effect visual analysis Response Surface Methodology Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Baijiu is a widely enjoyed alcoholic beverage appreciated for its distinct flavor. According to the National Bureau of Statistics, the annual sales of Baijiu in China, where it originated, reached 6.71 million kiloliters in 2022, with a value of 95.14 billion US dollars. (data from the National Bureau of Statistics). The traditional craftsmanship behind Baijiu involves a meticulous process, including the manufacturing of jiuqu , fermentation, and distillation under solid-state, aging in pottery jar, and blending. Due to the various sources of raw materials, fermentation techniques, and microorganisms, the composition of Baijiu is always highly varied. Between 1990 and 2017, researchers identified more than 1874 different volatile compounds in Baijiu. Currently, twelve flavors are commonly recognized by customers. Among them, Strong , Light , Soysauce , and Rice are considered as the four fundamental flavor types, while the additional flavors, i.e ., Feng , Te , Sesame , Laobaigan , Fuyu , Herbal , Chi , and Jian , are regarded as the derived types (Liu & Sun, 2018 ; Wang et al., 2021 ). The specific flavor of Baijiu is determined by the categories and amounts of flavor compounds (Wang et al., 2022 a). Despite the thousands of volatile flavor compounds that have been identified and reported in Baijiu, our overall understanding of its flavor remains incomplete as earlier studies mainly concentrated on the types, contents, and characteristics of volatile flavor compounds, without considering the important role of non-volatile flavor compounds (Dong et al., 2019 ; Wang et al., 2022 b; L. Zhu et al., 2020 ). Although non-volatile compounds do not directly impact the aroma, their interactions with volatiles still have a significant influence on the flavor and quality of Baijiu (Jia & Ma, 2023 ; Niu et al., 2020 ; Sáenz-Navajas et al., 2010 ). Numerous studies have uncovered the vital part that the interactions between these compounds play. Stephanie et al. established that the non-volatiles had a major influence on the strength of some aroma qualities of Dornfelder red wine since the flavor of the recombinant model mixture was enhanced when non-volatile flavor compounds were added, which was more in line with the flavor of the original red wine (Frank et al., 2011 ). It has been established that non-volatile organic acids play an essential role in the flavor of Baijiu. The flavor of recombination B more closely resembled the original Baijiu than recombination A, as it contained the same 33 odor-active compounds, plus an additional 37 non-volatile organic acids. The incorporation of non-volatile organic acids brought down the odors of alcohol and sweetness, while at the same time intensifying odors related to fruit, acid, flowers, jujube, and grain (Wang et al., 2022 c). Furthermore, Wang et al. discovered that the addition of lactic acid led to a significant decrease in the thresholds of ethyl lactate and ethyl acetate, indicating that lactic acid has either an additive or synergistic effect on the release of these two esters (Wang et al., 2022 d). However, the mechanism of interaction between non-volatile and volatile flavor components in Baijiu has not been sufficiently revealed. This scientific ambiguity has largely prevented us from fully grasping the flavor of Baijiu. Consequently, the interaction between non-volatile and volatile compounds in Baijiu should be further examined. Previous reports suggest that nonvolatile components in the matrix can alter the gas-liquid distribution of volatile compounds in the headspace, thus influencing the body flavor of the wine (Robinson et al., 2009 ). Zhang et al. employed HS-SPME-GC-MS to investigate the interaction between lactic acid and flavor compounds in Baijiu. And the results indicated that lactic acid can raise the volatility of short-chain branched esters while diminishing the volatility of most aromatic compounds (Zhang et al., 2022 ). For food flavor analysis, GC-MS is the most advanced and mature technique (Jia et al., 2020 ). However, analyzing Baijiu necessitates a complex, time-consuming GC-MS process with intricate pre-treatment (Louw, 2021 ). The alteration of the headspace concentration directly mirrors the alteration in the volatility of the flavor compounds. Nevertheless, with HS-SPME analysis, the aroma compounds are not only shared between liquid and gas (air) but also between gas and solid (SPME fibers), which might affect the analysis results (Ferracane et al., 2022 ). Recently, headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), a technology utilizing the drift times of ions to identify volatiles, has been developed as a way to provide fast and effective detection. This technology is also being employed to accurately monitor the flavor compounds of foods. HS-GC-IMS is fitted with an automated HS injector, which is capable of analyzing volatile compounds from both liquid and solid samples without any prior preparation (Zhang et al., 2022 ). Benefiting from the contrasting mobility of gas phase ions in an unchanging electric field, IMS is an advantageous analytical technique with a brief incubation period, accelerated analytical time, and heightened sensitivity (He et al., 2022 ; Sun et al., 2023 ). Besides, compared to GC-MS, GC-IMS offers a lower detection limit, remarkable sensitivity, and ease of use, making it a great choice for detecting flavor compounds at room temperature and ambient pressure (Song et al., 2023 ; Zhu et al., 2021 ). Meanwhile, GC-IMS is able to provide a color contours image to illustrate the analysis results, helping to clearly differentiate between samples (Yao et al., 2022 ). GC-IMS is currently employed for the identification of food safety, origin, tracking process, and authenticity (Calle et al., 2023 ; Tian et al., 2019 ; Zhao et al., 2021 ). So far, there is no research that has employed GC-IMS to investigate the interactions between various flavor components in Baijiu. Thus, the objectives of this study were to (1) employ Box Behnken design and Response Surface Methodology to develop and refine an analytical method based on HS-GC-IMS, aiming to swiftly identify the interaction between flavor compounds in Baijiu; (2) use the optimized method to explore the influence of lactic acid on the release of 13 commonly flavor compounds in Baijiu. Chemometric techniques, such as PCA, heatmap, HCA, and correlation analysis, were employed to measure the intensity of the interaction effect. 2. Materials and methods 2.1 Reagents and samples All the standards employed in this study had a minimum purity of 95%, sourced from Sigma-Aldrich Co. Ltd. (Shanghai, China) and J&K Co. Ltd. (Shanghai, China). Experiments in this study were conducted using model solutions of “flavor compounds-aqueous ethanol”. The concentration of flavor compounds used in the experiment was the average of the concentrations reported in Baijiu (Table 1 ). The detailed concentrations of each compound in Baijiu are shown in Table S1 . To create the standard stock solutions, the standard flavor compounds were dissolved in ethanol. The model solutions were created by mixing the standard stock solutions and then diluting them in steps until the desired concentrations were reached. Lactic acid widely exists in different flavor types of Baijiu, and the highest amount present has not surpassed 3000 mg/L. Thus, in order to gain a better understanding of the interaction between lactic acid and other flavor compounds, this study varied the amount of lactic acid from 200 mg/L, 400 mg/L, 800 mg/L, 1600 mg/L to 3200 mg/L. Besides, the model solution of this experiment is labeled CG (Control group, with no lactic acid added), A (100 mg/L of lactic acid), B (200 mg/L of lactic acid), C (400 mg/L of lactic acid), D (800 mg/L of lactic acid), E (1600 mg/L of lactic acid) and F (3200 mg/L of lactic acid) according to the amount of lactic acid added. Table 1 Aroma description, inter-day precision, intra-day precision, and experiment concentration of 13 flavor compounds. Compounds Aroma description Inter-day precision (%) Intra-day precision (%) Concentration (mg/L) Ethyl lactate Fruity 1.86 0.12 1262.11 Dimethyl trisulfide a Sauerkraut-like, sulfur, cabbage 10.14 7.37 802.56 3-Hydroxy-2-butanone Sweet, creamy, buttery 7.17 2.98 26.00 Ethyl hexanoate Fruity 1.2 0.07 1037.02 3-Methylbutanal a Green, malty 2.92 1.17 320.00 2-Methoxy-4-methylphenol a Woody, smoky 8.84 3.35 1011.83 Ethyl pentanoate Fruity, apple 1.17 0.99 20.41 Hexanoic acid Sweaty, cheesy 1.96 1.04 230.61 2-Methyl-1-propanol Fruity, malty 0.55 0.61 168.38 Ethyl acetate Fruity, pineapple 1.14 0.99 1478.40 3-Methyl-1-butanol Malty 2.68 1.37 405.33 Ethyl butanoate Fruity, pineapple 2.06 1.14 101.03 Propanol Alcoholic 3.71 2.46 418.10 a , The concentration was µg/L. Aroma description and concentration of each compound were reported in (Fan et al., 2015 ; Fan et al., 2019 ; Gao et al., 2014 ; Li et al., 2019 ; Wang et al., 2021 ; Zhao et al., 2018 ). 2.2 Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) The GC-IMS analysis was performed on an HS-GC-IMS Flavor Spec (G.A.S., Dortmund, Germany). The model solutions enclosed in vials were immediately incubated in the auto-sampler oven. After that, an automatic injection of 500 µL of the sample headspace was conducted at a flow rate of 2 mL/min at 85 o C. A new MXT-WAX column (30 meters, 0.53 mm ID, 1 µm df) was employed to achieve chromatographic separation, with high-purity nitrogen (99.999%) as the carrier gas and heated at 60 o C to ensure timely separation. All tests employed n -ketones C 4 -C 9 (provided by Sinopharm Chemical Reagent Co., Ltd, China) as an external reference for the determination of the retention index (RI) of the flavor compound. Flavor compounds are initially identified by matching their retention index (RI) and drift time with the NIST Library and IMS database retrieval software from G.A.S. Subsequently, all flavor compounds are further confirmed by analyzing them under the same conditions with their standards. 2.3 Box-Behnken Design-Response Surface Methodology Research has demonstrated that specific independent variables exhibit a direct correlation with the signal of the IMS spectra, particularly those factors that influence the concentrations of volatile components in the headspace injected into the GC-IMS system. The variables to be considered include the sample volume, the injection volume, and the incubation time. Furthermore, when studying alcohol samples, the alcohol content is a major factor to consider. Therefore, in order to optimize the process, four variables were chosen: the sample volume, the incubation time, the injection volume, and the alcohol content. Three levels (low, middle, and high points) were allocated to each variable (Table S2). To acquire the greatest peak intensity, a Box-Behnken design in conjunction with Response Surface Methodology was utilized to optimize the analysis conditions (Putri et al., 2020 ). The design was formulated from 46 experiments, all of which were conducted in a random sequence (Table 2 ). The aim of the optimization was to maximize the response value of the target compounds, so as to guarantee the most desirable efficiency when detecting flavor compounds in Baijiu. Design Expert software 8.0.6 was used to analyze all the experimental data, and the model analysis was conducted with a 95% confidence interval ( p < 0.05). The estimated ridge of the best response was determined when the results demonstrated a saddle point in the response surface. Table 2 Experimental design of BBD matrix with the corresponding factors. Std Run incubation time (min) Injection volume (mL) Sample volume (mL:) Alcohol content (%) Sum intensities 5 7 25 0.5 0.2 5 144341 13 6 25 0.2 0.2 12.5 145776 1 12 10 0.2 0.5 12.5 146600 21 9 25 0.2 0.5 5 152162 2 8 40 0.2 0.5 12.5 152430 15 20 25 0.2 0.8 12.5 155275 9 26 10 0.5 0.5 5 157204 23 15 25 0.2 0.5 20 159691 10 13 40 0.5 0.5 5 160299 6 5 25 0.5 0.8 5 163515 17 1 10 0.5 0.2 12.5 167697 18 3 40 0.5 0.2 12.5 170543 22 29 25 0.8 0.5 5 178334 7 28 25 0.5 0.2 20 182261 19 24 10 0.5 0.8 12.5 184145 20 14 40 0.5 0.8 12.5 187597 11 2 10 0.5 0.5 20 189060 14 21 25 0.8 0.2 12.5 189788 8 16 25 0.5 0.8 20 194712 12 22 40 0.5 0.5 20 194928 3 17 10 0.8 0.5 12.5 195115 4 19 40 0.8 0.5 12.5 203266 28 4 25 0.5 0.5 12.5 208411 16 25 25 0.8 0.8 12.5 209218 24 23 25 0.8 0.5 20 210785 27 11 25 0.5 0.5 12.5 211719 25 18 25 0.5 0.5 12.5 213464 29 10 25 0.5 0.5 12.5 214149 26 27 25 0.5 0.5 12.5 215245 2.4 Statistical analysis The instrumental analysis software LAV (version 2.0.0 from G.A.S., Dortmund, Germany), Reporter, Gallery Plot, and GC × IMS Library Search were employed to analyze the IMS data, allowing for sample analysis from various perspectives. A design Expert was employed to devise the BBD and to ascertain the most suitable analysis conditions via RSM analysis. Unsupervised analyses such as PCA, heatmap, and HCA were used to illustrate the distinctions and connections among different model solutions. IBM SPSS Statistics 24 software (IBM Inc., Armonk, NY, USA) was utilized to execute one-way ANOVA and Duncan’s multiple range test. The data was considered to be significantly different when p < 0.05. The Spearman correlation test was used to examine the correlation between lactic acid addition and the volatilization of flavor compounds. 3. Results and discussion 3.1 volatile profiles of GC-IMS analysis A 3-dimensional map is used to present GC-IMS detection results, including retention time, drift time, and intensity. A topographic plot is shown in Fig. 1 a, with a bright vertical line on the left representing the normalized reaction ion peak (RIP). Every point to the right of the RIP stands for one or more signal peaks, and the deeper the red, the more powerful the peak. The figure clearly displays the 13 volatile compounds in the model solution, and the other points may be interference from the alcohol solutions or impurities. 3.2 Optimization of the method This experiment was conducted to optimize an analytical method capable of detecting and measuring the flavor compounds in Baijiu. The optimization of the four main variables (sample volume, incubation time, injection volume, and alcohol content) was the initial step, which was done by means of BBD-RSM. (Zhang et al., 2022 ). Table 2 displays the experimental conditions along with the sum of the peak intensities of the target compounds. The sum of peak intensities, as seen in Table 2 , ranged from 144341 to 215245, indicating that the optimized experimental conditions had a considerable effect on the detection of flavor compounds in Baijiu. Fisher’s F-test was employed to assess the model, and the details of the result can be found in Table S3. The coefficient of variation (CV) being below 10% and the “Adeq Precision” being over 4 indicate the reliability of the model. In addition, the predicted R 2 (0.8925) and adjusted R 2 (0.9604) differed by less than 0.2, demonstrating the model’s high accuracy. Lastly, the p -value was less than 0.0001 and the lack of fit was not significant ( p > 0.05), demonstrating that the model was effective in elucidating the relationship between independent variables and dependent variable. The discriminant values obtained from the experiment were then adjusted to the values predicted by the polynomial function model (Eq. (1)). y = \({\beta }_{0}+\sum _{i=1}^{k}{\beta }_{i}{x}_{i}+\sum _{i=1}^{k}{\beta }_{ii}{x}_{i}^{2}+\sum _{i=1}^{k}\sum _{j=1,j\ne i}^{k}{\beta }_{ij}{x}_{i}{x}_{j}+\epsilon\) (1) In this equation, y is the predicted response, i.e ., the sum of the peak intensities of the target compounds. \({\beta }_{0}\) represents the ordinate at the origin, \({x}_{i}\) are the variables that influence the response. \({\beta }_{i}\) represents the linear coefficients, \({\beta }_{ii}\) are the quadratic coefficients, and \({\beta }_{ij}\) are the cross-product coefficients. The significance of the coefficients of the parameters in the quadratic equation is indicated by the p -values in Table 3 . This optimization experiment revealed that injection volume, sample volume, and alcohol content were all significant variables ( p < 0.0001). Additionally, the interaction between injection volume and alcohol content was also significant ( p = 0.0173). The regression coefficient ( β ) of the injection volume was 22881.04, this positive value indicates a positive effect, which implies that an increase in the factor leads to an increase in the differences between the samples. On the contrary, a negative value of the regression coefficient of the sample volume \(\times\) sample volume ( β = -19221.29) and alcohol content × alcohol content ( β = -20168.2) indicate a negative effect, and the lower the value of these factors, the greater the differences between the samples. Table 3 Analysis of variance of the quadratic model adjusted for the discrimination of flavor compounds in model solutions. Source Coefficient Sum of Squares Degrees of freedom Mean Square F-value p-value β 0 213000 Model 1.58E + 10 14 1.13E + 09 52.91 < 0.0001 significant A-Time 2436.78 7.13E + 07 1 7.13E + 07 3.34 0.089 B-Injection volume 22881.04 6.28E + 09 1 6.28E + 09 294.51 < 0.0001 C-Sample volume 7837.86 7.37E + 08 1 7.37E + 08 34.56 < 0.0001 D-Alcohol content 14631.85 2.57E + 09 1 2.57E + 09 120.44 < 0.0001 AB 579.93 1.35E + 06 1 1.35E + 06 0.0631 0.8054 AC 151.44 9.17E + 04 1 9.17E + 04 0.0043 0.9486 AD 693.33 1.92E + 06 1 1.92E + 06 0.0901 0.7684 BC 2482.91 2.47E + 07 1 2.47E + 07 1.16 0.3005 BD 6230.34 1.55E + 08 1 1.55E + 08 7.28 0.0173 CD -1680.6 1.13E + 07 1 1.13E + 07 0.5296 0.4788 A² -17469.1 1.98E + 09 1 1.98E + 09 92.79 < 0.0001 B² -18774.63 2.29E + 09 1 2.29E + 09 107.18 < 0.0001 C² -19221.29 2.40E + 09 1 2.40E + 09 112.34 < 0.0001 D² 0 2.64E + 09 1 2.64E + 09 123.69 < 0.0001 Residual 2.99E + 08 14 2.13E + 07 Lack of Fit 2.70E + 08 10 2.70E + 07 3.8 0.1052 not significant Pure Error 2.85E + 07 4 7.12E + 06 Cor Total 1.61E + 10 28 By using 3-D response surface plots and 2-D contour plots, as seen in Fig. 2 a & b, it is possible to determine the sum of the peak intensities of the target compounds. The results of this analysis will show if the fitted surface is a maximum, a minimum, or a saddle point. Finally, the optimal experimental settings were determined by the signature model and 3D contour, which include the sample volume of 0.57 mL, the incubation time of 26 minutes, the injection volume of 0.71 mL, and the alcohol content of 16%. 3.2 Precisions of the method After the best possible conditions had been established, the accuracy of the new method was to be calculated. To determine intra-day precision, each compound was analyzed at 10 mg/L concentration three times in one day. Similarly, the inter-day precision was determined by repeating the same intra-day precision analysis on three different days. This method exhibits great accuracy, with RSD values for intra-day precision being no higher than 7.37%, and those for inter-day precision not exceeding 10.14% (Table 1 ). 3.3 Effect of lactic acid on the volatilities of aroma compounds in Baijiu As a complex food matrix, Baijiu is composed of a variety of flavor compounds, and the interaction between these compounds has an important contribution to the flavor of Baijiu. Previous studies have shown that the addition of non-volatile organic acids changes the overall flavor of Baijiu. In addition, after the inclusion of different concentrations of lactic acid, ethyl acetate and ethyl lactate experienced considerable shifts in their perceived thresholds. Therefore, in this study, 13 volatile flavor compounds commonly found in Baijiu were chosen and dissolved in an aqueous ethanol solution to construct model solutions. Then, to explore the interaction between lactic acid and volatile flavor compounds, five different concentrations of lactic acid were added to the model solution. And HS-GC-IMS was employed to differentiate between the various model solutions, as GC-IMS is renowned for its accuracy, sensitivity, and stability in detecting modifications in compound concentrations. In addition, by combining with the LAV software (G.A.S., Dortmund, Germany), the contrasts between different model solutions can be more easily perceived (Fig. 1 ). The data was initially depicted with a 3D topographical visualization, where the Y-axis, X-axis, and Z-axis symbolized the retention time of the gas chromatograph, ion migration time, and peak height, respectively. The color of the flavor substance is an indicator of its signal strength, with white showing a weaker signal and red showing a stronger one. It is perceptible that the detection concentration of the 13 volatile compounds shifted as the lactic acid amount augmented (Fig. 1 a). Given the roughness of the 3D spectrum, a differential comparisons mode could be employed (Fig. 1 b). The control group, which did not add lactic acid, has a blue background, with a red vertical line at 1.0 on the horizontal coordinate representing the reactant ion peak (RIP), and each point on the right-hand side of the RIP symbolize a compound. The spectral diagram of the model solution without lactic acid was taken as the reference, and the spectral diagram of the other samples were then subtracted from it. Spots of varying colors indicate the intensity of the peak concentrations; red spots signify a higher concentration than the reference, blue spots signify a lower concentration, and white shows that the concentrations are the same. The differential comparisons spectrum elucidates the need for a more rigorous statistical analysis by clearly demonstrating the disparities between different model solutions. The topographic plots visually demonstrated the fluctuation of volatile compounds. Nevertheless, it was still challenging to make a precise evaluation of the components on the map. The Gallery Plot of the LAV software program was a great asset in resolving this issue, as it was used for fingerprint analysis. In the fingerprint, each horizontal line displays multiple volatile compounds from a single sample, while each vertical line shows the variation of a particular volatile compound across various samples. Additionally, the color of the cell represents the amount of the volatile compound, with brighter colors indicating higher concentrations. By using fingerprint, the alterations of volatile compounds in different samples were monitored. The strength of the interaction between lactic acid and these thirteen aroma compounds can be measured by comparing the detected concentrations of these volatile compounds in the various model solutions (Fig. 1 c). The addition of lactic acid causes a notable increase in the release of flavor compounds such as dimethyl trisulfide, ethyl lactate, 3-hydroxy-2-butanone, 2-methoxy-4-methylphenol and 1-propanol in the model solution. 3.3.1 Principal component analysis and hierarchical cluster analysis To evaluate the effect of lactic acid on the flavor compounds released in Baijiu, PCA was performed and the data underwent preprocessing before the analysis. The logarithmic transformation was utilized to transform the concentration of all flavor compounds, thus preserving the effect of small values in the data (Xie et al., 2023 ). This conversion lessened the standard deviation of the variables, thereby providing more accurate analysis results. The PCA score plot was illustrated in Fig. 3 a. The first principal component (PC1) and second principal component (PC2) together accounted for 81.5% of the total variance, with PC1 making up 56.1% of the variance and PC2 accounting for 25.4%. Through the PCA plot, it is evident that the samples which are nearer to each other are more similar, whereas those which are farther apart indicate greater dissimilarity. The analysis revealed that the distance between the seven model solutions increased when lactic acid was added, which demonstrating that lactic acid was the cause of the variation in the samples. It is evident that lactic acid plays a key role in the release of flavor compounds in Baijiu. 3.3.2 Heatmap, hierarchical cluster analysis Unsupervised pattern recognition methods enable an initial assessment of the information held within a data matrix. Combining hierarchical cluster analysis (HCA), an unsupervised pattern recognition method, with a heatmap can help to better visualize the similarities and differences between different samples (Meng et al., 2024 ). As depicted in Fig. 3 b, a gradient of light to dark colors was used to represent the change in intensity from low to high. Samples that were grouped in the same category showed a strong correlation, with the shorter Euclidean distance indicating a higher level of similarity. The HCA clearly classified the model samples into two groups, the first composed of CG, A and B, and the second of C, D, E and F. The HCA results corroborated the PCA findings, indicating that model solutions with similar amounts of lactic acid added tend to group together. This further implies that the addition of lactic acid has a considerable impact on the release of flavor compounds in the model solution. Heat maps provide an alternative view compared to HCA, and can be used to divide flavor compounds into two clusters. Cluster 1 consists of caproic acid, 1-propanol, ethyl acetate, 2-methoxy-4-methylphenol, 3-hydroxy-2-butanone, dimethyl trisulfide, and ethyl lactate, which show relatively reduced volatility in model solutions containing low levels of lactic acid. Cluster 2 encompasses 6 flavor compounds, such as 3-methyl-1-butanol, ethyl hexanoate, ethyl pentanoate, 3-methylbutanal, ethyl butanoate and 2-methyl-1-propanol, which have a greater release in solutions with low lactic acid addition. This is likely due to the hydrogen bonding force between lactic acid and flavor compounds, as most of the compounds in this cluster contain hydroxyl groups. 3.3.3 Correlation analysis Both the PCA and HCA analysis results demonstrate that the incorporation of lactic acid is strongly associated with the liberation of flavor compounds. To ascertain the correlation between lactic acid and the release of flavor compounds, Spearman correlation test was used to evaluate the relationship between model solutions with varying lactic acid levels and the concentration of aroma compounds released (Canalejo et al., 2024 ; Gugino et al., 2024 ; Li et al., 2020 ; Yu et al., 2024 ). The correlation analysis revealed that a positive correlation exists between the addition of lactic acid in the model solution and 2-methoxy-4-methylphenol, ethyl lactate, 3-hydroxy-2-butanone, 1-propanol, hexanoic acid and dimethyl trisulfide, with correlation coefficients of 0.96, 0.95, 0.95, 0.85, 0.87 and 0.65 respectively, and p ≤ 0.01 or 0.001 (Fig. 4 ). The results of the positive correlation between lactic acid and ethyl lactate were in line with the findings of the previous study. This may be attributed to the special steric effects between the inter-molecules of the short-chain branched esters, which give them lower boiling points and higher saturated vapor pressures. Consequently, the volatilities of short-chain branched esters were more affected by lactic acids (Burdock, 2019 , p. 2). As lactic acid is more acidic, it causes the dissociation equilibrium of flavor compounds containing carboxyl groups to shift left, resulting in an increase in their molecular concentration. This, in turn, affects the gas-liquid balance of these flavor compounds and increases their volatility and headspace concentration (Zhang et al., 2022 ). A significant negative correlation was observed between the addition of lactic acid and both ethyl hexanoate and ethyl pentanoate, with r = -0.79 and − 0.74, respectively, and p ≤ 0.001. Previous studies have revealed that the incorporation of non-volatile organic acids can enhance the fruity aroma of Laobaigan Baijiu. Ethyl lactate, ethyl hexanoate and ethyl pentanoate all possess a fruity aroma, however, their interaction with lactic acid varies, thus necessitating further research to investigate the interaction mechanism of these compounds (Wang et al., 2022 c). Interestingly, this study revealed that the flavor compounds that had a significant positive correlation with the amount of lactic acid added contained hydroxyl groups, whereas the ones that had a significant negative correlation did not. Previous studies have demonstrated that the van der Waals force and hydrogen bonding force are the primary forces responsible for the interaction between lactic acid and other flavor compounds (Zhang et al., 2022 ). Therefore, we hypothesize that the presence of hydroxyl groups could be a factor in this significant correlation, but further investigation is required to confirm this assumption. Despite the growing evidence of the interactions between flavor compounds in Baijiu, the principles of their production and the connection between the different compounds remain a mystery. To the best of our knowledge, this is the first time that the link between lactic acid and certain crucial flavor compounds in Baijiu has been described. 4. Conclusion In this study, we demonstrate that GC-IMS is a suitable method for swiftly, accurately, and completely observing the interplay between flavor compounds in Baijiu. Firstly, this study utilizes a Box Behnken design in combination with Response Surface Methodology to optimize an analytical method for quickly detecting flavor compounds in Baijiu based on GC-IMS. Subsequently, based on the optimized detection method, the effect of different concentrations of lactic acid on the release of 13 flavor compounds was investigated. Results revealed that the addition of lactic acid altered the release of most of the flavor compounds, and the disparity between the model solutions grew as the amount of lactic acid added increased. Finally, to clarify the effect of lactic acid addition on the release of flavor compounds, the correlation analysis of “lactic acid addition amount - flavor compound release”, revealed that the addition of lactic acid was positively correlated with 2-methoxy-4-methylphenol, ethyl lactate, 3-hydroxy-2-butanone, 1-propanol, hexanoic acid and dimethyl trisulfide, and was significantly negatively correlated with ethyl hexanoate and ethyl pentanoate. This research not only provides a fast and visual way to assess the interaction between flavor compounds in Baijiu but also gives a fresh perspective and ideas to the analysis of Baijiu flavor, leading to a more comprehensive understanding of the flavor of Baijiu or other complex food matrices. Declarations Acknowledgements This work was financially supported by the National Key R&D Program of China (Grant No. 2022YFD2100803). Credit authorship contribution statement Guangnan Wang: Conceptualization, Writing - Original Draft; Feifei Liu: Investigation, Methodology; Huan Cheng: Resources, Supervision; Fuping Zheng: Conceptualization, Writing - Review & Editing; Xingqian Ye: Conceptualization, Writing - Review & Editing; Baoguo Sun: Resources, Supervision Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript. References Burdock GA (2019) Fenaroli’s Handbook of Flavor Ingredients: Volume 2. CRC Press Calle JLP, Vázquez-Espinosa M, Barea-Sepúlveda M, Ruiz-Rodríguez A, Ferreiro-González M, Palma M (2023) Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices. Foods 12(13) Article 13. https://doi.org/10.3390/foods12132536 Canalejo D, Martínez-Lapuente L, Ayestarán B, Pérez-Magariño S, Guadalupe Z (2024) Potential use of grape and wine polysaccharide extracts as fining agents to modulate the volatile composition of Viura wines. Food Chem 430:137047. https://doi.org/10.1016/j.foodchem.2023.137047 Dong W, Guo R, Liu M, Shen C, Sun X, Zhao M, Sun J, Li H, Zheng F, Huang M, Wu J (2019) Characterization of key odorants causing the roasted and mud-like aromas in strong-aroma types of base Baijiu. Food Res Int 125:108546. https://doi.org/10.1016/j.foodres.2019.108546 Fan H, Fan W, Xu Y (2015) Characterization of Key Odorants in Chinese Chixiang Aroma-Type Liquor by Gas Chromatography-Olfactometry, Quantitative Measurements, Aroma Recombination, and Omission Studies. J Agric Food Chem 63(14):3660–3668. https://doi.org/10.1021/jf506238f Fan Q, Wang X, Zhao Y, Zheng F, Li H, Zhang F, Zhang Y, Chen F (2019) Characterization of key aroma compounds in Laobaigan Chinese Baijiu by GC×GC-TOF/MS and means of molecular sensory science. Flavour Fragr J 34(6):514–525. https://doi.org/10.1002/ffj.3533 Ferracane A, Manousi N, Tranchida PQ, Zachariadis GA, Mondello L, Rosenberg E (2022) Exploring the volatile profile of whiskey samples using solid-phase microextraction Arrow and comprehensive two-dimensional gas chromatography-mass spectrometry. J Chromatogr A 1676. https://doi.org/10.1016/j.chroma.2022.463241 . Scopus Frank S, Wollmann N, Schieberle P, Hofmann T (2011) Reconstitution of the Flavor Signature of Dornfelder Red Wine on the Basis of the Natural Concentrations of Its Key Aroma and Taste Compounds. J Agric Food Chem 59(16):8866–8874. https://doi.org/10.1021/jf202169h Gao W, Fan W, Xu Y (2014) Characterization of the Key Odorants in Light Aroma Type Chinese Liquor by Gas Chromatography-Olfactometry, Quantitative Measurements, Aroma Recombination, and Omission Studies. J Agric Food Chem 62(25):5796–5804. https://doi.org/10.1021/jf501214c Gugino IM, Alfeo V, Ashkezary MR, Marconi O, Pirrone A, Francesca N, Cincotta F, Verzera A, Todaro A (2024) Maiorca wheat malt: A comprehensive analysis of physicochemical properties, volatile compounds, and sensory evaluation in brewing process and final product quality. Food Chem 435:137517. https://doi.org/10.1016/j.foodchem.2023.137517 He F, Yang S, Zhang G, Xu L, Li H, Sun J, Huang M, Zheng F, Sun B (2022) Exploration of key aroma active compounds in strong flavor Baijiu during the distillation by modern instrument detection technology combined with multivariate statistical analysis methods. J Food Compos Anal 110:104577. https://doi.org/10.1016/j.jfca.2022.104577 Jia W, Fan Z, Du A, Li Y, Zhang R, Shi Q, Shi L, Chu X (2020) Recent advances in Baijiu analysis by chromatography based technology-A review. Food Chem 324:126899. https://doi.org/10.1016/j.foodchem.2020.126899 Jia W, Ma R (2023) Cross-modal interactions caused by nonvolatile compounds derived from fermentation, distillation and aging to harmonize flavor. Crit Rev Food Sci Nutr 0(0):1–28. https://doi.org/10.1080/10408398.2023.2172714 Li H, Qin D, Wu Z, Sun B, Sun X, Huang M, Sun J, Zheng F (2019) Characterization of key aroma compounds in Chinese Guojing sesame-flavor Baijiu by means of molecular sensory science. Food Chem 284:100–107. https://doi.org/10.1016/j.foodchem.2019.01.102 Li S, Yang H, Tian H, Zou J, Li J (2020) Correlation analysis of the age of brandy and volatiles in brandy by gas chromatography-mass spectrometry and gas chromatography-ion mobility spectrometry. Microchem J 157:104948. https://doi.org/10.1016/j.microc.2020.104948 Liu H, Sun B (2018) Effect of Fermentation Processing on the Flavor of Baijiu. J Agric Food Chem 66(22):5425–5432. https://doi.org/10.1021/acs.jafc.8b00692 . Scopus Louw S (2021) Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020. Anal Sci Adv 2(3–4):157–170. https://doi.org/10.1002/ansa.202000158 Meng Y, Wang X, Li Y, Chen J, Chen X (2024) Microbial interactions and dynamic changes of volatile flavor compounds during the fermentation of traditional kombucha. Food Chem 430:137060. https://doi.org/10.1016/j.foodchem.2023.137060 National Bureau of Statistics (2022) National data. Retrieved from http://www.data.stats.gov.cn . Accessed 8 November 2023 Niu Y, Zhang J, Xiao Z, Zhu J (2020) Evaluation of the Perceptual Interactions Between Higher Alcohols and Off-Odor Acids in Laimao Baijiu by σ-τ Plot and Partition Coefficient. J Agric Food Chem 68(50):14938–14949. https://doi.org/10.1021/acs.jafc.0c05676 Putri AR, Aliaño-González MJ, Ferreiro M, Setyaningsih W, Rohman A, Riyanto S, Palma M (2020) Development of a methodology based on headspace-gas chromatography-ion mobility spectrometry for the rapid detection and determination of patin fish oil adulterated with palm oil. Arab J Chem 13(10):7524–7532. https://doi.org/10.1016/j.arabjc.2020.08.026 Robinson AL, Ebeler SE, Heymann H, Boss PK, Solomon PS, Trengove RD (2009) Interactions between Wine Volatile Compounds and Grape and Wine Matrix Components Influence Aroma Compound Headspace Partitioning. J Agric Food Chem 57(21):10313–10322. https://doi.org/10.1021/jf902586n Sáenz-Navajas M-P, Campo E, Culleré L, Fernández-Zurbano P, Valentin D, Ferreira V (2010) Effects of the Nonvolatile Matrix on the Aroma Perception of Wine. J Agric Food Chem 58(9):5574–5585. https://doi.org/10.1021/jf904377p Song Z, Cao Y, Qiao H, Wen P, Sun G, Zhang W, Han L (2023) Analysis of the effect of Tenebrio Molitor rennet on the flavor formation of Cheddar cheese during ripening based on gas chromatography-ion mobility spectrometry (GC-IMS). Food Res Int 171:113074. https://doi.org/10.1016/j.foodres.2023.113074 Sun X, Wan Y, Han J, Liu W, Wei C (2023) Analysis of Volatile Compounds and Flavor Fingerprint in Hot-Pressed Flaxseed Oil Processed Under Different Roasting Conditions Using Headspace-Gas Chromatography-Ion Mobility Spectrometry. Food Anal Methods 16(5):888–899. https://doi.org/10.1007/s12161-023-02467-8 Tian L, Zeng Y, Zheng X, Chiu Y, Liu T (2019) Detection of Peanut Oil Adulteration Mixed with Rapeseed Oil Using Gas Chromatography and Gas Chromatography-Ion Mobility Spectrometry. Food Anal Methods 12(10):2282–2292. https://doi.org/10.1007/s12161-019-01571-y Wang G, Song X, Zhu L, Li Q, Zheng F, Geng X, Li L, Wu J, Li H, Sun B (2022a) A flavoromics strategy for the differentiation of different types of Baijiu according to the non-volatile organic acids. Food Chem 374:131641. https://doi.org/10.1016/j.foodchem.2021.131641 Wang G, Li X, Song X, Jing S, Meng S, Zheng F, Li H, Li Z, Shen C, Shen Y (2022b) Optimization and Validation of a Method for Analysis of Non-Volatile Organic Acids in Baijiu by Derivatization and its Application in Three Flavor-Types of Baijiu. Food Anal Methods 15(6):1606–1618. https://doi.org/10.1007/s12161-021-02215-w Wang G, Jing S, Song X, Zhu L, Zheng F, Sun B (2022c) Reconstitution of the Flavor Signature of Laobaigan-Type Baijiu Based on the Natural Concentrations of Its Odor-Active Compounds and Nonvolatile Organic Acids. J Agric Food Chem 70(3):837–846. https://doi.org/10.1021/acs.jafc.1c06791 Wang G, Jing S, Wang X, Zheng F, Li H, Sun B, Li Z (2022d) Evaluation of the Perceptual Interaction among Ester Odorants and Nonvolatile Organic Acids in Baijiu by GC-MS, GC-O, Odor Threshold, and Sensory Analysis. J Agric Food Chem 70(43):13987–13995. https://doi.org/10.1021/acs.jafc.2c04321 Wang J, Ming Y, Li Y, Huang M, Luo S, Li H, Li H, Wu J, Sun X, Luo X (2021) Characterization and comparative study of the key odorants in Caoyuanwang mild-flavor style Baijiu using gas chromatography-olfactometry and sensory approaches. Food Chem 347:129028. https://doi.org/10.1016/j.foodchem.2021.129028 Wang W, Xu Y, Huang H, Pang Z, Fu Z, Niu J, Zhang C, Li W, Li X, Sun B (2021) Correlation between microbial communities and flavor compounds during the fifth and sixth rounds of sauce-flavor baijiu fermentation. Food Res Int 150:110741. https://doi.org/10.1016/j.foodres.2021.110741 Xie Y, Bai T, Zhang T, Zheng P, Huang M, Xin L, Gong W, Naeem A, Chen F, Zhang H, Zhang J (2023) Correlations between flavor and fermentation days and changes in quality-related physiochemical characteristics of fermented Aurantii Fructus. Food Chem 429:136424. https://doi.org/10.1016/j.foodchem.2023.136424 Yao W, Cai Y, Liu D, Chen Y, Li J, Zhang M, Chen N, Zhang H (2022) Analysis of flavor formation during production of Dezhou braised chicken using headspace-gas chromatography-ion mobility spec-trometry (HS-GC-IMS). Food Chemistry 370. Scopus. https://doi.org/10.1016/j.foodchem.2021.130989 Yu M, Xie Q, Sun H, Wang Y, Tang Y, Wang B, Song H, Wang L, Jiang S, Li K, Zhang Y, Zheng C (2024) Characterization of odor properties of human milk: Effect of inter-individual nutrient differences on key odor-active compounds and odor attributes. Food Chem 431:137091. https://doi.org/10.1016/j.foodchem.2023.137091 Zhang H, Wang Y, Feng X, Iftikhar M, Meng X, Wang J (2022) The Analysis of Changes in Nutritional Components and Flavor Characteristics of Wazu Rice Wine During Fermentation Process. Food Anal Methods 15(4):1132–1142. https://doi.org/10.1007/s12161-021-02188-w . Scopus Zhang M, Wei D, He L, Wang D, Wang L, Tang D, Zhao R, Ye X, Wu C, Peng W (2022) Application of response surface methodology (RSM) for optimization of the supercritical CO2 extract of oil from Zanthoxylum bungeanum pericarp: Yield, composition and gastric protective effect. Food Chemistry: X 15:100391. https://doi.org/10.1016/j.fochx.2022.100391 Zhang Q, Shi J, Wang Y, Zhu T, Huang M, Ye H, Wei J, Wu J, Sun J, Li H (2022) Research on interaction regularities and mechanisms between lactic acid and aroma compounds of Baijiu. Food Chem 397:133765. https://doi.org/10.1016/j.foodchem.2022.133765 Zhao D, Shi D, Sun J, Li A, Sun B, Zhao M, Chen F, Sun X, Li H, Huang M, Zheng F (2018) Characterization of key aroma compounds in Gujinggong Chinese Baijiu by gas chromatography-olfactometry, quantitative measurements, and sensory evaluation. Food Res Int 105:616–627. https://doi.org/10.1016/j.foodres.2017.11.074 Zhao Y, Zhan P, Tian HL, Wang P, Lu C, Tian P, Zhang YY (2021) Insights into the Aroma Profile in Three Kiwifruit Varieties by HS-SPME-GC-MS and GC-IMS Coupled with DSA. Food Anal Methods 14(5):1033–1042. https://doi.org/10.1007/s12161-020-01952-8 Zhu L, Wang X, Song X, Zheng F, Li H, Chen F, Zhang Y, Zhang F (2020) Evolution of the key odorants and aroma profiles in traditional Laowuzeng baijiu during its one-year ageing. Food Chem 310:125898. https://doi.org/10.1016/j.foodchem.2019.125898 Zhu W, Benkwitz F, Sarmadi B, Kilmartin PA (2021) Validation Study on the simultaneous Quantitation of Multiple Wine Aroma Compounds with StaticHeadspace-Gas Chromatography-Ion Mobility Spectrometry. J Agric Food Chem 69(49):15020–15035. https://doi.org/10.1021/acs.jafc.1c06411 Additional Declarations No competing interests reported. Supplementary Files Supplementmaterials.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-3890358","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268914950,"identity":"ac37673d-99ab-44aa-aa39-362b59cf3a79","order_by":0,"name":"Guangnan Wang","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Guangnan","middleName":"","lastName":"Wang","suffix":""},{"id":268914951,"identity":"2fbe9451-1e4b-4db0-b0a0-c9d89123587f","order_by":1,"name":"Feifei Liu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Feifei","middleName":"","lastName":"Liu","suffix":""},{"id":268914952,"identity":"33072378-8208-404d-a54d-fe8cfbaab366","order_by":2,"name":"Huan Cheng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Cheng","suffix":""},{"id":268914953,"identity":"635746d6-364f-4c56-9e4c-b3f92f6fde12","order_by":3,"name":"Fuping Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACCRBhwyBnwMADYjETqyWNwdiAjVQtiRuI1iI/u/nYwy8Jd9K3y/cek2CosE5sYD97AK8WxjnH0o1lEp7l7mzjS5NgOJOe2MCTl4BXC7NEjpm05I/DuRuO8ZhJMLYdTmyQ4DHAq4VNIv+btETC4XQDsJZ/RGjhkchhk/yQcDgBoqWBCC0SEmlm0gwJzwx3tuUlWyQAPdbGk4Nfi/yM5GeSPxLuyJsznz1440ONtWw/+xn8WkCAmYfhAISVAPIdQfVAwPgDpmUUjIJRMApGATYAAGrNQXM4rIXTAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Fuping","middleName":"","lastName":"Zheng","suffix":""},{"id":268914954,"identity":"e5cd5ed1-5f2e-4ff9-bac4-c75943923bf5","order_by":4,"name":"Xingqian Ye","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xingqian","middleName":"","lastName":"Ye","suffix":""},{"id":268914955,"identity":"2ff3f421-e46a-4c6d-a6d9-a5f32d3ba5fc","order_by":5,"name":"Baoguo Sun","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Baoguo","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-01-23 07:59:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50228320,"identity":"4a99e72b-0c86-4fa0-86a8-2a56ea35cb2d","added_by":"auto","created_at":"2024-01-26 18:46:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":671591,"visible":true,"origin":"","legend":"\u003cp\u003eHS-GC-IMS analysis based on different model solutions: (a) 3D topographic plots of different model solutions; (b) 2D topographic plots with differential comparison mode of different model solutions; (c) fingerprints of all model solutions generated using the Gallery Plot. A: 100 mg/L of lactic acid, B: 200 mg/L of lactic acid, C: 400 mg/L of lactic acid, D: 800 mg/L of lactic acid, E: 1600 mg/L of lactic acid and F: 3200 mg/L of lactic acid. All numbers of model solution in this experiment are the same as described above.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/3acd641ef3edbd1529fa10ba.png"},{"id":50227935,"identity":"251163ae-3598-4cae-b6e0-c410791e2aac","added_by":"auto","created_at":"2024-01-26 18:38:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1074532,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of the analysis conditions of flavor compounds in Baijiu. (a) 3-D response surface plots for the peak intensities of the target compounds. (b) 2-D contour plots for the peak intensities of the target compounds.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/7378fae87afa1b87f4bd8c35.png"},{"id":50227933,"identity":"d60c2112-94ce-4233-9fdf-530ceaad735b","added_by":"auto","created_at":"2024-01-26 18:38:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229239,"visible":true,"origin":"","legend":"\u003cp\u003e(a) PCA score plot of different model solutions. (b) Heatmap for the 13 flavor compounds and HCA for different model solutions.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/c48506d74415c20164730a92.png"},{"id":50227932,"identity":"71d6d9c7-f02a-48ce-8848-e4600ac860c9","added_by":"auto","created_at":"2024-01-26 18:38:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163615,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of the addition of lactic acid and the release of 13 flavor compounds. Red represents a positive correlation and blue represents a negative correlation.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/580cf22b1a18b12db1bc58e8.png"},{"id":50429381,"identity":"e687f869-8352-4f4f-ac92-e269a13abf27","added_by":"auto","created_at":"2024-01-31 11:37:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2245768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/33def451-f9c0-4d04-b42f-d06f95567cee.pdf"},{"id":50228319,"identity":"e0edcacd-7cfe-4c3f-919f-a8a2e886e17b","added_by":"auto","created_at":"2024-01-26 18:46:35","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21713,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3890358/v1/838656858a13cd38cb7e9da9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization a method based on headspace-gas chromatography-ion mobility spectrometry for the rapid and visual analyzation of flavor compounds interaction in Baijiu","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBaijiu is a widely enjoyed alcoholic beverage appreciated for its distinct flavor. According to the National Bureau of Statistics, the annual sales of Baijiu in China, where it originated, reached 6.71\u0026nbsp;million kiloliters in 2022, with a value of 95.14\u0026nbsp;billion US dollars. (data from the National Bureau of Statistics). The traditional craftsmanship behind Baijiu involves a meticulous process, including the manufacturing of \u003cem\u003ejiuqu\u003c/em\u003e, fermentation, and distillation under solid-state, aging in pottery jar, and blending. Due to the various sources of raw materials, fermentation techniques, and microorganisms, the composition of Baijiu is always highly varied. Between 1990 and 2017, researchers identified more than 1874 different volatile compounds in Baijiu. Currently, twelve flavors are commonly recognized by customers. Among them, \u003cem\u003eStrong\u003c/em\u003e, \u003cem\u003eLight\u003c/em\u003e, \u003cem\u003eSoysauce\u003c/em\u003e, and \u003cem\u003eRice\u003c/em\u003e are considered as the four fundamental flavor types, while the additional flavors, \u003cem\u003ei.e\u003c/em\u003e., \u003cem\u003eFeng\u003c/em\u003e, \u003cem\u003eTe\u003c/em\u003e, \u003cem\u003eSesame\u003c/em\u003e, \u003cem\u003eLaobaigan\u003c/em\u003e, \u003cem\u003eFuyu\u003c/em\u003e, \u003cem\u003eHerbal\u003c/em\u003e, \u003cem\u003eChi\u003c/em\u003e, and \u003cem\u003eJian\u003c/em\u003e, are regarded as the derived types (Liu \u0026amp; Sun, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe specific flavor of Baijiu is determined by the categories and amounts of flavor compounds (Wang et al., 2022 a). Despite the thousands of volatile flavor compounds that have been identified and reported in Baijiu, our overall understanding of its flavor remains incomplete as earlier studies mainly concentrated on the types, contents, and characteristics of volatile flavor compounds, without considering the important role of non-volatile flavor compounds (Dong et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., 2022 b; L. Zhu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although non-volatile compounds do not directly impact the aroma, their interactions with volatiles still have a significant influence on the flavor and quality of Baijiu (Jia \u0026amp; Ma, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S\u0026aacute;enz-Navajas et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Numerous studies have uncovered the vital part that the interactions between these compounds play. Stephanie et al. established that the non-volatiles had a major influence on the strength of some aroma qualities of Dornfelder red wine since the flavor of the recombinant model mixture was enhanced when non-volatile flavor compounds were added, which was more in line with the flavor of the original red wine (Frank et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It has been established that non-volatile organic acids play an essential role in the flavor of Baijiu. The flavor of recombination B more closely resembled the original Baijiu than recombination A, as it contained the same 33 odor-active compounds, plus an additional 37 non-volatile organic acids. The incorporation of non-volatile organic acids brought down the odors of alcohol and sweetness, while at the same time intensifying odors related to fruit, acid, flowers, jujube, and grain (Wang et al., 2022 c). Furthermore, Wang et al. discovered that the addition of lactic acid led to a significant decrease in the thresholds of ethyl lactate and ethyl acetate, indicating that lactic acid has either an additive or synergistic effect on the release of these two esters (Wang et al., 2022 d). However, the mechanism of interaction between non-volatile and volatile flavor components in Baijiu has not been sufficiently revealed. This scientific ambiguity has largely prevented us from fully grasping the flavor of Baijiu. Consequently, the interaction between non-volatile and volatile compounds in Baijiu should be further examined.\u003c/p\u003e \u003cp\u003ePrevious reports suggest that nonvolatile components in the matrix can alter the gas-liquid distribution of volatile compounds in the headspace, thus influencing the body flavor of the wine (Robinson et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Zhang et al. employed HS-SPME-GC-MS to investigate the interaction between lactic acid and flavor compounds in Baijiu. And the results indicated that lactic acid can raise the volatility of short-chain branched esters while diminishing the volatility of most aromatic compounds (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For food flavor analysis, GC-MS is the most advanced and mature technique (Jia et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, analyzing Baijiu necessitates a complex, time-consuming GC-MS process with intricate pre-treatment (Louw, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The alteration of the headspace concentration directly mirrors the alteration in the volatility of the flavor compounds. Nevertheless, with HS-SPME analysis, the aroma compounds are not only shared between liquid and gas (air) but also between gas and solid (SPME fibers), which might affect the analysis results (Ferracane et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), a technology utilizing the drift times of ions to identify volatiles, has been developed as a way to provide fast and effective detection. This technology is also being employed to accurately monitor the flavor compounds of foods. HS-GC-IMS is fitted with an automated HS injector, which is capable of analyzing volatile compounds from both liquid and solid samples without any prior preparation (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Benefiting from the contrasting mobility of gas phase ions in an unchanging electric field, IMS is an advantageous analytical technique with a brief incubation period, accelerated analytical time, and heightened sensitivity (He et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides, compared to GC-MS, GC-IMS offers a lower detection limit, remarkable sensitivity, and ease of use, making it a great choice for detecting flavor compounds at room temperature and ambient pressure (Song et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, GC-IMS is able to provide a color contours image to illustrate the analysis results, helping to clearly differentiate between samples (Yao et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). GC-IMS is currently employed for the identification of food safety, origin, tracking process, and authenticity (Calle et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). So far, there is no research that has employed GC-IMS to investigate the interactions between various flavor components in Baijiu.\u003c/p\u003e \u003cp\u003eThus, the objectives of this study were to (1) employ Box Behnken design and Response Surface Methodology to develop and refine an analytical method based on HS-GC-IMS, aiming to swiftly identify the interaction between flavor compounds in Baijiu; (2) use the optimized method to explore the influence of lactic acid on the release of 13 commonly flavor compounds in Baijiu. Chemometric techniques, such as PCA, heatmap, HCA, and correlation analysis, were employed to measure the intensity of the interaction effect.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Reagents and samples\u003c/h2\u003e \u003cp\u003eAll the standards employed in this study had a minimum purity of 95%, sourced from Sigma-Aldrich Co. Ltd. (Shanghai, China) and J\u0026amp;K Co. Ltd. (Shanghai, China). Experiments in this study were conducted using model solutions of \u0026ldquo;flavor compounds-aqueous ethanol\u0026rdquo;. The concentration of flavor compounds used in the experiment was the average of the concentrations reported in Baijiu (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The detailed concentrations of each compound in Baijiu are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. To create the standard stock solutions, the standard flavor compounds were dissolved in ethanol. The model solutions were created by mixing the standard stock solutions and then diluting them in steps until the desired concentrations were reached. Lactic acid widely exists in different flavor types of Baijiu, and the highest amount present has not surpassed 3000 mg/L. Thus, in order to gain a better understanding of the interaction between lactic acid and other flavor compounds, this study varied the amount of lactic acid from 200 mg/L, 400 mg/L, 800 mg/L, 1600 mg/L to 3200 mg/L. Besides, the model solution of this experiment is labeled CG (Control group, with no lactic acid added), A (100 mg/L of lactic acid), B (200 mg/L of lactic acid), C (400 mg/L of lactic acid), D (800 mg/L of lactic acid), E (1600 mg/L of lactic acid) and F (3200 mg/L of lactic acid) according to the amount of lactic acid added.\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\u003eAroma description, inter-day precision, intra-day precision, and experiment concentration of 13 flavor compounds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompounds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAroma description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInter-day precision (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntra-day precision (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConcentration (mg/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthyl lactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1262.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimethyl trisulfide\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSauerkraut-like, sulfur, cabbage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e802.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Hydroxy-2-butanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweet, creamy, buttery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthyl hexanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1037.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Methylbutanal\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreen, malty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e320.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Methoxy-4-methylphenol\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWoody, smoky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1011.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthyl pentanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity, apple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHexanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweaty, cheesy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e230.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Methyl-1-propanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity, malty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e168.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity, pineapple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1478.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Methyl-1-butanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e405.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthyl butanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFruity, pineapple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e418.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e, The concentration was \u0026micro;g/L.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAroma description and concentration of each compound were reported in (Fan et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS)\u003c/h2\u003e \u003cp\u003eThe GC-IMS analysis was performed on an HS-GC-IMS Flavor Spec (G.A.S., Dortmund, Germany). The model solutions enclosed in vials were immediately incubated in the auto-sampler oven. After that, an automatic injection of 500 \u0026micro;L of the sample headspace was conducted at a flow rate of 2 mL/min at 85 \u003csup\u003eo\u003c/sup\u003eC. A new MXT-WAX column (30 meters, 0.53 mm ID, 1 \u0026micro;m df) was employed to achieve chromatographic separation, with high-purity nitrogen (99.999%) as the carrier gas and heated at 60 \u003csup\u003eo\u003c/sup\u003eC to ensure timely separation.\u003c/p\u003e \u003cp\u003eAll tests employed \u003cem\u003en\u003c/em\u003e-ketones C\u003csub\u003e4\u003c/sub\u003e-C\u003csub\u003e9\u003c/sub\u003e (provided by Sinopharm Chemical Reagent Co., Ltd, China) as an external reference for the determination of the retention index (RI) of the flavor compound. Flavor compounds are initially identified by matching their retention index (RI) and drift time with the NIST Library and IMS database retrieval software from G.A.S. Subsequently, all flavor compounds are further confirmed by analyzing them under the same conditions with their standards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Box-Behnken Design-Response Surface Methodology\u003c/h2\u003e \u003cp\u003eResearch has demonstrated that specific independent variables exhibit a direct correlation with the signal of the IMS spectra, particularly those factors that influence the concentrations of volatile components in the headspace injected into the GC-IMS system. The variables to be considered include the sample volume, the injection volume, and the incubation time. Furthermore, when studying alcohol samples, the alcohol content is a major factor to consider. Therefore, in order to optimize the process, four variables were chosen: the sample volume, the incubation time, the injection volume, and the alcohol content. Three levels (low, middle, and high points) were allocated to each variable (Table S2).\u003c/p\u003e \u003cp\u003eTo acquire the greatest peak intensity, a Box-Behnken design in conjunction with Response Surface Methodology was utilized to optimize the analysis conditions (Putri et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The design was formulated from 46 experiments, all of which were conducted in a random sequence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The aim of the optimization was to maximize the response value of the target compounds, so as to guarantee the most desirable efficiency when detecting flavor compounds in Baijiu. Design Expert software 8.0.6 was used to analyze all the experimental data, and the model analysis was conducted with a 95% confidence interval (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The estimated ridge of the best response was determined when the results demonstrated a saddle point in the response surface.\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\u003eExperimental design of BBD matrix with the corresponding factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eincubation time (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInjection volume (mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample volume (mL:)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlcohol content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSum intensities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e144341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e145776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e146600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e152162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e152430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e155275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e157204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e159691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e160299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e163515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e167697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e170543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e182261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e184145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e189060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e189788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e194712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e194928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e195115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e203266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e208411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e209218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e210785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e211719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e213464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e214149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e215245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe instrumental analysis software LAV (version 2.0.0 from G.A.S., Dortmund, Germany), Reporter, Gallery Plot, and GC \u0026times; IMS Library Search were employed to analyze the IMS data, allowing for sample analysis from various perspectives. A design Expert was employed to devise the BBD and to ascertain the most suitable analysis conditions via RSM analysis. Unsupervised analyses such as PCA, heatmap, and HCA were used to illustrate the distinctions and connections among different model solutions. IBM SPSS Statistics 24 software (IBM Inc., Armonk, NY, USA) was utilized to execute one-way ANOVA and Duncan\u0026rsquo;s multiple range test. The data was considered to be significantly different when \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Spearman correlation test was used to examine the correlation between lactic acid addition and the volatilization of flavor compounds.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 volatile profiles of GC-IMS analysis\u003c/h2\u003e \u003cp\u003eA 3-dimensional map is used to present GC-IMS detection results, including retention time, drift time, and intensity. A topographic plot is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, with a bright vertical line on the left representing the normalized reaction ion peak (RIP). Every point to the right of the RIP stands for one or more signal peaks, and the deeper the red, the more powerful the peak. The figure clearly displays the 13 volatile compounds in the model solution, and the other points may be interference from the alcohol solutions or impurities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optimization of the method\u003c/h2\u003e \u003cp\u003eThis experiment was conducted to optimize an analytical method capable of detecting and measuring the flavor compounds in Baijiu. The optimization of the four main variables (sample volume, incubation time, injection volume, and alcohol content) was the initial step, which was done by means of BBD-RSM. (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the experimental conditions along with the sum of the peak intensities of the target compounds. The sum of peak intensities, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, ranged from 144341 to 215245, indicating that the optimized experimental conditions had a considerable effect on the detection of flavor compounds in Baijiu. Fisher\u0026rsquo;s F-test was employed to assess the model, and the details of the result can be found in Table S3. The coefficient of variation (CV) being below 10% and the \u0026ldquo;Adeq Precision\u0026rdquo; being over 4 indicate the reliability of the model. In addition, the predicted R\u003csup\u003e2\u003c/sup\u003e (0.8925) and adjusted R\u003csup\u003e2\u003c/sup\u003e (0.9604) differed by less than 0.2, demonstrating the model\u0026rsquo;s high accuracy. Lastly, the \u003cem\u003ep\u003c/em\u003e-value was less than 0.0001 and the lack of fit was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), demonstrating that the model was effective in elucidating the relationship between independent variables and dependent variable. The discriminant values obtained from the experiment were then adjusted to the values predicted by the polynomial function model (Eq.\u0026nbsp;(1)).\u003c/p\u003e \u003cp\u003ey = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}+\\sum _{i=1}^{k}{\\beta }_{i}{x}_{i}+\\sum _{i=1}^{k}{\\beta }_{ii}{x}_{i}^{2}+\\sum _{i=1}^{k}\\sum _{j=1,j\\ne i}^{k}{\\beta }_{ij}{x}_{i}{x}_{j}+\\epsilon\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eIn this equation, y is the predicted response, \u003cem\u003ei.e\u003c/em\u003e., the sum of the peak intensities of the target compounds. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the ordinate at the origin, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the variables that influence the response. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the linear coefficients, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{ii}\\)\u003c/span\u003e\u003c/span\u003e are the quadratic coefficients, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{ij}\\)\u003c/span\u003e\u003c/span\u003e are the cross-product coefficients. The significance of the coefficients of the parameters in the quadratic equation is indicated by the \u003cem\u003ep\u003c/em\u003e-values in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This optimization experiment revealed that injection volume, sample volume, and alcohol content were all significant variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, the interaction between injection volume and alcohol content was also significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0173). The regression coefficient (\u003cem\u003eβ\u003c/em\u003e) of the injection volume was 22881.04, this positive value indicates a positive effect, which implies that an increase in the factor leads to an increase in the differences between the samples. On the contrary, a negative value of the regression coefficient of the sample volume \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e sample volume (\u003cem\u003eβ\u003c/em\u003e = -19221.29) and alcohol content \u0026times; alcohol content (\u003cem\u003eβ\u003c/em\u003e = -20168.2) indicate a negative effect, and the lower the value of these factors, the greater the differences between the samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance of the quadratic model adjusted for the discrimination of flavor compounds in model solutions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2436.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.13E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Injection volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22881.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.28E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.28E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e294.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Sample volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7837.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.37E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.37E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Alcohol content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14631.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.57E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e120.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.17E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.17E\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e693.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.92E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2482.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6230.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1680.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-17469.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18774.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19221.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64E\u0026thinsp;+\u0026thinsp;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e123.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.13E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.70E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.12E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCor Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBy using 3-D response surface plots and 2-D contour plots, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; b, it is possible to determine the sum of the peak intensities of the target compounds. The results of this analysis will show if the fitted surface is a maximum, a minimum, or a saddle point. Finally, the optimal experimental settings were determined by the signature model and 3D contour, which include the sample volume of 0.57 mL, the incubation time of 26 minutes, the injection volume of 0.71 mL, and the alcohol content of 16%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Precisions of the method\u003c/h2\u003e \u003cp\u003eAfter the best possible conditions had been established, the accuracy of the new method was to be calculated. To determine intra-day precision, each compound was analyzed at 10 mg/L concentration three times in one day. Similarly, the inter-day precision was determined by repeating the same intra-day precision analysis on three different days. This method exhibits great accuracy, with RSD values for intra-day precision being no higher than 7.37%, and those for inter-day precision not exceeding 10.14% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of lactic acid on the volatilities of aroma compounds in Baijiu\u003c/h2\u003e \u003cp\u003eAs a complex food matrix, Baijiu is composed of a variety of flavor compounds, and the interaction between these compounds has an important contribution to the flavor of Baijiu. Previous studies have shown that the addition of non-volatile organic acids changes the overall flavor of Baijiu. In addition, after the inclusion of different concentrations of lactic acid, ethyl acetate and ethyl lactate experienced considerable shifts in their perceived thresholds. Therefore, in this study, 13 volatile flavor compounds commonly found in Baijiu were chosen and dissolved in an aqueous ethanol solution to construct model solutions. Then, to explore the interaction between lactic acid and volatile flavor compounds, five different concentrations of lactic acid were added to the model solution. And HS-GC-IMS was employed to differentiate between the various model solutions, as GC-IMS is renowned for its accuracy, sensitivity, and stability in detecting modifications in compound concentrations. In addition, by combining with the LAV software (G.A.S., Dortmund, Germany), the contrasts between different model solutions can be more easily perceived (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data was initially depicted with a 3D topographical visualization, where the Y-axis, X-axis, and Z-axis symbolized the retention time of the gas chromatograph, ion migration time, and peak height, respectively. The color of the flavor substance is an indicator of its signal strength, with white showing a weaker signal and red showing a stronger one. It is perceptible that the detection concentration of the 13 volatile compounds shifted as the lactic acid amount augmented (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eGiven the roughness of the 3D spectrum, a differential comparisons mode could be employed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The control group, which did not add lactic acid, has a blue background, with a red vertical line at 1.0 on the horizontal coordinate representing the reactant ion peak (RIP), and each point on the right-hand side of the RIP symbolize a compound. The spectral diagram of the model solution without lactic acid was taken as the reference, and the spectral diagram of the other samples were then subtracted from it. Spots of varying colors indicate the intensity of the peak concentrations; red spots signify a higher concentration than the reference, blue spots signify a lower concentration, and white shows that the concentrations are the same. The differential comparisons spectrum elucidates the need for a more rigorous statistical analysis by clearly demonstrating the disparities between different model solutions.\u003c/p\u003e \u003cp\u003eThe topographic plots visually demonstrated the fluctuation of volatile compounds. Nevertheless, it was still challenging to make a precise evaluation of the components on the map. The Gallery Plot of the LAV software program was a great asset in resolving this issue, as it was used for fingerprint analysis. In the fingerprint, each horizontal line displays multiple volatile compounds from a single sample, while each vertical line shows the variation of a particular volatile compound across various samples. Additionally, the color of the cell represents the amount of the volatile compound, with brighter colors indicating higher concentrations. By using fingerprint, the alterations of volatile compounds in different samples were monitored. The strength of the interaction between lactic acid and these thirteen aroma compounds can be measured by comparing the detected concentrations of these volatile compounds in the various model solutions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The addition of lactic acid causes a notable increase in the release of flavor compounds such as dimethyl trisulfide, ethyl lactate, 3-hydroxy-2-butanone, 2-methoxy-4-methylphenol and 1-propanol in the model solution.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Principal component analysis and hierarchical cluster analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the effect of lactic acid on the flavor compounds released in Baijiu, PCA was performed and the data underwent preprocessing before the analysis. The logarithmic transformation was utilized to transform the concentration of all flavor compounds, thus preserving the effect of small values in the data (Xie et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This conversion lessened the standard deviation of the variables, thereby providing more accurate analysis results.\u003c/p\u003e \u003cp\u003eThe PCA score plot was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. The first principal component (PC1) and second principal component (PC2) together accounted for 81.5% of the total variance, with PC1 making up 56.1% of the variance and PC2 accounting for 25.4%. Through the PCA plot, it is evident that the samples which are nearer to each other are more similar, whereas those which are farther apart indicate greater dissimilarity. The analysis revealed that the distance between the seven model solutions increased when lactic acid was added, which demonstrating that lactic acid was the cause of the variation in the samples. It is evident that lactic acid plays a key role in the release of flavor compounds in Baijiu.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Heatmap, hierarchical cluster analysis\u003c/h2\u003e \u003cp\u003eUnsupervised pattern recognition methods enable an initial assessment of the information held within a data matrix. Combining hierarchical cluster analysis (HCA), an unsupervised pattern recognition method, with a heatmap can help to better visualize the similarities and differences between different samples (Meng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, a gradient of light to dark colors was used to represent the change in intensity from low to high. Samples that were grouped in the same category showed a strong correlation, with the shorter Euclidean distance indicating a higher level of similarity. The HCA clearly classified the model samples into two groups, the first composed of CG, A and B, and the second of C, D, E and F. The HCA results corroborated the PCA findings, indicating that model solutions with similar amounts of lactic acid added tend to group together. This further implies that the addition of lactic acid has a considerable impact on the release of flavor compounds in the model solution.\u003c/p\u003e \u003cp\u003eHeat maps provide an alternative view compared to HCA, and can be used to divide flavor compounds into two clusters. Cluster 1 consists of caproic acid, 1-propanol, ethyl acetate, 2-methoxy-4-methylphenol, 3-hydroxy-2-butanone, dimethyl trisulfide, and ethyl lactate, which show relatively reduced volatility in model solutions containing low levels of lactic acid. Cluster 2 encompasses 6 flavor compounds, such as 3-methyl-1-butanol, ethyl hexanoate, ethyl pentanoate, 3-methylbutanal, ethyl butanoate and 2-methyl-1-propanol, which have a greater release in solutions with low lactic acid addition. This is likely due to the hydrogen bonding force between lactic acid and flavor compounds, as most of the compounds in this cluster contain hydroxyl groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Correlation analysis\u003c/h2\u003e \u003cp\u003eBoth the PCA and HCA analysis results demonstrate that the incorporation of lactic acid is strongly associated with the liberation of flavor compounds. To ascertain the correlation between lactic acid and the release of flavor compounds, Spearman correlation test was used to evaluate the relationship between model solutions with varying lactic acid levels and the concentration of aroma compounds released (Canalejo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gugino et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The correlation analysis revealed that a positive correlation exists between the addition of lactic acid in the model solution and 2-methoxy-4-methylphenol, ethyl lactate, 3-hydroxy-2-butanone, 1-propanol, hexanoic acid and dimethyl trisulfide, with correlation coefficients of 0.96, 0.95, 0.95, 0.85, 0.87 and 0.65 respectively, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01 or 0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results of the positive correlation between lactic acid and ethyl lactate were in line with the findings of the previous study. This may be attributed to the special steric effects between the inter-molecules of the short-chain branched esters, which give them lower boiling points and higher saturated vapor pressures. Consequently, the volatilities of short-chain branched esters were more affected by lactic acids (Burdock, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, p. 2). As lactic acid is more acidic, it causes the dissociation equilibrium of flavor compounds containing carboxyl groups to shift left, resulting in an increase in their molecular concentration. This, in turn, affects the gas-liquid balance of these flavor compounds and increases their volatility and headspace concentration (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A significant negative correlation was observed between the addition of lactic acid and both ethyl hexanoate and ethyl pentanoate, with r = -0.79 and \u0026minus;\u0026thinsp;0.74, respectively, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001. Previous studies have revealed that the incorporation of non-volatile organic acids can enhance the fruity aroma of \u003cem\u003eLaobaigan\u003c/em\u003e Baijiu. Ethyl lactate, ethyl hexanoate and ethyl pentanoate all possess a fruity aroma, however, their interaction with lactic acid varies, thus necessitating further research to investigate the interaction mechanism of these compounds (Wang et al., 2022 c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, this study revealed that the flavor compounds that had a significant positive correlation with the amount of lactic acid added contained hydroxyl groups, whereas the ones that had a significant negative correlation did not. Previous studies have demonstrated that the van der Waals force and hydrogen bonding force are the primary forces responsible for the interaction between lactic acid and other flavor compounds (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, we hypothesize that the presence of hydroxyl groups could be a factor in this significant correlation, but further investigation is required to confirm this assumption.\u003c/p\u003e \u003cp\u003eDespite the growing evidence of the interactions between flavor compounds in Baijiu, the principles of their production and the connection between the different compounds remain a mystery. To the best of our knowledge, this is the first time that the link between lactic acid and certain crucial flavor compounds in Baijiu has been described.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, we demonstrate that GC-IMS is a suitable method for swiftly, accurately, and completely observing the interplay between flavor compounds in Baijiu. Firstly, this study utilizes a Box Behnken design in combination with Response Surface Methodology to optimize an analytical method for quickly detecting flavor compounds in Baijiu based on GC-IMS. Subsequently, based on the optimized detection method, the effect of different concentrations of lactic acid on the release of 13 flavor compounds was investigated. Results revealed that the addition of lactic acid altered the release of most of the flavor compounds, and the disparity between the model solutions grew as the amount of lactic acid added increased. Finally, to clarify the effect of lactic acid addition on the release of flavor compounds, the correlation analysis of \u0026ldquo;lactic acid addition amount - flavor compound release\u0026rdquo;, revealed that the addition of lactic acid was positively correlated with 2-methoxy-4-methylphenol, ethyl lactate, 3-hydroxy-2-butanone, 1-propanol, hexanoic acid and dimethyl trisulfide, and was significantly negatively correlated with ethyl hexanoate and ethyl pentanoate. This research not only provides a fast and visual way to assess the interaction between flavor compounds in Baijiu but also gives a fresh perspective and ideas to the analysis of Baijiu flavor, leading to a more comprehensive understanding of the flavor of Baijiu or other complex food matrices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Key R\u0026amp;D Program of China (Grant No. 2022YFD2100803).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuangnan Wang: Conceptualization, Writing - Original Draft; Feifei Liu: Investigation, Methodology; Huan Cheng: Resources, Supervision; Fuping Zheng: Conceptualization, Writing - Review \u0026amp; Editing; Xingqian Ye: Conceptualization, Writing - Review \u0026amp; Editing; Baoguo Sun: Resources, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBurdock GA (2019) Fenaroli\u0026rsquo;s Handbook of Flavor Ingredients: Volume 2. CRC Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalle JLP, V\u0026aacute;zquez-Espinosa M, Barea-Sep\u0026uacute;lveda M, Ruiz-Rodr\u0026iacute;guez A, Ferreiro-Gonz\u0026aacute;lez M, Palma M (2023) Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices. Foods 12(13) Article 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods12132536\u003c/span\u003e\u003cspan address=\"10.3390/foods12132536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanalejo D, Mart\u0026iacute;nez-Lapuente L, Ayestar\u0026aacute;n B, P\u0026eacute;rez-Magari\u0026ntilde;o S, Guadalupe Z (2024) Potential use of grape and wine polysaccharide extracts as fining agents to modulate the volatile composition of Viura wines. Food Chem 430:137047. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2023.137047\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2023.137047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong W, Guo R, Liu M, Shen C, Sun X, Zhao M, Sun J, Li H, Zheng F, Huang M, Wu J (2019) Characterization of key odorants causing the roasted and mud-like aromas in strong-aroma types of base Baijiu. Food Res Int 125:108546. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodres.2019.108546\u003c/span\u003e\u003cspan address=\"10.1016/j.foodres.2019.108546\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan H, Fan W, Xu Y (2015) Characterization of Key Odorants in Chinese Chixiang Aroma-Type Liquor by Gas Chromatography-Olfactometry, Quantitative Measurements, Aroma Recombination, and Omission Studies. J Agric Food Chem 63(14):3660\u0026ndash;3668. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf506238f\u003c/span\u003e\u003cspan address=\"10.1021/jf506238f\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Q, Wang X, Zhao Y, Zheng F, Li H, Zhang F, Zhang Y, Chen F (2019) Characterization of key aroma compounds in Laobaigan Chinese Baijiu by GC\u0026times;GC-TOF/MS and means of molecular sensory science. Flavour Fragr J 34(6):514\u0026ndash;525. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ffj.3533\u003c/span\u003e\u003cspan address=\"10.1002/ffj.3533\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerracane A, Manousi N, Tranchida PQ, Zachariadis GA, Mondello L, Rosenberg E (2022) Exploring the volatile profile of whiskey samples using solid-phase microextraction Arrow and comprehensive two-dimensional gas chromatography-mass spectrometry. J Chromatogr A 1676. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chroma.2022.463241\u003c/span\u003e\u003cspan address=\"10.1016/j.chroma.2022.463241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Scopus\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank S, Wollmann N, Schieberle P, Hofmann T (2011) Reconstitution of the Flavor Signature of Dornfelder Red Wine on the Basis of the Natural Concentrations of Its Key Aroma and Taste Compounds. J Agric Food Chem 59(16):8866\u0026ndash;8874. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf202169h\u003c/span\u003e\u003cspan address=\"10.1021/jf202169h\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao W, Fan W, Xu Y (2014) Characterization of the Key Odorants in Light Aroma Type Chinese Liquor by Gas Chromatography-Olfactometry, Quantitative Measurements, Aroma Recombination, and Omission Studies. J Agric Food Chem 62(25):5796\u0026ndash;5804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf501214c\u003c/span\u003e\u003cspan address=\"10.1021/jf501214c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGugino IM, Alfeo V, Ashkezary MR, Marconi O, Pirrone A, Francesca N, Cincotta F, Verzera A, Todaro A (2024) Maiorca wheat malt: A comprehensive analysis of physicochemical properties, volatile compounds, and sensory evaluation in brewing process and final product quality. Food Chem 435:137517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2023.137517\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2023.137517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe F, Yang S, Zhang G, Xu L, Li H, Sun J, Huang M, Zheng F, Sun B (2022) Exploration of key aroma active compounds in strong flavor Baijiu during the distillation by modern instrument detection technology combined with multivariate statistical analysis methods. J Food Compos Anal 110:104577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jfca.2022.104577\u003c/span\u003e\u003cspan address=\"10.1016/j.jfca.2022.104577\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Fan Z, Du A, Li Y, Zhang R, Shi Q, Shi L, Chu X (2020) Recent advances in Baijiu analysis by chromatography based technology-A review. Food Chem 324:126899. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2020.126899\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2020.126899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Ma R (2023) Cross-modal interactions caused by nonvolatile compounds derived from fermentation, distillation and aging to harmonize flavor. Crit Rev Food Sci Nutr 0(0):1\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10408398.2023.2172714\u003c/span\u003e\u003cspan address=\"10.1080/10408398.2023.2172714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Qin D, Wu Z, Sun B, Sun X, Huang M, Sun J, Zheng F (2019) Characterization of key aroma compounds in Chinese Guojing sesame-flavor Baijiu by means of molecular sensory science. Food Chem 284:100\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2019.01.102\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2019.01.102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Yang H, Tian H, Zou J, Li J (2020) Correlation analysis of the age of brandy and volatiles in brandy by gas chromatography-mass spectrometry and gas chromatography-ion mobility spectrometry. Microchem J 157:104948. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.microc.2020.104948\u003c/span\u003e\u003cspan address=\"10.1016/j.microc.2020.104948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Sun B (2018) Effect of Fermentation Processing on the Flavor of Baijiu. J Agric Food Chem 66(22):5425\u0026ndash;5432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jafc.8b00692\u003c/span\u003e\u003cspan address=\"10.1021/acs.jafc.8b00692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Scopus\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouw S (2021) Recent trends in the chromatographic analysis of volatile flavor and fragrance compounds: Annual review 2020. Anal Sci Adv 2(3\u0026ndash;4):157\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ansa.202000158\u003c/span\u003e\u003cspan address=\"10.1002/ansa.202000158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Y, Wang X, Li Y, Chen J, Chen X (2024) Microbial interactions and dynamic changes of volatile flavor compounds during the fermentation of traditional kombucha. Food Chem 430:137060. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2023.137060\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2023.137060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics (2022) National data. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.data.stats.gov.cn\u003c/span\u003e\u003cspan address=\"http://www.data.stats.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 8 November 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Y, Zhang J, Xiao Z, Zhu J (2020) Evaluation of the Perceptual Interactions Between Higher Alcohols and Off-Odor Acids in Laimao Baijiu by σ-τ Plot and Partition Coefficient. J Agric Food Chem 68(50):14938\u0026ndash;14949. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jafc.0c05676\u003c/span\u003e\u003cspan address=\"10.1021/acs.jafc.0c05676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePutri AR, Alia\u0026ntilde;o-Gonz\u0026aacute;lez MJ, Ferreiro M, Setyaningsih W, Rohman A, Riyanto S, Palma M (2020) Development of a methodology based on headspace-gas chromatography-ion mobility spectrometry for the rapid detection and determination of patin fish oil adulterated with palm oil. Arab J Chem 13(10):7524\u0026ndash;7532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arabjc.2020.08.026\u003c/span\u003e\u003cspan address=\"10.1016/j.arabjc.2020.08.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson AL, Ebeler SE, Heymann H, Boss PK, Solomon PS, Trengove RD (2009) Interactions between Wine Volatile Compounds and Grape and Wine Matrix Components Influence Aroma Compound Headspace Partitioning. J Agric Food Chem 57(21):10313\u0026ndash;10322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf902586n\u003c/span\u003e\u003cspan address=\"10.1021/jf902586n\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;enz-Navajas M-P, Campo E, Culler\u0026eacute; L, Fern\u0026aacute;ndez-Zurbano P, Valentin D, Ferreira V (2010) Effects of the Nonvolatile Matrix on the Aroma Perception of Wine. J Agric Food Chem 58(9):5574\u0026ndash;5585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jf904377p\u003c/span\u003e\u003cspan address=\"10.1021/jf904377p\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong Z, Cao Y, Qiao H, Wen P, Sun G, Zhang W, Han L (2023) Analysis of the effect of Tenebrio Molitor rennet on the flavor formation of Cheddar cheese during ripening based on gas chromatography-ion mobility spectrometry (GC-IMS). Food Res Int 171:113074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodres.2023.113074\u003c/span\u003e\u003cspan address=\"10.1016/j.foodres.2023.113074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Wan Y, Han J, Liu W, Wei C (2023) Analysis of Volatile Compounds and Flavor Fingerprint in Hot-Pressed Flaxseed Oil Processed Under Different Roasting Conditions Using Headspace-Gas Chromatography-Ion Mobility Spectrometry. Food Anal Methods 16(5):888\u0026ndash;899. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-023-02467-8\u003c/span\u003e\u003cspan address=\"10.1007/s12161-023-02467-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian L, Zeng Y, Zheng X, Chiu Y, Liu T (2019) Detection of Peanut Oil Adulteration Mixed with Rapeseed Oil Using Gas Chromatography and Gas Chromatography-Ion Mobility Spectrometry. Food Anal Methods 12(10):2282\u0026ndash;2292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-019-01571-y\u003c/span\u003e\u003cspan address=\"10.1007/s12161-019-01571-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Song X, Zhu L, Li Q, Zheng F, Geng X, Li L, Wu J, Li H, Sun B (2022a) A flavoromics strategy for the differentiation of different types of Baijiu according to the non-volatile organic acids. Food Chem 374:131641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2021.131641\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2021.131641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Li X, Song X, Jing S, Meng S, Zheng F, Li H, Li Z, Shen C, Shen Y (2022b) Optimization and Validation of a Method for Analysis of Non-Volatile Organic Acids in Baijiu by Derivatization and its Application in Three Flavor-Types of Baijiu. Food Anal Methods 15(6):1606\u0026ndash;1618. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-021-02215-w\u003c/span\u003e\u003cspan address=\"10.1007/s12161-021-02215-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Jing S, Song X, Zhu L, Zheng F, Sun B (2022c) Reconstitution of the Flavor Signature of Laobaigan-Type Baijiu Based on the Natural Concentrations of Its Odor-Active Compounds and Nonvolatile Organic Acids. J Agric Food Chem 70(3):837\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jafc.1c06791\u003c/span\u003e\u003cspan address=\"10.1021/acs.jafc.1c06791\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Jing S, Wang X, Zheng F, Li H, Sun B, Li Z (2022d) Evaluation of the Perceptual Interaction among Ester Odorants and Nonvolatile Organic Acids in Baijiu by GC-MS, GC-O, Odor Threshold, and Sensory Analysis. J Agric Food Chem 70(43):13987\u0026ndash;13995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jafc.2c04321\u003c/span\u003e\u003cspan address=\"10.1021/acs.jafc.2c04321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Ming Y, Li Y, Huang M, Luo S, Li H, Li H, Wu J, Sun X, Luo X (2021) Characterization and comparative study of the key odorants in Caoyuanwang mild-flavor style Baijiu using gas chromatography-olfactometry and sensory approaches. Food Chem 347:129028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2021.129028\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2021.129028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Xu Y, Huang H, Pang Z, Fu Z, Niu J, Zhang C, Li W, Li X, Sun B (2021) Correlation between microbial communities and flavor compounds during the fifth and sixth rounds of sauce-flavor baijiu fermentation. Food Res Int 150:110741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodres.2021.110741\u003c/span\u003e\u003cspan address=\"10.1016/j.foodres.2021.110741\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Bai T, Zhang T, Zheng P, Huang M, Xin L, Gong W, Naeem A, Chen F, Zhang H, Zhang J (2023) Correlations between flavor and fermentation days and changes in quality-related physiochemical characteristics of fermented Aurantii Fructus. Food Chem 429:136424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2023.136424\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2023.136424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao W, Cai Y, Liu D, Chen Y, Li J, Zhang M, Chen N, Zhang H (2022) Analysis of flavor formation during production of Dezhou braised chicken using headspace-gas chromatography-ion mobility spec-trometry (HS-GC-IMS). Food Chemistry 370. Scopus. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2021.130989\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2021.130989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu M, Xie Q, Sun H, Wang Y, Tang Y, Wang B, Song H, Wang L, Jiang S, Li K, Zhang Y, Zheng C (2024) Characterization of odor properties of human milk: Effect of inter-individual nutrient differences on key odor-active compounds and odor attributes. Food Chem 431:137091. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2023.137091\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2023.137091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Wang Y, Feng X, Iftikhar M, Meng X, Wang J (2022) The Analysis of Changes in Nutritional Components and Flavor Characteristics of Wazu Rice Wine During Fermentation Process. Food Anal Methods 15(4):1132\u0026ndash;1142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-021-02188-w\u003c/span\u003e\u003cspan address=\"10.1007/s12161-021-02188-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Scopus\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang M, Wei D, He L, Wang D, Wang L, Tang D, Zhao R, Ye X, Wu C, Peng W (2022) Application of response surface methodology (RSM) for optimization of the supercritical CO2 extract of oil from Zanthoxylum bungeanum pericarp: Yield, composition and gastric protective effect. Food Chemistry: X 15:100391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fochx.2022.100391\u003c/span\u003e\u003cspan address=\"10.1016/j.fochx.2022.100391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Q, Shi J, Wang Y, Zhu T, Huang M, Ye H, Wei J, Wu J, Sun J, Li H (2022) Research on interaction regularities and mechanisms between lactic acid and aroma compounds of Baijiu. Food Chem 397:133765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2022.133765\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2022.133765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao D, Shi D, Sun J, Li A, Sun B, Zhao M, Chen F, Sun X, Li H, Huang M, Zheng F (2018) Characterization of key aroma compounds in Gujinggong Chinese Baijiu by gas chromatography-olfactometry, quantitative measurements, and sensory evaluation. Food Res Int 105:616\u0026ndash;627. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodres.2017.11.074\u003c/span\u003e\u003cspan address=\"10.1016/j.foodres.2017.11.074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Zhan P, Tian HL, Wang P, Lu C, Tian P, Zhang YY (2021) Insights into the Aroma Profile in Three Kiwifruit Varieties by HS-SPME-GC-MS and GC-IMS Coupled with DSA. Food Anal Methods 14(5):1033\u0026ndash;1042. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-020-01952-8\u003c/span\u003e\u003cspan address=\"10.1007/s12161-020-01952-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu L, Wang X, Song X, Zheng F, Li H, Chen F, Zhang Y, Zhang F (2020) Evolution of the key odorants and aroma profiles in traditional Laowuzeng baijiu during its one-year ageing. Food Chem 310:125898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2019.125898\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2019.125898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Benkwitz F, Sarmadi B, Kilmartin PA (2021) Validation Study on the simultaneous Quantitation of Multiple Wine Aroma Compounds with StaticHeadspace-Gas Chromatography-Ion Mobility Spectrometry. J Agric Food Chem 69(49):15020\u0026ndash;15035. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.jafc.1c06411\u003c/span\u003e\u003cspan address=\"10.1021/acs.jafc.1c06411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"GC-IMS, Baijiu, flavor, interaction effect, visual analysis, Response Surface Methodology","lastPublishedDoi":"10.21203/rs.3.rs-3890358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe flavor of Baijiu is not only determined by the individual flavors of the compounds but also by the combination of them. The exploration of the interaction effects between flavor compounds remains in the nascent stages. In this study, a method based on headspace-gas chromatography-ion mobility spectrometry has been proposed to swiftly elucidate the interactions among flavor compounds in Baijiu. To optimize the critical factors influencing headspace generation, namely, incubation time, sample quantity, injection volume, and alcohol content, we employ a Box-Behnken design integrated with Response Surface Methodology. Model solutions were created with 13 common flavor compounds typically found in Baijiu and varying concentrations of lactic acid, allowing researchers to evaluate the intensity of the interaction between lactic acid and these compounds by comparing the concentration of flavor compounds in the presence of different levels of lactic acid. The findings revealed that lactic acid had a strong correlation with the majority of the flavor compounds, and the disparities among the model solutions with flavor compounds augmented as the lactic acid content rose. This research presents a novel analytical approach, offering rapid insights into the correlation between flavor components in Baijiu and other complex food matrices.\u003c/p\u003e","manuscriptTitle":"Optimization a method based on headspace-gas chromatography-ion mobility spectrometry for the rapid and visual analyzation of flavor compounds interaction in Baijiu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-26 18:38:31","doi":"10.21203/rs.3.rs-3890358/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":"b20c76a3-fbe7-4b46-8197-c747f81c3ce9","owner":[],"postedDate":"January 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-31T11:29:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-26 18:38:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3890358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890358","identity":"rs-3890358","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}