Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images

Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images | 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 Article Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images Sanqin zhao, Dongsheng Xiao, Jiabing Gu, Yongkang Li, Zhe Chao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5504020/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 Pork color is one of the most important attributeswhich reflect meat quality and affect consumer perception and purchase decisions.However, the rapid and objectivepork color scoring method is still lack. The traditional methodsfor the construction of pork color scoring models depend on a large sample sizeand face the problem of sample imbalance,and the slope and intercept calibration significantly rely on the source of the samples and device.To addressed the limitations,a rapid method for constructing a precision pork color scoring models based on six standard color boardimages was proposed in this study, and performanceswere compared with the models constructed using traditional methodsbased on 525 actual pork images from seven pig herds. The results show the performances of CS_1models constructed by the novel methodwith a R 2 of 0.96 to 0.97 are similar to the traditional CS_2 models with a R 2 of 0.97 to 0.99, but are superior to the traditional CS_3 models with is a R 2 of 0.50-0.81, while theoverallclassification accuraciesof theCS_1 models are similar to the traditional models at different scores by the intercept calibration using the pork images from the mixed pig herd, exhibiting thatthe overall classification accuracies of CS_1_L*, CS_1_L*a*, CS_1_L*a*b*models reach 91.43%, 95.62% and 94.10%within a scoring scale ≤± 0.50, respectively. Moreover, the results indicate that the classification accuracies of the models vary considerably among the pig herdsandsignificantly improved by the intercept calibration using the pork imagesfromthe individual pig herd, exhibiting that the overall classification accuracies are enhanced from 91.60% to 93.75%, 95.70% to 95.90%, 93.75% to 96.10% for the CS_1_L*, CS_1_L*a*, CS_1_L*a*b*modelswithin a scoring scale ≤± 0.50, respectively.Taken together, this study provides a fast method for constructing the pork color scoring model, whilethe novel method exhibits several advantages, including full range coverage ranged from 1.0 to 6.0, equivalent small samples with one score for six standard color board images, and rapid intercept calibration with five actual pork images. Moreover, this study demonstrates that the intercept calibration of models is a fast and effective method to enhance the classification accuracy for pork color scoring, which provides new theoretical support for the rapid and objective assessment of pork color. Biological sciences/Computational biology and bioinformatics/Computational models Scientific community and society/Agriculture Physical sciences/Engineering/Electrical and electronic engineering Meat color Standard color board Scoring model Fullrange Equivalent sample Rapid calibration Figures Figure 1 Figure 2 Figure 3 1 Introduction The pig industry plays a vital role in global meat production, which provides 33% of total meat consumption (Lebret & Candek-Potokar, 2022 ) and currently has become the second-largestconsumed meat worldwide. With the improvement of people’s living standards and consumption upgrades, the demand for high-quality pork has become an inevitable trend. Pork quality, a comprehensive concept, comprises a variety of attributes related to fresh pork and related processed products, while the quality of fresh pork traditionally can be evaluated by a set of properties, such as color, marbling, pH, drip loss, water-holding, tenderness, flavor, and odor (Purslow, 2017 ). Among these indicators, meat color is the paramount attribute that significantly influences consumer perception and purchase decisions (Gagaoua, Suman, Purslow, & Lebret, 2023 ), and discoloration can cause considerable economic losses (Ramanathan, 2022). Therefore, meat color has attracted wide attention from farms, slaughterhouses, retailers, and consumers, and the objective and rapid assessment of meat color has become increasingly important throughout the entire pig production process. Meat color measurement methodologies are mainly divided into two categories, namely visual assessment and instrumental measurement, the details on meat color measurement methods were completely reviewed in the American Meat Science Association (AMSA) Meat Color Measurement Guidelines ( http://www.meatscience.org ). The traditional colorimeter measurement is widely used for meat color evaluation and it is an objective meat color characterization method by collecting the feature parameters of L * (indicates lightness), a * (assesses redness), and b * (evaluates yellowness), hue angle ( H 0 , assess discoloration), and saturation index or chroma ( C *, assesses red color intensity), but the disadvantages of these colorimeters measurement are that the measured surface of meat must be uniform and the measurement area is rather small (ཞ2-5cm 2 ) (Kang, East, & Trujillo, 2008 ), and the grades of meat color cannot be obtained directly. Notably, a certain difference was existed in the feature parametersof color measured by various colorimeters (Wei et al., 2021 ), suggesting that the final output of meat color grades may be inconsistent in different instrumental measurement systems. Visual evaluations of meat color are the “fundamental standard” of color measurements because they closely relate to consumer perception evaluations and are the benchmark for instrumental measurement comparisons. Visual evaluation of meat color is influenced by a variety of conditions, such as type and angle of illumination, environmental differences, and human factors, but objective visual appraisals of meat color can be obtained by the well-trained panelists under a standardized condition (Ruedt, Gibis, & Weiss, 2023 ). Moreover, the standard reference materials, such as the standard board for color and marbling of pork constructed by the National Pork Producers Council (NPPC) in 1999 (NPPC, 1999 ), are usually used to enhance the reliability of visual meat color by subjective evaluation. In spite of this, visual sensory evaluation still has some unavoidable limitations, such as the low efficiency of evaluation by trained panelists or consumers, prone to visual fatigue, and the accuracy is often influenced by the sensitivity of the individual’s eyes. Recently, some novel technologies for meat quality evaluation have emerged, typically computer vision system (CVS) characterized by its obvious advantages, including rapid, consistent, objective, non-invasive, and economic (Wu & Sun, 2013 ), and the CVS has the advantage of providing more accurate measurements, closer to the real color, and these advantages have been validated in multiple species(Girolami, Napolitano, Faraone, & Braghieri, 2013 ; Milovanovic et al., 2019 ; Tomasevic, Tomovic, Ikonic, et al., 2019 ; Tomasevic, Tomovic, Milovanovic, et al., 2019 ), thus CVS is a promising method to obtain the real meat color values. It is particularly worth our attention that the rapid and accurate models for the evaluation of meat color scores are still lacking. Traditionally, the prediction models for the evaluation of meat color scores are developed based on large-scale samples using the feature parameters describing the meat color measured by instruments or other image features, and the performance of a model can be affected by the source of an animal population. For example, sun et al conducted a model development for meat color scores using 1,400 pigs from seven different plants, the classification accuracy of developed models in different pig populations varied from 75.0%-92.5% with an average accuracy of 78.9% (X. Sun, J. Young, J. H. Liu, & D. Newman, 2018), indicating the accuracy and robustness of the model constructed by the traditional method is still limited.To addressed the limitations inherent in traditional method for constructing color scoring model using actual pork, including a narrow scoring range of collected pork usually with 3.0 to 5.0 score, a large but unevenly distributed sample size, and the slope and intercept calibration significantly depend on the source of the pork and device, resulting in a challenging for the calibration and impractical applications, a novel method for rapidly constructing accurate pork color scoring models based on the NPPC standard color boards was proposed. The main contributions of this studyare summarized as follows: (1) Develop full-range coveragepork color scoring models based on the standard color board, and the slope of unbiased predictive model is independent of the sample source. (2) Fast intercept calibration of the model only depends a small of samples, thus making it easy to popularize in practice. (3) The pork color scoring models based on the standard color board achieve satisfactory classification accuracy, andthe classification ability is similar to the models developed by the traditional method. 2 Materials and Methods 2.1 Experimental design Three methods were used to develop thepork color scoring models, one was based onthe standard board images, the others were based on the actual pork images. The diagram of the above methods is shown in Fig. 1 . The steps were included in three parts. First, model construction involves establishing a regression equation between the feature parameters and the pork color scores. The modeling samples should cover the full range of pork color scores from 1.0 to 6.0 as comprehensively as possible. Second, model calibration refers to the rapid determination of the slope and intercept of the regression equation, with a clear understanding of their physical meanings. The samples for calibration should be as standardized and accessible as possible. In this study, the images of six pork color standard boards or a large number of actual pork samples wereused to determine the slope of the models, while five pork samples with a score of 3.5 were used for intercept calibration. Actually, using a small number of samples allows for quick calibration of the model parameters, facilitating its application in practice (Zhao et al., 2015 ). Third, model evaluation involves comparing the classification accuracy of pork color scoring models across different pig herds and color scores. This will help us analyze the factors affecting model performance and assess the applicability of the novel model construction method. The advantages of the model construction method based on the standard color boards are as follows: Modeling based on the standard color boards only use six color board images, with the advantages of color full-range coverage, balanced sample distribution, and easy slope determination. In contrast, modeling based on the actual pork images, with the disadvantages ofcoloroften concentrated on 3.0 to 5.0 score, with a large sample size and uneven distribution across color scores, and slope determination depends on a large sample size and is greatly influenced by the source of pig herd. This study focuses on comparing the accuracy and efficiency of the pork color scoring model construction method. It aims to propose a novel model construction method for pork color scoring based on the standard color board images, characterized by full-range coverage, uniform sample distribution, simple parameter calibration, and the slope determination of models is independent of pork sample source, thereby will be benefit to improve the standardization of automated pork color scoring. 2.2Experimental animals In this study, a total of 525 pigs (the information of them listed in Table 1 )involving in seven pig herds were slaughtered in three plants.Specifically, 294 Large White pigs(LW) and 89 Landrace × Large White pigs(LL) were from plant1, 32 Pietrain × Meishan pigs(PM), 10 Large White × Meishan pigs(LM), 3 Meishan pigs(Me), and 47 Suhuai(Su) pigs were from plant2, and 50 Duroc × (Landrace × Large White pigs)(DLL) were from plant3. All pigs were slaughtered in accordance with the national standards of the People's Republic of China (GB/T 17236 − 2019) in the standardized slaughterhouses. All protocols involving animals were approved by the Animal Protection and Utilization Committee of Nanjing Agricultural University (Certificate No.: Code: SYXK (Su) 2022-0031). Table 1 Basic information on experimental pigs and pork scores. Plants Plant1 Plant2 Plant3 Overall Herds LW LL Me LM Su PM DLL 7 Numbers 294 89 3 10 47 32 50 525 CS_Range 3.0–5.0 3.0-4.5 3.5-4.0 3.0–4.0 3.0–5.0 3.0-4.5 3.0–5.0 3.0–5.0 CS_Average 3.84 3.89 3.83 3.50 3.94 3.81 3.74 3.86 CS_Std 0.47 0.42 0.29 0.33 0.55 0.33 0.45 046 2.3Artificial colorscoring for actual pork Artificial scoring of actual pork color was conducted according to the agricultural industry standard of the People's Republic of China, Technical Procedures for Pork Quality Determination (NY/T821-2019). Briefly, approximately 2 cm thick cross-section of longissimus dorsi musclesat the third and fourth last thoracic ribs with an area of around 40 to 70 cm² werefirstly dissected from the left half of the carcassat 45 minutes after the pigs slaughtered.Then,the pork was placed on a panel with a white background, and the actual pork color were graded and recorded by afixed professional assessor according to the NPPC standard color board with a range of 1.0 to 6.0 grades, and 0.5 scale was allowed in the process of color grading, which was expressed as CS.The flowchart of artificial color scoring is shown in Fig. 2 . 2.4Images acquisition of actual pork and standard color boards The imagesof actual porkwere acquired immediately using the scanner after the artificialpork color scoringwas completed. Meanwhile, the images of standard color boards were collected in the same way as the images of the actual pork. The image acquisition process was shown in Fig. 3 (a1) and Fig. 3 (b1) . In this study,The EPSON V370 scanner with an LED light source with a color temperature of 6500Kwas used, and the resolution of the image acquisition was set as 400 dpi.To eliminate the influence of reflected light during scanning, the inside of the cover board was covered with a black photographic cloth.Moreover, the scanner was subjected to color calibration using the IT8 color card before images acquisition(Herdert, 1993 ). 2.5Color feature parameter extraction of actual pork and standard color board images The L *, a *, and b * values are feature parameters commonly used to evaluate color and are independent feature parameters in Lab color space. L *, a *, and b * indicate the lightness, red-green chromaticity, and blue-yellow chromaticity of the sample, respectively. To eliminates the influence of background and white adipose tissue at the edges of pork on the calculation of L *, a *, and b * values, the background of the images and white adipose tissue at the edges were removed usingthe MATLAB 2020 software.The process of image wasshown in Fig. 3 (a2) and Fig. 3 (b2) .Subsequently, the L *, a *, and b * values of the processed images derived from the standard color boards and the actual pork were calculated.In this study,theimages of six standard color boards and525 actual pork were processed to obtain the L *, a *, and b * values, which provides fundamental data for the construction of pork color scoring model. 2.6Construction of pork color scoring models Three methods were used to construct pork color scoring models as following. (1) The first method for constructing a pork color scoring model (CS_1)was based on the feature parameters of standard color board images.The ridge regression method in MATLAB software was used to construct the models using the feature parameters of the standard color board images as independent variable and the corresponding 1.0 ~ 6.0scoring values of the standard color boards as the dependent variable.When L *value was used in the model construction, the fitted equation wasdenoted as CS_1_L*; when L * and a * values were used in the model construction, the fitted equation wasdenoted as CS_1_L*a*; when L *, a *, and b * values were used in the model construction, the fitted equation wasdenoted as CS_1_L*a*b*. (2) The second method for constructing a pork color grading model(CS_2) was based on the feature parameters mean value of the actual pork images.Similarly, the ridge regression method in MATLAB software was used to construct the models using the feature parameters mean value of the actual pork images as independent variable and the mean grading value of the actual pork as the dependent variable. Specifically, the L *, a *, and b * feature parameters of all pork images were firstly classified according to the artificialgrades ranged from 1.0 to 6.0. Then, the meansof L *, a *, and b * of pork images at the same scores, as well as the corresponding artificial score, were used to construct the models. When the L * mean value was used in the model construction, the fitted equation was denoted as CS_2_L*; when the L * and a * mean values were used in the model construction, the fittedequation was denoted as CS_2_ L*a*; and when the L *, a *, and b *mean values were used in the model construction, the fittedequation were denoted as CS_2_L*a*b*. (3) The third method for constructing a pork color grading model (CS_3) was based on the feature parameters of actual pork images. Similarly, the ridge regression method in MATLAB software was used to construct the models using the feature parameter of the actual pork images as independent variable and the scoring values of the actual pork as the dependent variable. Specifically, the L *, a *, and b * feature parameters of each pork image, along with the corresponding artificial pork color scores, were used to construct the models. The model including the of L * feature parameter was denoted as CS_3_L*; The model including the of L * and a * feature parameters were denoted as CS_3_L*a*; whilethe model including the L *, a *, and b * feature parameters were denoted as CS_3_L*a*b*. In this study, the coefficient of determination (R²) of the fittedequations was used to evaluate the performance of models constructed using the three aforementioned methods. 2.7Intercepts calibration of the pork color scoring models and model evaluation In this study, five pork images with anartificial score of 3.5 were randomly selected to calibrate the intercepts of the fitted equations. Specifically, five pork images with anartificial score of 3.5 were processed according to the method described in Section 2.5 and their L *, a *, and b * feature parameter values were obtained. Then, these values were input into the fitted equations described in Section 2.6 to calculate the five intercepts for each equation. The average value of five intercepts was used as the calibrated intercept, and the intercepts in the fitted equations from Section 2.6 were replaced with the calibrated intercept to obtain the calibrated regression equations.Furthermore, the pork color scores of all the remaining pork images were predicted using the calibrated regression equations, and the classification accuracy was evaluated by comparing with the artificialpork color scores. Classification accuracy was assessed based on the thresholds of ≤ 0.25 and ≤ 0.50, andthe effects of different scores, herds on the classification accuracy of pork color scoring models were further evaluated. 3 Results and Discussion 3.1 Overview of the data In this study, 525 pigs were slaughtered in three plants, involving in seven pig herds. The artificial color scores of pork derived from the experimental pigs are ranged from 3.0 to 5.0, but slight differences are observed in different pig herds (Table 1 ). The most pork color score is clustered at 4.0, followed by 3.5 (Fig. 2 b). It shows that 9.90% of 3.0 score, 31.05% of 3.5 score, 43.05% of 4.0 score, 16.00% of 4.5 score, and 1.90% of 5.0 score. Among these pig herds, becausethe number of pigs of the LM and Me pigs wassmall, thus they were not used for subsequent classification accuracy analysis. 3.2 Performance evaluation of pork color scoring models calibrated by the mixed pig herd The performances of the pork color scoring models constructed using different image feature parameters are shown in Table 2 .It shows thata high level of performances were achieved only using the L * feature parameter based the standard board images, andperformance of the CS_1_L* model (R 2 =0.97) is comparable to that of CS_1_L*a* (R 2 ༝0.97)and CS_1_L*a*b* (R 2 ༝0.96), indicating that the performance of the models based the standard board images is less affected by the number of the image features parameters and L * feature parameter is critical for the performance of the models. Moreover, the resultsshow that the performancesof CS_2 models are consistent with those of CS_1 models, while the performances of CS_3modelsarefluctuated seriously with a R 2 ranged from 0.50 to 0.81 as the increasing of image features parameters,and the higher performance was observedin the CS_3_L*a*b* model (R 2 ༝0.81), indicating the performances of the models constructed using the image features parameters from the actual pork based the traditional method is significantly affected by the number of the feature parameter. Table 2 Performance comparison of the pork color scoring models based on different image feature parameters. Models Function 1 R 2 Function_Cal 2 CS_1_ L* CS=-0.175*L + 12.824 0.97 CS=-0.175*L + 11.332 CS_2_ L* CS=-0.207*L + 12.654 0.97 CS=-0.207*L + 12.764 CS_3_ L* CS=-0.132*L + 9.613 0.50 CS=-0.132*L + 9.407 CS_1_ L*a* CS=-0.145*L + 0.037*a + 10.213 0.97 CS=-0.145*L + 0.037*a + 9.590 CS_2_ L*a* CS=-0.156*L + 0.101*a + 9.368 0.99 CS=-0.156*L + 0.101*a + 9.392 CS_3_ L*a* CS=-0.113*L + 0.131*a + 7.156 0.79 CS=-0.113*L + 0.131*a + 7.144 CS_1_ L*a*b* CS=-0.086*L + 0.165*a-0.273*b + 8.742 0.96 CS=-0.086*L + 0.165*a-0.273*b + 7.868 CS_2_ L*a*b* CS=-0.136*L + 0.136*a-0.079*b + 8.774 0.99 CS=-0.136*L + 0.136*a-0.079*b + 8.785 CS_3_ L*a*b* CS=-0.106*L + 0.142*a-0.084*b + 7.439 0.81 CS=-0.106*L + 0.142*a-0.084*b + 7.419 Note: 1 : Pork color scoring equation fitted by ridge regression method using different image feature parameters. 2 : Calibrated pork color scoring equation fitted by ridge regression method using different image feature parameters; Models were calibrated by mixed pig herd. Due to the differences of image features between the pork standard color board and the actual pork images, so the models were further calibrated using the image feature parametersof the actual pork derived from the mixed pig herd. Notably, the intercepts of the CS_1 model constructed based on the standard board images were changed greatly after calibration, while no obvious changes were observed in the other models constructed based on the actual pork images (Table 2 ), indicating that it is necessary to calibrate the intercept using the actual pork images when applying the models based on the standard board image. Although the intercepts were not changed greatly after intercept calibration for the models constructed using the feature parametersofthe actual pork images, which does not mean that these models have the high-performance characteristic. On the contrary, the stability of these models constructed using the images features of the actual pork may be unsatisfied. For example, Sun et al found that the overall prediction accuracy was 78.9% of model constructed using the featuresof the pork from seven plants for pork color scores, while the prediction accuracies of the models wereranged from 75.0–92.5% in individual plant(Sun et al., 2018 ), indicating the performances of models developed were affected by many external factors.In this study, thenovel method proposed for constructing the CS_1 model hasseveral characteristics, including full range coverage, modeling with a small number of samples and balanced sample size, and the performance of the models is less affected by sample characteristics and ease to be calibrated with a small number of actual pork. 3.3Classification accuracy evaluation of the modelscalibrated by mixed pig herd at different pork color scores In this study, three methods were used to construct the pork color scoring models, three models for each method (Table 2 ). To test the predictive ability of these models, the classification accuracy of all the calibrated models were firstly evaluated at different pork color scoreswith a scoring scale of ≤ ± 0.25 and ≤ ± 0.50.The results shows that the overall classification accuracy with a scoring scale ≤ ± 0.50 is about 30% higher than that with a scoring scale ≤ ± 0.25 for each model (Table 3 and Table 4 ), that is because artificial pork colorscoring was performed based on a scale of ± 0.50. Notably, the high classification accuracy with a scoring scale of ≤ ± 0.50 was observed for most of the models, but the obvious differences of classification accuracy were observed at different pork color scores in a specific model (Table 4 ). Moreover, the highest classification accuracy was mainly observed at a score of 5.0, but the results need to be further validated by increasing the number of testingsamples.While,the stablyhighclassification accuracy was achieved at a score of 4.0for all the models. The main reason is that the samples are dominated in a score of 4.0. In addition, the classification accuracies of most models were improved as the increases of the image feature parameters,and the classification accuracy of the models based on the image feature parameters of the pork standard color board images have the similar predictive ability with the models based the image feature parameters of the actual pork images (Table 4 ). Table 3 Classification accuracy of the models calibrated by mixed pig herd within a scoring scale ≤ ± 0.25 at different pork color scores. Models Accuracy (%) Color Grade 3.0 3.5 4.0 4.5 5.0 Overall CS_1_ L* 28.30 53.25 74.47 58.90 70.00 61.33 CS_2_ L* 28.30 48.70 64.26 67.12 80.00 56.76 CS_3_ L* 22.64 63.64 71.06 17.81 10.00 55.43 CS_1_ L*a* 32.08 64.29 79.15 42.47 30.00 64.00 CS_2_ L*a* 54.72 62.34 75.74 72.60 80.00 69.33 CS_3_ L*a* 58.49 74.03 70.64 50.68 30.00 66.86 CS_1_ L*a*b* 52.83 60.39 53.19 47.95 40.00 54.29 CS_2_ L*a*b* 60.38 63.64 71.06 76.71 70.00 68.57 CS_3_ L*a*b* 56.60 70.13 67.23 46.58 20.00 63.24 Table 4 Classification accuracy of the models calibrated by mixed pig herd within a scoring scale ≤ ± 0.50 at different pork color scores. Models Accuracy (%) Color Grade 3.0 3.5 4.0 4.5 5.0 Overall CS_1_ L* 75.47 89.61 95.32 93.15 100.00 91.43 CS_2_ L* 73.58 82.47 91.91 97.26 100.00 88.19 CS_3_ L* 79.25 96.75 97.45 82.19 70.00 92.76 CS_1_ L*a* 84.91 96.75 97.87 93.15 100.00 95.62 CS_2_ L*a* 88.68 97.40 97.02 98.63 100.00 96.57 CS_3_ L*a* 90.57 100.00 99.57 94.52 90.00 97.90 CS_1_ L*a*b* 88.68 93.51 95.32 94.52 100.00 94.10 CS_2_ L*a*b* 90.57 98.05 97.87 100.00 100.00 97.52 CS_3_ L*a*b* 92.45 100.00 99.57 95.89 100.00 98.48 3.4Classification accuracy evaluation of the models calibratedby mixed pig herd ineach pig herd To explore the effect of different pig herds on the classification accuracy, the classification accuracy evaluation of the calibrated models was conducted in each pig herd. As shown Table 5 and Table 6 with a scoring scale of ≤ ± 0.25 and ≤ ± 0.50, respectively.Similarly, the results shows that the classification accuracieswith a scoring scale of ≤ ± 0.50areobviously higher than that with a scoring scale of ≤ ± 0.25 for each model. However, the classification accuraciesarefluctuated dramatically among the pig herds (Table 5 and Table 6 ). In the models constructed using the L * feature parameter, the classification accuraciesareranged from 65.96–96.94%; In the models constructed using the L * and a * feature parameters, the classification accuraciesareranged from 85.11–100.00%; In the models constructed usingthe L *, a * and b * feature parameters, the classification accuraciesareranged from 80.85–100.00%. Moreover, the overall highest classification accuracy was observed in the CS_3 models, but the CS_1 and CS_2 models have the comparative predictive ability with CS_3 models in a condition of the same method, and the CS_1 and CS_2 models show more similar predictive ability. Generally, these results indicate that the classification accuraciesfor all the models were significantly impacted by pig herds. Table 5 Classification accuracy of the models calibrated by mixed pig herd within a scoring scale ≤ ± 0.25 in different pig herds. Models Accuracy (%) Different pig herds LW LL PM Su DLL Overall CS_1_ L* 69.05 59.55 71.88 46.81 34.00 62.11 CS_2_ L* 69.05 51.69 68.75 27.66 22.00 57.62 CS_3_ L* 59.18 50.56 65.63 51.06 48.00 56.25 CS_1_ L*a* 71.77 59.55 75.00 46.81 44.00 64.84 CS_2_ L*a* 82.99 69.66 31.25 48.94 42.00 70.31 CS_3_ L*a* 70.41 71.91 34.38 65.96 62.00 67.19 CS_1_ L*a*b* 56.46 62.92 53.13 34.04 42.00 53.91 CS_2_ L*a*b* 79.93 74.16 31.25 44.68 42.00 68.95 CS_3_ L*a*b* 67.69 68.54 43.75 48.94 56.00 63.48 Note: The sample sizes of the LM and Me pig populations are small and do not have statistical significance, thus the pig herds for accuracies analysis were excluded. Table 6 Classification accuracy of the models calibrated by mixed pig herd within a scoring scale ≤ ± 0.50 in different pig herds. Models Accuracy (%) Different pig herds LW LL PM Su DLL Overall CS_1_ L* 96.94 89.89 96.88 76.60 74.00 91.60 CS_2_ L* 95.92 88.76 90.63 65.96 66.00 88.67 CS_3_ L* 94.56 89.89 96.88 85.11 92.00 92.77 CS_1_ L*a* 97.96 91.01 96.88 89.36 96.00 95.70 CS_2_ L*a* 99.66 93.26 96.88 85.11 96.00 96.68 CS_3_ L*a* 98.98 95.51 100.00 91.49 98.00 97.66 CS_1_ L*a*b* 95.24 95.51 90.63 80.85 96.00 93.75 CS_2_ L*a*b* 99.66 100.00 96.88 85.11 94.00 97.66 CS_3_ L*a*b* 98.98 96.63 100.00 93.62 98.00 98.05 Note: The sample sizes of the LM and Me pig populations are small and do not have statistical significance, thus the pig herd for accuracies analysis of were excluded. 3.5Classification accuracy evaluationof the models calibrated by individual pig herd in each pig herd Due to the classification accuracy of the modelsis affected by the pig herds, so the calibration of the models using the image features of the actual pork derived from the individual pig herd was subsequently performed and the classification accuracies of these calibrated modelsin each pig herd were evaluated.As shown in Table 7 ,the calibrated intercepts were different in each pig herd for the models. Notably, when compared with the calibrated intercepts in Table 2 , the greater the change of calibrated intercepts (Table 7 ), the greater the corresponding classification accuracy will be improved for a specific modelwith a scoring scale of ≤ ± 0.25or ≤ ± 0.5 (Table 5 vs. Table 8 , Table 6 and Table 9 ). As shown in Table 9 , the overall classification accuracies of all the models are improved greatly, and the classification accuracies of all the models constructed using the L *, a * and b * feature parameters reach more than 96.00% and the models constructed by the three methods shows the similar predictive ability. These results indicate that the classification accuracies of all the models can be improved by the calibration of intercept, and a much larger improvement can be achieved using the novelmodel construction method based on the pork color standard boar proposed in this study compared to the traditional method, and the similar predictive ability of the models constructed by the novel method can be obtained after the intercept calibration compared to the models constructed by the traditional method. Table 7 Intercept calibration of pork color scoring models using the feature parameters of actual pork from individual pig herd. Models Intercepts Different pig herds LW LL PM Su DLL CS_1_ L* 11.276 11.465 11.120 11.098 10.830 CS_2_ L* 12.698 12.921 12.513 12.488 12.170 CS_3_ L* 9.365 9.508 9.248 9.231 9.029 CS_1_ L*a* 9.577 9.691 9.367 9.419 9.204 CS_2_ L*a* 9.434 9.485 9.073 9.244 9.026 CS_3_ L*a* 7.227 7.196 6.839 7.071 6.925 CS_1_ L*a*b* 7.959 7.984 7.776 7.552 7.565 CS_2_ L*a*b* 8.856 8.880 8.510 8.597 8.449 CS_3_ L*a*b* 7.505 7.492 7.177 7.270 7.172 Table 8 Classification accuracy of models calibrated by individual pig herd within a scoring scale ≤ ± 0.25 in each pig herd. Models Accuracy (%) Different pig herds LW LL PM Su DLL Overall CS_1_ L* 65.99 55.06 68.75 55.32 64.00 63.09 CS_2_ L* 66.33 48.31 68.75 44.68 64.00 61.13 CS_3_ L* 60.88 57.30 62.50 48.94 60.00 59.18 CS_1_ L*a* 71.09 69.66 81.25 61.70 64.00 69.92 CS_2_ L*a* 80.61 71.91 81.25 59.57 68.00 75.97 CS_3_ L*a* 76.53 75.28 87.50 63.83 68.00 75.00 CS_1_ L*a*b* 64.63 75.28 65.63 65.96 74.00 67.58 CS_2_ L*a*b* 78.57 76.40 87.50 63.83 68.00 76.37 CS_3_ L*a*b* 78.23 79.78 87.50 65.96 74.00 77.54 Table 9 Classification accuracy of models calibrated by individual pig herd within a scoring scale ≤ ± 0.50 in each pig herd. Models Accuracy (%) Different pig herds LW LL PM Su DLL Overall CS_1_ L* 95.92 93.26 93.75 80.85 94.00 93.75 CS_2_ L* 96.26 88.76 93.75 78.72 98.00 93.36 CS_3_ L* 93.20 93.26 93.75 78.72 94.00 91.99 CS_1_ L*a* 98.30 95.51 96.88 82.98 94.00 95.90 CS_2_ L*a* 99.32 97.75 100.00 91.49 98.00 98.24 CS_3_ L*a* 98.64 98.88 100.00 91.49 94.00 97.66 CS_1_ L*a*b* 95.24 100.00 96.88 93.62 96.00 96.10 CS_2_ L*a*b* 98.98 100.00 100.00 93.62 96.00 98.44 CS_3_ L*a*b* 98.98 100.00 100.00 93.62 98.00 98.63 4 Conclusion In this study, a novel method for the rapid construction of a precise color scoring models based on the pork colorstandard board imageswere proposed, andthe classification accuracies of the models developed by this novel method are comparable to the models constructed by the traditional method across all pork color scores. For example, under a scoring scale of ≤ ± 0.50, all the overall classification accuracies of all the models calibrated by mixed pig herd based on L *, a * and b * feature parameters reaches more than94.00%. In the porkcolor scoring models, the evaluable range of models is influenced by the pork color scoring range of the samples used to develop the models. However,most of the pork collected were distributed between 3.5 and 4.0 color score in this study, with an overall range of 3.0 to 5.0 color score.In contrast, a completed distribution of 1.0 to 6.0 colorscore can be provided by the pork colorstandard boards. Therefore, the color scoring models constructed based on the pork colorstandard boardscan be adapted to the full range of pork color evaluation.Moreover,the results showed that the models achievedthe higher classification accuracy for pork color scorein a condition of large number of samples than that in a condition of small number of samples. CS_1 model was developed only using the feature parameters of six pork colorstandard board images and the intercepts were calibrated only using five actual pork, while525pork was needed to determine the slopeand intercept of the CS_2 and CS_3 models. Therefore, the novel method proposed for the construction of CS_1 model exhibitsthe advantages of simple model construction and fast parameter calibration. The pig herd is critical factor affecting the classification accuracy of the models. The results show that theoverall classification accuracies of aresignificantly improved in a scoring scale of ≤ ± 0.25 and ≤ ± 0.50by the intercept calibration using the feature parameters of the actual pork images derived from individual pig herd,and the differences of the classification accuracy among pig herds is reduced obviously.Moreover, the results show that a relatively small improvement of classification accuracieswere observed a scoring scale of ≤ ± 0.50 when compared to the scoring scale of ≤ ± 0.25 after intercept calibration in different herds,that is because the relatively high classification accuracy of the models already were existed an uncalibrated model. Together, the intercept calibrationis an effective method to enhance the classification accuracy and it should be performed for individual pig breed. In this study, most of pork collected were concentrated in the 3.0 to 4.5 color score, the pork with the color scoresat 2.0 or below and 5.0 or above are not enough. In the future, it will be necessary to increase the amount of pork with the color scores of 2.0 or below and 5.0 or above to improve the data set, andfurther evaluate the applicability of color scoring models for specific scoring levels. Declarations 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 paper. Data availability Data will be made available on request. References Gagaoua, M., Suman, S. P., Purslow, P. P., & Lebret, B. (2023). The color of fresh pork: Consumers expectations, underlying plant-to-fork factors, myoglobin chemistry and contribution of proteomics to decipher the biochemical mechanisms. Meat Science, 206. doi:ARTN 10934010.1016/j.meatsci.2023.109340 Girolami, A., Napolitano, F., Faraone, D., & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111-118. doi:10.1016/j.meatsci.2012.08.010 Herdert, f. (1993). Cie color space and it8 at work - a quantitative-analysis of color matching from scanner to print in a production environment. Paper presented at the device-independent color imaging and imaging systems integration. Kang, S. P., East, A. R., & Trujillo, F. J. (2008). Colour vision system evaluation of bicolour fruit: A case study with 'B74' mango. Postharvest Biology and Technology, 49(1), 77-85. doi:10.1016/j.postharvbio.2007.12.011 Lebret, B., & Candek-Potokar, M. (2022). Review: Pork quality attributes from plant to fork. Part I. Carcass and fresh meat. Animal, 16. doi:ARTN 10040210.1016/j.animal.2021.100402 Milovanovic, B., Djekic, I., Djordjevic, V., Tomovic, V., Barba, F., Tomasevic, I., & Lorenzo, J. M. (2019). Pros and cons of using a computer vision system for color evaluation of meat and meat products. 60th International Meat Industry Conference Meatcon2019, 333. doi:Artn 01200810.1088/1755-1315/333/1/012008 NPPC. (1999). Official Color and Marbling Standards. In. Des Moines, IA, USA: The Pork Producers Council. Purslow, P. P. (2017). Chapter 1 - Introduction. In P. P. Purslow (Ed.), New Aspects of Meat Quality (pp. 1-9): Woodhead Publishing. Ruedt, C., Gibis, M., & Weiss, J. (2023). Meat color and iridescence: Origin, analysis, and approaches to modulation. Comprehensive Reviews in Food Science and Food Safety, 22(4), 3366-3394. doi:10.1111/1541-4337.13191 Sun, X., Young, J., Liu, J. H., & Newman, D. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Science, 140, 72-77. doi:10.1016/j.meatsci.2018.03.005 Tomasevic, I., Tomovic, V., Ikonic, P., Rodriguez, J. M. L., Barba, F. J., Djekic, I., Zivkovic, D. (2019). Evaluation of poultry meat colour using computer vision system and colourimeter Is there a difference? British Food Journal, 121(5), 1078-1087. doi:10.1108/Bfj-06-2018-0376 Tomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J., Dordevic, V., Karabasil, N., & Djekic, I. (2019). Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Science, 148, 5-12. doi:10.1016/j.meatsci.2018.09.015 Wei, X. Y., Lam, S., Bohrer, B. M., Uttaro, B., López-Campos, O., Prieto, N., Juárez, M. (2021). A Comparison of Fresh Pork Colour Measurements by Using Four Commercial Handheld Devices. Foods, 10(11). doi:ARTN 251510.3390/foods10112515 Wu, D., & Sun, D. W. (2013). Colour measurements by computer vision for food quality control - A review. Trends in Food Science & Technology, 29(1), 5-20. doi:10.1016/j.tifs.2012.08.004 Zhao, S., Gu, J., Zhao, Y., Hassan, M., Li, Y., & Ding, W. (2015). A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model. Scientific Reports, 5(1), 16241. doi:10.1038/srep16241 Additional Declarations There is NO Competing Interest. 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-5504020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":386107289,"identity":"cc9ffacf-9d0e-4496-bc3e-932265810c3e","order_by":0,"name":"Sanqin zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCSjNxsB8AMpJIFoLWwKJWhgYeAygDAJa5Gc3P3z4o+aObJ90z7cHFjWHGfjZcwwYfu7ArYVxzjFjY55jz4zbZM5uN5A4dphBsueNAWPvGdxamCUSzKQZ2A4ntknkbpOQYDvMYHAjx4CZsQ23FjaJ9G+SP/6BtOQ8k5D4d5jBnpAWHokcMwneNrAWNgnJNqAtEgS0SEjkFBvz9h02bpNIM5OQ7EvnkTjzrOBgLx4t8jPSNz788e2w7PwZyc+kJb5Zy/G3J2988BOPFhhgbGAAhQXQpSDeAcIaoFoYPxCjdBSMglEwCkYcAAD4V0wImpXrKwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Sanqin","middleName":"","lastName":"zhao","suffix":""},{"id":386107290,"identity":"8817f2f1-1c12-47fe-ae4f-ce70ea265ab2","order_by":1,"name":"Dongsheng Xiao","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Xiao","suffix":""},{"id":386107291,"identity":"f2e27514-8ef4-4860-8395-6c4a1ec9a19d","order_by":2,"name":"Jiabing Gu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiabing","middleName":"","lastName":"Gu","suffix":""},{"id":386107292,"identity":"de6f89ff-e3d0-44b3-a09a-f9a42000e1eb","order_by":3,"name":"Yongkang Li","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yongkang","middleName":"","lastName":"Li","suffix":""},{"id":386107293,"identity":"127adca8-b026-4abc-a7e8-905229819ff4","order_by":4,"name":"Zhe Chao","email":"","orcid":"","institution":"Hainan Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Chao","suffix":""},{"id":386107294,"identity":"65ee8a8e-559f-47f0-baba-e30cb84bf0a1","order_by":5,"name":"Wen Yang","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Yang","suffix":""},{"id":386107295,"identity":"b66c50d9-8a95-4a4e-a3af-96349270640f","order_by":6,"name":"Zi Meng","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zi","middleName":"","lastName":"Meng","suffix":""},{"id":386107296,"identity":"77c67d49-2ca6-44c1-b2cc-89373f9326e9","order_by":7,"name":"Chengwan Zha","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Chengwan","middleName":"","lastName":"Zha","suffix":""},{"id":386107297,"identity":"1b59b004-2ce1-4bb1-a3e4-585503004d47","order_by":8,"name":"Wangjun Wu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wangjun","middleName":"","lastName":"Wu","suffix":""},{"id":386107298,"identity":"44d49a9a-0846-49e4-94e7-a1a34571f0f1","order_by":9,"name":"Yutao Liu","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yutao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-11-22 10:55:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5504020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5504020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78883031,"identity":"b3fdfacc-0b11-4297-a1d1-69ff5184b241","added_by":"auto","created_at":"2025-03-20 09:08:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317569,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic diagram of the methodology for rapid construction of precise pork color scoring model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5504020/v1/a87c2a6c09de3a5ee7213787.png"},{"id":78883028,"identity":"88492054-ec14-4f41-9cef-bfd277763da0","added_by":"auto","created_at":"2025-03-20 09:08:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98374,"visible":true,"origin":"","legend":"\u003cp\u003ePork collection and artificial color scoring. (a) Flowchart of pork collection, (b) Flowchart of artificial color scoring and histogram of color score distribution.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5504020/v1/df7beae0064b568165cfa607.png"},{"id":78883046,"identity":"0abd3a32-7e9c-4863-af7c-e8c2092bfef0","added_by":"auto","created_at":"2025-03-20 09:08:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250078,"visible":true,"origin":"","legend":"\u003cp\u003eAcquisition and processing of image. (a1) Flowchart of color standard board image acquisition, (b1) Flowchart of pork image acquisition, (a2) Color standard board image processing procedure, (b2) Actual pork image processing procedure.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5504020/v1/56f5a7b8be97f68826ed9afb.png"},{"id":78884021,"identity":"8e87fe1a-145e-4109-b23e-2f4bd8ab4acb","added_by":"auto","created_at":"2025-03-20 09:16:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1996260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5504020/v1/4986fdfb-0d97-4b95-8838-892ea5623fb5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe pig industry plays a vital role in global meat production, which provides 33% of total meat consumption (Lebret \u0026amp; Candek-Potokar, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and currently has become the second-largestconsumed meat worldwide. With the improvement of people\u0026rsquo;s living standards and consumption upgrades, the demand for high-quality pork has become an inevitable trend. Pork quality, a comprehensive concept, comprises a variety of attributes related to fresh pork and related processed products, while the quality of fresh pork traditionally can be evaluated by a set of properties, such as color, marbling, pH, drip loss, water-holding, tenderness, flavor, and odor (Purslow, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these indicators, meat color is the paramount attribute that significantly influences consumer perception and purchase decisions (Gagaoua, Suman, Purslow, \u0026amp; Lebret, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and discoloration can cause considerable economic losses (Ramanathan, 2022). Therefore, meat color has attracted wide attention from farms, slaughterhouses, retailers, and consumers, and the objective and rapid assessment of meat color has become increasingly important throughout the entire pig production process.\u003c/p\u003e \u003cp\u003eMeat color measurement methodologies are mainly divided into two categories, namely visual assessment and instrumental measurement, the details on meat color measurement methods were completely reviewed in the American Meat Science Association (AMSA) Meat Color Measurement Guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.meatscience.org\u003c/span\u003e\u003cspan address=\"http://www.meatscience.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The traditional colorimeter measurement is widely used for meat color evaluation and it is an objective meat color characterization method by collecting the feature parameters of \u003cem\u003eL\u003c/em\u003e* (indicates lightness), \u003cem\u003ea\u003c/em\u003e* (assesses redness), and \u003cem\u003eb\u003c/em\u003e* (evaluates yellowness), hue angle (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e0\u003c/sup\u003e, assess discoloration), and saturation index or chroma (\u003cem\u003eC\u003c/em\u003e*, assesses red color intensity), but the disadvantages of these colorimeters measurement are that the measured surface of meat must be uniform and the measurement area is rather small (ཞ2-5cm\u003csup\u003e2\u003c/sup\u003e) (Kang, East, \u0026amp; Trujillo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and the grades of meat color cannot be obtained directly. Notably, a certain difference was existed in the feature parametersof color measured by various colorimeters (Wei et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that the final output of meat color grades may be inconsistent in different instrumental measurement systems.\u003c/p\u003e \u003cp\u003eVisual evaluations of meat color are the \u0026ldquo;fundamental standard\u0026rdquo; of color measurements because they closely relate to consumer perception evaluations and are the benchmark for instrumental measurement comparisons. Visual evaluation of meat color is influenced by a variety of conditions, such as type and angle of illumination, environmental differences, and human factors, but objective visual appraisals of meat color can be obtained by the well-trained panelists under a standardized condition (Ruedt, Gibis, \u0026amp; Weiss, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the standard reference materials, such as the standard board for color and marbling of pork constructed by the National Pork Producers Council (NPPC) in 1999 (NPPC, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), are usually used to enhance the reliability of visual meat color by subjective evaluation. In spite of this, visual sensory evaluation still has some unavoidable limitations, such as the low efficiency of evaluation by trained panelists or consumers, prone to visual fatigue, and the accuracy is often influenced by the sensitivity of the individual\u0026rsquo;s eyes.\u003c/p\u003e \u003cp\u003eRecently, some novel technologies for meat quality evaluation have emerged, typically computer vision system (CVS) characterized by its obvious advantages, including rapid, consistent, objective, non-invasive, and economic (Wu \u0026amp; Sun, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and the CVS has the advantage of providing more accurate measurements, closer to the real color, and these advantages have been validated in multiple species(Girolami, Napolitano, Faraone, \u0026amp; Braghieri, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Milovanovic et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tomasevic, Tomovic, Ikonic, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tomasevic, Tomovic, Milovanovic, et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), thus CVS is a promising method to obtain the real meat color values. It is particularly worth our attention that the rapid and accurate models for the evaluation of meat color scores are still lacking. Traditionally, the prediction models for the evaluation of meat color scores are developed based on large-scale samples using the feature parameters describing the meat color measured by instruments or other image features, and the performance of a model can be affected by the source of an animal population. For example, sun et al conducted a model development for meat color scores using 1,400 pigs from seven different plants, the classification accuracy of developed models in different pig populations varied from 75.0%-92.5% with an average accuracy of 78.9% (X. Sun, J. Young, J. H. Liu, \u0026amp; D. Newman, 2018), indicating the accuracy and robustness of the model constructed by the traditional method is still limited.To addressed the limitations inherent in traditional method for constructing color scoring model using actual pork, including a narrow scoring range of collected pork usually with 3.0 to 5.0 score, a large but unevenly distributed sample size, and the slope and intercept calibration significantly depend on the source of the pork and device, resulting in a challenging for the calibration and impractical applications, a novel method for rapidly constructing accurate pork color scoring models based on the NPPC standard color boards was proposed. The main contributions of this studyare summarized as follows:\u003c/p\u003e \u003cp\u003e(1) Develop full-range coveragepork color scoring models based on the standard color board, and the slope of unbiased predictive model is independent of the sample source.\u003c/p\u003e \u003cp\u003e(2) Fast intercept calibration of the model only depends a small of samples, thus making it easy to popularize in practice.\u003c/p\u003e \u003cp\u003e (3) The pork color scoring models based on the standard color board achieve satisfactory classification accuracy, andthe classification ability is similar to the models developed by the traditional method.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental design\u003c/h2\u003e \u003cp\u003e Three methods were used to develop thepork color scoring models, one was based onthe standard board images, the others were based on the actual pork images. The diagram of the above methods is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The steps were included in three parts. First, model construction involves establishing a regression equation between the feature parameters and the pork color scores. The modeling samples should cover the full range of pork color scores from 1.0 to 6.0 as comprehensively as possible. Second, model calibration refers to the rapid determination of the slope and intercept of the regression equation, with a clear understanding of their physical meanings. The samples for calibration should be as standardized and accessible as possible. In this study, the images of six pork color standard boards or a large number of actual pork samples wereused to determine the slope of the models, while five pork samples with a score of 3.5 were used for intercept calibration. Actually, using a small number of samples allows for quick calibration of the model parameters, facilitating its application in practice (Zhao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Third, model evaluation involves comparing the classification accuracy of pork color scoring models across different pig herds and color scores. This will help us analyze the factors affecting model performance and assess the applicability of the novel model construction method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe advantages of the model construction method based on the standard color boards are as follows: Modeling based on the standard color boards only use six color board images, with the advantages of color full-range coverage, balanced sample distribution, and easy slope determination. In contrast, modeling based on the actual pork images, with the disadvantages ofcoloroften concentrated on 3.0 to 5.0 score, with a large sample size and uneven distribution across color scores, and slope determination depends on a large sample size and is greatly influenced by the source of pig herd.\u003c/p\u003e \u003cp\u003eThis study focuses on comparing the accuracy and efficiency of the pork color scoring model construction method. It aims to propose a novel model construction method for pork color scoring based on the standard color board images, characterized by full-range coverage, uniform sample distribution, simple parameter calibration, and the slope determination of models is independent of pork sample source, thereby will be benefit to improve the standardization of automated pork color scoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Experimental animals\u003c/h2\u003e \u003cp\u003eIn this study, a total of 525 pigs (the information of them listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)involving in seven pig herds were slaughtered in three plants.Specifically, 294 Large White pigs(LW) and 89 Landrace \u0026times; Large White pigs(LL) were from plant1, 32 Pietrain \u0026times; Meishan pigs(PM), 10 Large White \u0026times; Meishan pigs(LM), 3 Meishan pigs(Me), and 47 Suhuai(Su) pigs were from plant2, and 50 Duroc \u0026times; (Landrace \u0026times; Large White pigs)(DLL) were from plant3. All pigs were slaughtered in accordance with the national standards of the People's Republic of China (GB/T 17236\u0026thinsp;\u0026minus;\u0026thinsp;2019) in the standardized slaughterhouses. All protocols involving animals were approved by the Animal Protection and Utilization Committee of Nanjing Agricultural University (Certificate No.: Code: SYXK (Su) 2022-0031).\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\u003eBasic information on experimental pigs and pork scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePlant1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003ePlant2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlant3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5-4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u0026ndash;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.0\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.0\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_Std\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e046\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3Artificial colorscoring for actual pork\u003c/h2\u003e \u003cp\u003eArtificial scoring of actual pork color was conducted according to the agricultural industry standard of the People's Republic of China, Technical Procedures for Pork Quality Determination (NY/T821-2019). Briefly, approximately 2 cm thick cross-section of \u003cem\u003elongissimus dorsi\u003c/em\u003e musclesat the third and fourth last thoracic ribs with an area of around 40 to 70 cm\u0026sup2; werefirstly dissected from the left half of the carcassat 45 minutes after the pigs slaughtered.Then,the pork was placed on a panel with a white background, and the actual pork color were graded and recorded by afixed professional assessor according to the NPPC standard color board with a range of 1.0 to 6.0 grades, and 0.5 scale was allowed in the process of color grading, which was expressed as CS.The flowchart of artificial color scoring is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4Images acquisition of actual pork and standard color boards\u003c/h2\u003e \u003cp\u003eThe imagesof actual porkwere acquired immediately using the scanner after the artificialpork color scoringwas completed. Meanwhile, the images of standard color boards were collected in the same way as the images of the actual pork. The image acquisition process was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(a1) and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(b1)\u003c/b\u003e. In this study,The EPSON V370 scanner with an LED light source with a color temperature of 6500Kwas used, and the resolution of the image acquisition was set as 400 dpi.To eliminate the influence of reflected light during scanning, the inside of the cover board was covered with a black photographic cloth.Moreover, the scanner was subjected to color calibration using the IT8 color card before images acquisition(Herdert, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5Color feature parameter extraction of actual pork and standard color board images\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* values are feature parameters commonly used to evaluate color and are independent feature parameters in Lab color space. \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* indicate the lightness, red-green chromaticity, and blue-yellow chromaticity of the sample, respectively. To eliminates the influence of background and white adipose tissue at the edges of pork on the calculation of \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* values, the background of the images and white adipose tissue at the edges were removed usingthe MATLAB 2020 software.The process of image wasshown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(a2) and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(b2)\u003c/b\u003e.Subsequently, the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* values of the processed images derived from the standard color boards and the actual pork were calculated.In this study,theimages of six standard color boards and525 actual pork were processed to obtain the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* values, which provides fundamental data for the construction of pork color scoring model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6Construction of pork color scoring models\u003c/h2\u003e \u003cp\u003eThree methods were used to construct pork color scoring models as following.\u003c/p\u003e \u003cp\u003e(1) The first method for constructing a pork color scoring model (CS_1)was based on the feature parameters of standard color board images.The ridge regression method in MATLAB software was used to construct the models using the feature parameters of the standard color board images as independent variable and the corresponding 1.0\u0026thinsp;~\u0026thinsp;6.0scoring values of the standard color boards as the dependent variable.When\u003cem\u003eL\u003c/em\u003e*value was used in the model construction, the fitted equation wasdenoted as CS_1_L*; when \u003cem\u003eL\u003c/em\u003e* and \u003cem\u003ea\u003c/em\u003e* values were used in the model construction, the fitted equation wasdenoted as CS_1_L*a*; when \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* values were used in the model construction, the fitted equation wasdenoted as CS_1_L*a*b*.\u003c/p\u003e \u003cp\u003e(2) The second method for constructing a pork color grading model(CS_2) was based on the feature parameters mean value of the actual pork images.Similarly, the ridge regression method in MATLAB software was used to construct the models using the feature parameters mean value of the actual pork images as independent variable and the mean grading value of the actual pork as the dependent variable. Specifically, the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* feature parameters of all pork images were firstly classified according to the artificialgrades ranged from 1.0 to 6.0. Then, the meansof\u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* of pork images at the same scores, as well as the corresponding artificial score, were used to construct the models. When the\u003cem\u003eL\u003c/em\u003e* mean value was used in the model construction, the fitted equation was denoted as CS_2_L*; when the \u003cem\u003eL\u003c/em\u003e* and \u003cem\u003ea\u003c/em\u003e* mean values were used in the model construction, the fittedequation was denoted as CS_2_ L*a*; and when the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e*mean values were used in the model construction, the fittedequation were denoted as CS_2_L*a*b*.\u003c/p\u003e \u003cp\u003e(3) The third method for constructing a pork color grading model (CS_3) was based on the feature parameters of actual pork images. Similarly, the ridge regression method in MATLAB software was used to construct the models using the feature parameter of the actual pork images as independent variable and the scoring values of the actual pork as the dependent variable. Specifically, the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* feature parameters of each pork image, along with the corresponding artificial pork color scores, were used to construct the models. The model including the of \u003cem\u003eL\u003c/em\u003e* feature parameter was denoted as CS_3_L*; The model including the of \u003cem\u003eL\u003c/em\u003e* and \u003cem\u003ea\u003c/em\u003e* feature parameters were denoted as CS_3_L*a*; whilethe model including the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* feature parameters were denoted as CS_3_L*a*b*.\u003c/p\u003e \u003cp\u003eIn this study, the coefficient of determination (R\u0026sup2;) of the fittedequations was used to evaluate the performance of models constructed using the three aforementioned methods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7Intercepts calibration of the pork color scoring models and model evaluation\u003c/h2\u003e \u003cp\u003eIn this study, five pork images with anartificial score of 3.5 were randomly selected to calibrate the intercepts of the fitted equations. Specifically, five pork images with anartificial score of 3.5 were processed according to the method described in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e and their \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e*, and \u003cem\u003eb\u003c/em\u003e* feature parameter values were obtained. Then, these values were input into the fitted equations described in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e to calculate the five intercepts for each equation. The average value of five intercepts was used as the calibrated intercept, and the intercepts in the fitted equations from Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e were replaced with the calibrated intercept to obtain the calibrated regression equations.Furthermore, the pork color scores of all the remaining pork images were predicted using the calibrated regression equations, and the classification accuracy was evaluated by comparing with the artificialpork color scores. Classification accuracy was assessed based on the thresholds of \u0026le;\u0026thinsp;0.25 and \u0026le;\u0026thinsp;0.50, andthe effects of different scores, herds on the classification accuracy of pork color scoring models were further evaluated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview of the data\u003c/h2\u003e \u003cp\u003eIn this study, 525 pigs were slaughtered in three plants, involving in seven pig herds. The artificial color scores of pork derived from the experimental pigs are ranged from 3.0 to 5.0, but slight differences are observed in different pig herds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most pork color score is clustered at 4.0, followed by 3.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). It shows that 9.90% of 3.0 score, 31.05% of 3.5 score, 43.05% of 4.0 score, 16.00% of 4.5 score, and 1.90% of 5.0 score. Among these pig herds, becausethe number of pigs of the LM and Me pigs wassmall, thus they were not used for subsequent classification accuracy analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Performance evaluation of pork color scoring models calibrated by the mixed pig herd\u003c/h2\u003e \u003cp\u003eThe performances of the pork color scoring models constructed using different image feature parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.It shows thata high level of performances were achieved only using the \u003cem\u003eL\u003c/em\u003e* feature parameter based the standard board images, andperformance of the CS_1_L* model (R\u003csup\u003e2\u003c/sup\u003e=0.97) is comparable to that of CS_1_L*a* (R\u003csup\u003e2\u003c/sup\u003e༝0.97)and CS_1_L*a*b* (R\u003csup\u003e2\u003c/sup\u003e༝0.96), indicating that the performance of the models based the standard board images is less affected by the number of the image features parameters and \u003cem\u003eL\u003c/em\u003e* feature parameter is critical for the performance of the models. Moreover, the resultsshow that the performancesof CS_2 models are consistent with those of CS_1 models, while the performances of CS_3modelsarefluctuated seriously with a R\u003csup\u003e2\u003c/sup\u003eranged from 0.50 to 0.81 as the increasing of image features parameters,and the higher performance was observedin the CS_3_L*a*b* model (R\u003csup\u003e2\u003c/sup\u003e༝0.81), indicating the performances of the models constructed using the image features parameters from the actual pork based the traditional method is significantly affected by the number of the feature parameter.\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\u003ePerformance comparison of the pork color scoring models based on different image feature parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFunction\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction_Cal\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.175*L\u0026thinsp;+\u0026thinsp;12.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.175*L\u0026thinsp;+\u0026thinsp;11.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.207*L\u0026thinsp;+\u0026thinsp;12.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.207*L\u0026thinsp;+\u0026thinsp;12.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.132*L\u0026thinsp;+\u0026thinsp;9.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.132*L\u0026thinsp;+\u0026thinsp;9.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.145*L\u0026thinsp;+\u0026thinsp;0.037*a\u0026thinsp;+\u0026thinsp;10.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.145*L\u0026thinsp;+\u0026thinsp;0.037*a\u0026thinsp;+\u0026thinsp;9.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.156*L\u0026thinsp;+\u0026thinsp;0.101*a\u0026thinsp;+\u0026thinsp;9.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.156*L\u0026thinsp;+\u0026thinsp;0.101*a\u0026thinsp;+\u0026thinsp;9.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.113*L\u0026thinsp;+\u0026thinsp;0.131*a\u0026thinsp;+\u0026thinsp;7.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.113*L\u0026thinsp;+\u0026thinsp;0.131*a\u0026thinsp;+\u0026thinsp;7.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.086*L\u0026thinsp;+\u0026thinsp;0.165*a-0.273*b\u0026thinsp;+\u0026thinsp;8.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.086*L\u0026thinsp;+\u0026thinsp;0.165*a-0.273*b\u0026thinsp;+\u0026thinsp;7.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.136*L\u0026thinsp;+\u0026thinsp;0.136*a-0.079*b\u0026thinsp;+\u0026thinsp;8.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.136*L\u0026thinsp;+\u0026thinsp;0.136*a-0.079*b\u0026thinsp;+\u0026thinsp;8.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS=-0.106*L\u0026thinsp;+\u0026thinsp;0.142*a-0.084*b\u0026thinsp;+\u0026thinsp;7.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS=-0.106*L\u0026thinsp;+\u0026thinsp;0.142*a-0.084*b\u0026thinsp;+\u0026thinsp;7.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e: Pork color scoring equation fitted by ridge regression method using different image feature parameters.\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003e: Calibrated pork color scoring equation fitted by ridge regression method using different image feature parameters; Models were calibrated by mixed pig herd.\u003c/p\u003e \u003cp\u003e Due to the differences of image features between the pork standard color board and the actual pork images, so the models were further calibrated using the image feature parametersof the actual pork derived from the mixed pig herd. Notably, the intercepts of the CS_1 model constructed based on the standard board images were changed greatly after calibration, while no obvious changes were observed in the other models constructed based on the actual pork images (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that it is necessary to calibrate the intercept using the actual pork images when applying the models based on the standard board image. Although the intercepts were not changed greatly after intercept calibration for the models constructed using the feature parametersofthe actual pork images, which does not mean that these models have the high-performance characteristic. On the contrary, the stability of these models constructed using the images features of the actual pork may be unsatisfied. For example, Sun et al found that the overall prediction accuracy was 78.9% of model constructed using the featuresof the pork from seven plants for pork color scores, while the prediction accuracies of the models wereranged from 75.0\u0026ndash;92.5% in individual plant(Sun et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), indicating the performances of models developed were affected by many external factors.In this study, thenovel method proposed for constructing the CS_1 model hasseveral characteristics, including full range coverage, modeling with a small number of samples and balanced sample size, and the performance of the models is less affected by sample characteristics and ease to be calibrated with a small number of actual pork.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3Classification accuracy evaluation of the modelscalibrated by mixed pig herd at different pork color scores\u003c/h2\u003e \u003cp\u003eIn this study, three methods were used to construct the pork color scoring models, three models for each method (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To test the predictive ability of these models, the classification accuracy of all the calibrated models were firstly evaluated at different pork color scoreswith a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 and \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50.The results shows that the overall classification accuracy with a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 is about 30% higher than that with a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 for each model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), that is because artificial pork colorscoring was performed based on a scale of \u0026plusmn;\u0026thinsp;0.50. Notably, the high classification accuracy with a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 was observed for most of the models, but the obvious differences of classification accuracy were observed at different pork color scores in a specific model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, the highest classification accuracy was mainly observed at a score of 5.0, but the results need to be further validated by increasing the number of testingsamples.While,the stablyhighclassification accuracy was achieved at a score of 4.0for all the models. The main reason is that the samples are dominated in a score of 4.0. In addition, the classification accuracies of most models were improved as the increases of the image feature parameters,and the classification accuracy of the models based on the image feature parameters of the pork standard color board images have the similar predictive ability with the models based the image feature parameters of the actual pork images (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eClassification accuracy of the models calibrated by mixed pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 at different pork color scores.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eColor Grade\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of the models calibrated by mixed pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 at different pork color scores.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eColor Grade\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.48\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4Classification accuracy evaluation of the models calibratedby mixed pig herd ineach pig herd\u003c/h2\u003e \u003cp\u003eTo explore the effect of different pig herds on the classification accuracy, the classification accuracy evaluation of the calibrated models was conducted in each pig herd. As shown Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003ewith a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 and \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50, respectively.Similarly, the results shows that the classification accuracieswith a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50areobviously higher than that with a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 for each model. However, the classification accuraciesarefluctuated dramatically among the pig herds (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the models constructed using the\u003cem\u003eL\u003c/em\u003e* feature parameter, the classification accuraciesareranged from 65.96\u0026ndash;96.94%; In the models constructed using the\u003cem\u003eL\u003c/em\u003e* and \u003cem\u003ea\u003c/em\u003e* feature parameters, the classification accuraciesareranged from 85.11\u0026ndash;100.00%; In the models constructed usingthe \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e* and \u003cem\u003eb\u003c/em\u003e* feature parameters, the classification accuraciesareranged from 80.85\u0026ndash;100.00%. Moreover, the overall highest classification accuracy was observed in the CS_3 models, but the CS_1 and CS_2 models have the comparative predictive ability with CS_3 models in a condition of the same method, and the CS_1 and CS_2 models show more similar predictive ability. Generally, these results indicate that the classification accuraciesfor all the models were significantly impacted by pig herds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of the models calibrated by mixed pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 in different pig herds.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDifferent pig herds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e68.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The sample sizes of the LM and Me pig populations are small and do not have statistical significance, thus the pig herds for accuracies analysis were excluded.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of the models calibrated by mixed pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 in different pig herds.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDifferent pig herds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The sample sizes of the LM and Me pig populations are small and do not have statistical significance, thus the pig herd for accuracies analysis of were excluded.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5Classification accuracy evaluationof the models calibrated by individual pig herd in each pig herd\u003c/h2\u003e \u003cp\u003eDue to the classification accuracy of the modelsis affected by the pig herds, so the calibration of the models using the image features of the actual pork derived from the individual pig herd was subsequently performed and the classification accuracies of these calibrated modelsin each pig herd were evaluated.As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e,the calibrated intercepts were different in each pig herd for the models. Notably, when compared with the calibrated intercepts in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the greater the change of calibrated intercepts (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the greater the corresponding classification accuracy will be improved for a specific modelwith a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25or\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003evs.\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the overall classification accuracies of all the models are improved greatly, and the classification accuracies of all the models constructed using the \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e* and \u003cem\u003eb\u003c/em\u003e* feature parameters reach more than 96.00% and the models constructed by the three methods shows the similar predictive ability. These results indicate that the classification accuracies of all the models can be improved by the calibration of intercept, and a much larger improvement can be achieved using the novelmodel construction method based on the pork color standard boar proposed in this study compared to the traditional method, and the similar predictive ability of the models constructed by the novel method can be obtained after the intercept calibration compared to the models constructed by the traditional method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntercept calibration of pork color scoring models using the feature parameters of actual pork from individual pig herd.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIntercepts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDifferent pig herds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of models calibrated by individual pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 in each pig herd.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDifferent pig herds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification accuracy of models calibrated by individual pig herd within a scoring scale\u0026thinsp;\u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 in each pig herd.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eAccuracy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDifferent pig herds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_1_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_2_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS_3_ L*a*b*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.63\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"},{"header":"4 Conclusion","content":"\u003cp\u003e In this study, a novel method for the rapid construction of a precise color scoring models based on the pork colorstandard board imageswere proposed, andthe classification accuracies of the models developed by this novel method are comparable to the models constructed by the traditional method across all pork color scores. For example, under a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50, all the overall classification accuracies of all the models calibrated by mixed pig herd based on \u003cem\u003eL\u003c/em\u003e*, \u003cem\u003ea\u003c/em\u003e* and \u003cem\u003eb\u003c/em\u003e* feature parameters reaches more than94.00%. In the porkcolor scoring models, the evaluable range of models is influenced by the pork color scoring range of the samples used to develop the models. However,most of the pork collected were distributed between 3.5 and 4.0 color score in this study, with an overall range of 3.0 to 5.0 color score.In contrast, a completed distribution of 1.0 to 6.0 colorscore can be provided by the pork colorstandard boards. Therefore, the color scoring models constructed based on the pork colorstandard boardscan be adapted to the full range of pork color evaluation.Moreover,the results showed that the models achievedthe higher classification accuracy for pork color scorein a condition of large number of samples than that in a condition of small number of samples.\u003c/p\u003e \u003cp\u003e CS_1 model was developed only using the feature parameters of six pork colorstandard board images and the intercepts were calibrated only using five actual pork, while525pork was needed to determine the slopeand intercept of the CS_2 and CS_3 models. Therefore, the novel method proposed for the construction of CS_1 model exhibitsthe advantages of simple model construction and fast parameter calibration.\u003c/p\u003e \u003cp\u003eThe pig herd is critical factor affecting the classification accuracy of the models. The results show that theoverall classification accuracies of aresignificantly improved in a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 and \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50by the intercept calibration using the feature parameters of the actual pork images derived from individual pig herd,and the differences of the classification accuracy among pig herds is reduced obviously.Moreover, the results show that a relatively small improvement of classification accuracieswere observed a scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 when compared to the scoring scale of \u0026le;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 after intercept calibration in different herds,that is because the relatively high classification accuracy of the models already were existed an uncalibrated model. Together, the intercept calibrationis an effective method to enhance the classification accuracy and it should be performed for individual pig breed.\u003c/p\u003e \u003cp\u003eIn this study, most of pork collected were concentrated in the 3.0 to 4.5 color score, the pork with the color scoresat 2.0 or below and 5.0 or above are not enough. In the future, it will be necessary to increase the amount of pork with the color scores of 2.0 or below and 5.0 or above to improve the data set, andfurther evaluate the applicability of color scoring models for specific scoring levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/h3\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 paper.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGagaoua, M., Suman, S. P., Purslow, P. P., \u0026amp; Lebret, B. (2023). The color of fresh pork: Consumers expectations, underlying plant-to-fork factors, myoglobin chemistry and contribution of proteomics to decipher the biochemical mechanisms. Meat Science, 206. doi:ARTN 10934010.1016/j.meatsci.2023.109340\u003c/li\u003e\n\u003cli\u003eGirolami, A., Napolitano, F., Faraone, D., \u0026amp; Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111-118. doi:10.1016/j.meatsci.2012.08.010\u003c/li\u003e\n\u003cli\u003eHerdert, f. (1993). Cie color space and it8 at work - a quantitative-analysis of color matching from scanner to print in a production environment. Paper presented at the device-independent color imaging and imaging systems integration. \u003c/li\u003e\n\u003cli\u003eKang, S. P., East, A. R., \u0026amp; Trujillo, F. J. (2008). Colour vision system evaluation of bicolour fruit: A case study with \u0026apos;B74\u0026apos; mango. Postharvest Biology and Technology, 49(1), 77-85. doi:10.1016/j.postharvbio.2007.12.011\u003c/li\u003e\n\u003cli\u003eLebret, B., \u0026amp; Candek-Potokar, M. (2022). Review: Pork quality attributes from plant to fork. Part I. Carcass and fresh meat. Animal, 16. doi:ARTN 10040210.1016/j.animal.2021.100402\u003c/li\u003e\n\u003cli\u003eMilovanovic, B., Djekic, I., Djordjevic, V., Tomovic, V., Barba, F., Tomasevic, I., \u0026amp; Lorenzo, J. M. (2019). Pros and cons of using a computer vision system for color evaluation of meat and meat products. 60th International Meat Industry Conference Meatcon2019, 333. doi:Artn 01200810.1088/1755-1315/333/1/012008\u003c/li\u003e\n\u003cli\u003eNPPC. (1999). Official Color and Marbling Standards. In. Des Moines, IA, USA: The Pork Producers Council.\u003c/li\u003e\n\u003cli\u003ePurslow, P. P. (2017). Chapter 1 - Introduction. In P. P. Purslow (Ed.), New Aspects of Meat Quality (pp. 1-9): Woodhead Publishing.\u003c/li\u003e\n\u003cli\u003eRuedt, C., Gibis, M., \u0026amp; Weiss, J. (2023). Meat color and iridescence: Origin, analysis, and approaches to modulation. Comprehensive Reviews in Food Science and Food Safety, 22(4), 3366-3394. doi:10.1111/1541-4337.13191\u003c/li\u003e\n\u003cli\u003eSun, X., Young, J., Liu, J. H., \u0026amp; Newman, D. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Science, 140, 72-77. doi:10.1016/j.meatsci.2018.03.005\u003c/li\u003e\n\u003cli\u003eTomasevic, I., Tomovic, V., Ikonic, P., Rodriguez, J. M. L., Barba, F. J., Djekic, I., Zivkovic, D. (2019). Evaluation of poultry meat colour using computer vision system and colourimeter Is there a difference? British Food Journal, 121(5), 1078-1087. doi:10.1108/Bfj-06-2018-0376\u003c/li\u003e\n\u003cli\u003eTomasevic, I., Tomovic, V., Milovanovic, B., Lorenzo, J., Dordevic, V., Karabasil, N., \u0026amp; Djekic, I. (2019). Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Science, 148, 5-12. doi:10.1016/j.meatsci.2018.09.015\u003c/li\u003e\n\u003cli\u003eWei, X. Y., Lam, S., Bohrer, B. M., Uttaro, B., L\u0026oacute;pez-Campos, O., Prieto, N., Ju\u0026aacute;rez, M. (2021). A Comparison of Fresh Pork Colour Measurements by Using Four Commercial Handheld Devices. Foods, 10(11). doi:ARTN 251510.3390/foods10112515\u003c/li\u003e\n\u003cli\u003eWu, D., \u0026amp; Sun, D. W. (2013). Colour measurements by computer vision for food quality control - A review. Trends in Food Science \u0026amp; Technology, 29(1), 5-20. doi:10.1016/j.tifs.2012.08.004\u003c/li\u003e\n\u003cli\u003eZhao, S., Gu, J., Zhao, Y., Hassan, M., Li, Y., \u0026amp; Ding, W. (2015). A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model. Scientific Reports, 5(1), 16241. doi:10.1038/srep16241\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Meat color, Standard color board, Scoring model, Fullrange, Equivalent sample, Rapid calibration","lastPublishedDoi":"10.21203/rs.3.rs-5504020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5504020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePork color is one of the most important attributeswhich reflect meat quality and affect consumer perception and purchase decisions.However, the rapid and objectivepork color scoring method is still lack. The traditional methodsfor the construction of pork color scoring models depend on a large sample sizeand face the problem of sample imbalance,and the slope and intercept calibration significantly rely on the source of the samples and device.To addressed the limitations,a rapid method for constructing a precision pork color scoring models based on six standard color boardimages was proposed in this study, and performanceswere compared with the\u0026nbsp;models constructed using traditional methodsbased on 525 actual pork images from seven pig herds. The results show the performances of CS_1models constructed by the novel methodwith a R\u003csup\u003e2 \u003c/sup\u003eof 0.96 to 0.97 are similar to the traditional CS_2 models with a R\u003csup\u003e2\u003c/sup\u003e of 0.97 to 0.99, but are superior to the traditional CS_3 models with is a R\u003csup\u003e2\u003c/sup\u003e of 0.50-0.81, while theoverallclassification accuraciesof theCS_1 models are similar to the traditional models at different scores by the intercept calibration using the pork images from the mixed pig herd, exhibiting thatthe overall classification accuracies of CS_1_L*, CS_1_L*a*, CS_1_L*a*b*models reach 91.43%, 95.62% and 94.10%within a scoring scale ≤± 0.50, respectively. Moreover, the results indicate that the classification accuracies of the models vary considerably among the pig herdsandsignificantly improved by the intercept calibration using the pork imagesfromthe individual pig herd, exhibiting that the overall classification accuracies are enhanced from 91.60% to 93.75%, 95.70% to 95.90%, 93.75% to 96.10% for the CS_1_L*, CS_1_L*a*, CS_1_L*a*b*modelswithin a scoring scale ≤± 0.50, respectively.Taken together, this study provides a fast method for constructing the pork color scoring model, whilethe novel method exhibits several advantages, including full range coverage ranged from 1.0 to 6.0, equivalent small samples with one score for six standard color board images, and rapid intercept calibration with five actual pork images. Moreover, this study demonstrates that the intercept calibration of models is a fast and effective method to enhance the classification accuracy for pork color scoring, which provides new theoretical support for the rapid and objective assessment of pork color.\u003c/p\u003e","manuscriptTitle":"Rapid Construction Method for a Precision Pork Color Scoring Model Based on Standard Color Board Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 09:08:07","doi":"10.21203/rs.3.rs-5504020/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":"f7f2952d-e382-43e8-b832-e300715eba96","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41136846,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":41136847,"name":"Scientific community and society/Agriculture"},{"id":41136848,"name":"Physical sciences/Engineering/Electrical and electronic engineering"}],"tags":[],"updatedAt":"2025-03-20T09:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 09:08:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5504020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5504020","identity":"rs-5504020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}