Visible hyperspectral imaging for lamb quality prediction

Three factors, including tenderness, juiciness and flavour, are found to have an impact on lamb eating quality, which determines the repurchase behaviour of customers. In addition to these factors, the surface colour of lamb can also influence the purchase decision of consumers. From a long time ago, meat industries have been looking for fast and non-invasive objective quality evaluation approaches, where near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) have shown great promises in assessing beef quality compared with conventional methods. However, rare research has been conducted for lamb samples. Therefore, in this paper the feasibility of the HSI system for evaluating lamb quality was tested. In total 80 lamb samples were imaged using a visible range HSI system and the spectral profiles were used for predicting lamb quality related traits. For some traits, noise was further removed from HSI spectra by singular spectrum analysis (SSA) for better performance. Considering support vector machine (SVM) is sensitive to high dimensional data, principal component analysis (PCA) was applied to reduce the dimensionality of HSI spectra before feeding into SVM for constructing prediction equations. The prediction results suggest that HSI is promising in predicting some lamb eating quality traits, which could be beneficial for lamb industries

Using machine learning procedures to ascertain the influence of beef carcass profiles on carcass conformation scores

In this study, a total of 163 young-bull carcasses belonging to seven Spanish native beef cattle breeds showing substantial carcass variation were photographed in order to obtain digital assessments of carcass dimensions and profiles. This dataset was then analysed using machine learning (ML) methodologies to ascertain the influence of carcass profiles on the grade obtained using the SEUROP system. To achieve this goal, carcasses were obtained using the same standard feeding regime and classified homogeneous conditions in order to avoid non-linear behaviour in grading performance. Carcass weight affects grading to a large extent and the classification error obtained when this attribute was included in the training sets was consistently lower than when it was not. However, carcass profile information was considered non-relevant by the ML algorithm in earlier stages of the analysis. Furthermore, when carcass weight was taken into account, the ML algorithm used only easy-to-measure attributes to clone the classifiers decisions. Here we confirm the possibility of designing a more objective and easy-to-interpret system to classify the most common types of carcass in the territory of the EU using only a few single attributes that are easily obtained in an industrial environment

Computer Vision System as a Tool to Estimate Pork Marbling

Currently pork marbling is assessed subjectively in the industry, because of the limited methods and tools that are suitable for the industry. In this dissertation, we are devoted to develop a computer vision system for objective measurement of pork which suits the industrial needs. Experiment 1 examined the possibility of using computer vision system (CVS) to predict marbling in a lab-based experiment using pork samples that were already trimmed of subcutaneous fat and connective tissue. Experiment 2 an industrial scale CVS was built to predict the 3rd and 10th rib pork chop’s marbling. Experiment 3 the industrial scale CVS was tested in the meat plant and images of whole boneless pork loin were collected. The CVS predicted marbling were compared with subjective marbling score using crude fat percentage (CF%) as standard. In experiment 1 subjective marbling score had a correlation of 0.81 with CF% while CVS had a 0.66 correlation. CVS has shown an accuracy of 63% for stepwise regression model and 75% for support vector machine model. These results indicate that CVS has the potential to be used as an tool to predict pork intramuscular fat (IMF)%. In experiment 2 the accuracy of CVS predicting pork chop CF% was 68.6% and subjective marbling was 70.1%. A drop of accuracy in predicting anterior chop CF% for both CVS and objective marbling score was observed when compared to posterior chop, this suggest that there is a discrepancy in accuracy between the anatomy location of samples collected. In experiment 3 the accuracy of CVS predicting boneless whole loin was 58.6% and subjective marbling score was 53.3%. In this research, CVS has demonstrated a consistency of accuracies using different pork samples. CVS has shown higher accuracy when predicting whole boneless loin IMF% when compared to subjective assessment.National Pork BoardColeman Natura

Ability, repeatability, and reproducibility of rapid evaporative ionization mass spectrometry to predict beef quality attributes

Includes bibliographical references.2022 Fall.Tenderness, juiciness, and flavor are beef quality attributes that influence consumer satisfaction eating beef. Rapid evaporative ionization mass spectrometry (REIMS) is a novel technique that provides chemical information of biological tissues with the potential to predict beef quality attributes. Two studies were conducted to evaluate the ability of REIMS to predict quality attributes of beef (study I) and to evaluate the repeatability and reproducibility of REIMS in a beef matrix (study II). In study I, USDA Select or upper two-thirds Choice (n = 42, N=84) striploins and tenderloins were collected approximately 36h post-mortem from a commercial beef abattoir. Slivers of the longissimus dorsi muscle between the 12-13th rib were collected during grading (GR, 36h post-mortem) and analyzed using REIMS. Striploins (LM) and tenderloins (PM) were cut into portions and assigned to 6 aging periods (3, 14, 28, 42, 56, and 70 days). However, only samples aged 3, 14, and 28 days were used to represent industry practices in study I. After aging, portions were cut into 2.54-cm steaks to analyze juiciness, tenderness, and 10 flavor attributes with a trained sensory panel. In addition, tenderness measures were performed using slice shear force (SSF) and Warner-Bratzler shear force (WBF). Samples were categorized by SSF, WBF, and sensory panel tenderness (PT) into "tough" and "tender"; by juiciness into "dry" and "juicy"; and by flavor into "acceptable" and "unacceptable" classes using a composite score of all flavor descriptors. Combinations of three dimensionality reduction methods (principal component analysis [PCA], feature selection, [FS], and a combination of both [PCA-FS]) with 13 machine learning algorithms were used to create classification models based on REIMS data for tenderness, juiciness, and flavor classes at the three aging periods. The predictive ability of the models was assessed with the overall accuracy resulting from 10-fold cross-validation. Among all machine learning algorithms evaluated, the maximum classification accuracies for days 3, 14, and 28 were 94, 87, and 83% for PT; 86, 85, 92% for SSF; 87, 82, and 95 for WBF; 85, 84, and 86% for juiciness; and 87, 89, and 81% for flavor classes, respectively. FS performed the best as a dimensionality reduction method for all PT, juiciness, flavor, and SSF on day 3 and WBF on days 3 and 14. PCA-FS was the best dimensionality reduction method for SSF on days 14 and 28, and WBF on day 28. Extreme gradient boosting machine learning algorithm was the highest performing algorithm for all juiciness models, flavor model on day 28, PT on days 3 and 14, SSF on days 14 and 28, and WBF on days 3. Partial least squared discriminant analysis (PLSDA) performed better for PT day 28 and flavor day 14, while elastic-net regularized generalized linear model, random forest, and support vector machine were the highest performing algorithms for SSF day 3, and WBF days 14 and 28, respectively. Results demonstrated that the chemical fingerprints obtained with REIMS could potentially be used as in situ and real-time technique to sort carcasses by flavor, juiciness, and tenderness. However, overlaps between classes affected REIMS results, and unbalanced data negatively affected model accuracies. Therefore, exploring the full potential of REIMS will require increasing the sample size and developing a sampling method that allows increased separation between sensory evaluations. Study II was performed with REIMS data from all LM and PM samples from the six aging periods (n=1008), two sets of GR samples (n=168, N=84), and quality control (QC) samples (n=29) made from homogenized ground beef. Except for the second set of GR samples, REIMS analysis of all samples was performed at Colorado State University (CSU) using a meat probe as the sampling device. Analysis of all samples was performed over 5 days, including two batches per day. GR samples were evaluated on the first day, and LM and PM data were randomly analyzed on the remaining days. QC samples were analyzed at the beginning, middle, and end of each batch. The second set of GR samples was analyzed at Texas Tech University (TTU) using different mass spectrometry (MS) instruments, technicians, and an iKnife as the sampling device. The stability of REIMS data between burns, batches, and days was evaluated with QC data. Day effect and robustness of REIMS data were evaluated with data from LM and PM samples, and interlab reproducibility was evaluated with data from GR samples. Multiple classification models of muscle type and aging were built with LM and PM data to evaluate the robustness of REIMS and day-to-day variability. Models to predict sensory attributes of beef were used to assess the robustness of REIMS with respect to interlab variability. Coefficients of variation (CV) between burns of the mass bins representing 90% of the total ion current were between 0.7 to 0.98, while the most relevant mass bins showed CV less than 0.3. Variances between batches and collection days were not significant (P < 0.05). PCA of LM and PM showed that data variability by collection day was stronger than muscle type and aging time variability. However, data could classify samples into muscle types and two distant aging times with accuracies higher than 95.6% and 91.0%, respectively. PCA of GR samples showed that data collected in both labs differed, and the predictive models developed with the CSU data did not appropriately predict the quality classes with the TTU data. REIMS collected with the meat probe provides a chemometric profile of beef samples with good repeatability and interday reproducibility but low interlab reproducibility. Consequently, optimization and standardization of sampling methods will be required to improve the interlab reproducibility of REIMS

Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (trn.1); genotypes of PB sires and average of CB records per PB sire (trn.2); and individual CB genotypes and phenotypes (trn.3). The average of CB offspring records was the trait to be predicted from PB sire’s genotype using cross-validation. Single nucleotide polymorphisms (SNPs) were ranked based on the Spearman Rank correlation with the trait. Subsets with an increasing number (from 50 to 2,000) of the most informative SNPs were used as predictor variables in SVM. Prediction performance was the median of the Spearman correlation (SC, interquartile range in brackets) between observed and predicted phenotypes in the testing set. The best predictive performances were obtained when sire phenotypic information was included in trn.1 (0.22 [0.03] for RFI with SVM and 250 SNPs, and 0.12 [0.05] for ADG with SVM and 500–1,000 SNPs) or when trn.3 was used (0.29 [0.16] with Genomic best linear unbiased prediction (GBLUP) for RFI, and 0.15 [0.09] for ADG with just 50 SNPs). Animals from the last two generations were assigned to the testing set and remaining animals to the training set. Individual’s PB own phenotype and genotype improved the prediction ability of CB offspring of young animals for ADG but not for RFI. The highest SC was 0.34 [0.21] and 0.36 [0.22] for RFI and ADG, respectively, with SVM and 50 SNPs. Predictive performance using CB data for training leads to a SC of 0.34 [0.19] with GBLUP and 0.28 [0.18] with SVM and 250 SNPs for RFI and 0.34 [0.15] with SVM and 500 SNPs for ADG. Results suggest that PB candidates could be evaluated for CB performance with SVM and low-density SNP chip panels after collecting their own RFI or ADG performances or even earlier, after being genotyped using a reference population of CB animals.info:eu-repo/semantics/publishedVersio

MEAT QUALITY PREDICTION USING MACHINE LEARNING

Meat quality is an essential aspect of the food industry. However, traditional methods of meat quality prediction have limitations in terms of accuracy, cost, and time efficiency. This project focused on utilizing advanced Deep learning and Machine learning algorithms to develop- machine learning models that could predict the freshness (or spoilage) of meat with a 100% accuracy, based on image data. In addition to accuracy, this study emphasizes the significance of speed and time in selecting the optimal machine learning model. The research questions are: Q1. What hybrid neural networks should be used to predict freshness? Q2. How do hybrid neural networks determine the freshness of the meat based on the image? Q3. How can accuracy and performance speed be improved? A dataset from the Kaggle repository was used to explore various machine learning algorithms such as Support Vector Machines, Decision Trees, and Random Forests with a combination of Convolutional Neural Network, a deep learning network. The findings are: Q1. A combination of Support Vector Machines-Convolutional Neural Network, Decision Trees-Convolutional Neural Network, and Random Forests-Convolutional Neural Network were used to predict freshness. 2) The hybrid neural networks were trained using the tensorflow.keras.models, a high-level neural networks API of the TensorFlow library, which allowed the creation and training of complex machine learning models in a simple and straightforward manner. 3) The accuracy and performance speed of the model can be improved by utilizing a distributed computing environment for training, which involves the collaboration of multiple machines to carry out computations. The conclusion from our project is that Utilizing the hybrid neural networks developed, it is possible to classify meat products as either fresh or spoiled using image analysis. This approach not only reduces the reliance on human input for meat classification but also decreases the time taken to complete the classification process. Furthermore, emerging areas for future research that emerged from this study is to develop machine learning models that can integrate and fuse multi-modal data such as genetics, feeding and processing techniques to make more accurate predictions of meat quality

Identifying market segments in beef: Breed, slaughter weight and ageing time implications

In this paper we propose a method to learn the reasons why groups of consumers prefer some beef products to others. We emphasise the role of groups since, from a practical point of view, they may represent market segments that demand different products. Our method starts representing people’s preferences in a metric space; there we are able to define a kernel based similarity function that allows a clustering algorithm to identify significant groups of consumers with homogeneous likes. Finally, in each cluster, we developed, with a support vector machine (SVM), a function that explains the preferences of those consumers grouped in the cluster. The method was applied to a real case of consumers of beef that tasted beef from seven Spanish breeds, slaughtered at two different weights and aged for three different ageing periods. Two different clusters of consumers were identified for acceptability and tenderness, but not for flavour. Those clusters ranked two very different breeds (Asturiana and Retinta) in opposite order. In acceptability, ageing period was appreciated in a different way. However, in tenderness most consumers preferred long ageing periods and heavier to lighter animal