Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification

Challenges and future perspectives for the European grading of pig carcasses - A quality view

This study sought to evaluate pig carcass grading, describing the existing approaches and definitions, and highlighting the vision for overall quality grading. In particular, the current state of pig carcass grading in the European Union (SEUROP system), its weaknesses, and the challenges to achieve more uniformity and harmonization across member states were described, and a broader understanding of pig carcass value, which includes a vision for the inclusion of meat quality aspects in the grading, was discussed. Finally, the noninvasive methods for the on-line evaluation of pig carcass and meat quality (hereafter referred to as pork quality), and the conditions for their application were discussed. As the way pigs are raised (especially in terms of animal welfare and environmental impact), and more importantly, their perception of pork quality, is becoming increasingly important to consumers, the ideal grading of pigs should comprise pork quality aspects. As a result, a forward-looking "overall quality" approach to pork grading was proposed herein, in which grading systems would be based on the shared vision for pork quality (carcass and meat quality) among stakeholders in the pig industry and driven by consumer expectations with respect to the product. Emerging new technologies provide the technical foundation for such perspective; however, integrating all knowledge and technologies for their practical application to an "overall quality" grading approach is a major challenge. Nonetheless, such approach aligns with the recent vision of Industry 5.0, i.e. a model for the next level of industrialization that is human-centric, resilient, and sustainable.

Developing an alternative classification method for predicting ham composition using linear measurements from the cross-sectional ham surface

Digital image analysis based on the ham cross-sectional face was used to measure two lean muscle and three subcutaneous fat locations from 248 bone-in hams. Linear measurements of the two selected fat locations were used to predict dual-energy X-ray (DXA) fat or lean percentages with prediction accuracies (R2) of 0.7 in a stepwise regression eq. A classification system was built based on the prediction equations, and the linear measurements aimed to classify extremes at the threshold of the 10th percentile of DXA fat percentage (> 32.0%) and lean percentage (< 60.2%). When using either DXA fat or lean percentage, lean ham prediction accuracy dropped by 18%, but fat ham prediction accuracy increased by 60% when the threshold was changed from the 10th percentile to the 30th percentile. This classification approach has the potential to be converted into a manual tool with several useful applications for commercial pork processors.

A deep learning-based framework for predicting pork preference

Meat consumption per capita in South Korea has steadily increased over the last several years and is predicted to continue increasing. Up to 69.5% of Koreans eat pork at least once a week. Considering pork-related products produced and imported in Korea, Korean consumers have a high preference for high-fat parts, such as pork belly. Managing the high-fat portions of domestically produced and imported meat according to consumer needs has become a competitive factor. Therefore, this study presents a deep learning-based framework for predicting the flavor and appearance preference scores of the customers based on the characteristic information of pork using ultrasound equipment. The characteristic information is collected using ultrasound equipment (AutoFom III). Subsequently, according to the measured information, consumers' preferences for flavor and appearance were directly investigated for a long period and predicted using a deep learning methodology. For the first time, we have applied a deep neural network-based ensemble technique to predict consumer preference scores according to the measured pork carcasses. To demonstrate the efficiency of the proposed framework, an empirical evaluation was conducted using a survey and data on pork belly preference. Experimental results indicate a strong relationship between the predicted preference scores and characteristics of pork belly.

Olfactory visualization sensor system based on colorimetric sensor array and chemometric methods for high precision assessing beef freshness

Beef is easily spoiled, resulting in foodborne illness and high societal costs. This study proposed a novel olfactory visualization system based on colorimetric sensor array and chemometric methods to detect beef freshness. First, twelve color-sensitive materials were immobilized on a hydrophobic platform to acquire scent information of beef samples according to solvatochromic effects. Second, machine vision algorithms were used to extract the scent fingerprints, and principal component analysis (PCA) was employed to compress the feature dimensions of the fingerprints. Finally, four qualitative models, k-nearest neighbor, extreme learning machine, support vector machine (SVM), and random forest, were constructed to evaluate the beef freshness according to the value of total volatile basic nitrogen (TVB-N) and total viable counts (TVC). Results demonstrated that SVM had a preferable prediction ability, with 95.83% and 95.00% precision in the training and prediction sets, respectively. The results revealed that the simple constructed olfactory visualization sensor system could rapidly, robustly, and accurately assess beef freshness.

Feasibility of on/at Line Methods to Determine Boar Taint and Boar Taint Compounds: An Overview

Simple Summary

Due to welfare issues, the physical castration of male pigs is decreasing, and the entire male pig production is increasing. Fattening entire male pigs requires control due to the possibility of accumulating off odour/flavour called boar taint, which is mainly due to two compounds - skatole and androstenone. If carcasses with boar taint reach the market, it can cause a negative consumer reaction which may have economic consequences for the whole meat chain. Thus, it is necessary to sort out carcasses at the slaughter line. Today, a sensory quality control (human nose method) is used in some slaughter plants for this purpose. Detection by physical or chemical methods is also envisaged. A colorimetric method to determine skatole has been used in Danish abattoirs for decades, but it is foreseen that it will soon be replaced by the laser diode thermal desorption ion source coupled with a mass spectrometry equipment that allows a fully automated classification based on skatole and androstenone levels at speed line, with a delay of less than 40 min. Other potential methods such as the electrochemical biosensors, rapid evaporative ionization mass spectroscopy and Raman spectroscopy, still need further development and validation for an application at abattoir level.

Abstract

Classification of carcasses at the slaughter line allows an optimisation of its processing and differentiated payment to producers. Boar taint is a quality characteristic that is evaluated in some slaughter plants. This odour and flavour is mostly present in entire males and perceived generally by sensitive consumers as unpleasant. In the present work, the methodologies currently used in slaughter plants for boar taint classification (colorimetric method and sensory quality control-human nose) and the methodologies that have the potential to be implemented on/at the slaughter line (mass spectrometry, Raman and biosensors) have been summarized. Their main characteristics are presented and an analysis of strengths, weaknesses, opportunities and threats (SWOT) has been carried out. From this, we can conclude that, apart from human nose, the technology that arises as very promising and available on the market, and that will probably become a substitute for the colorimetric method, is the tandem between the laser diode thermal desorption ion source and the mass spectrometry (LDTD-MS/MS) with automation of the sampling and sample pre-treatment, because it is able to work at the slaughter line, is fast and robust, and measures both androstenone and skatole.

Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches

This study evaluated visible and near-infrared spectroscopy (Vis-NIRS) to authenticate barley-finished beef using different discrimination approaches. Dietary grain source (barley, corn, or blend-50% barley/50% corn) did not affect (P > 0.05) meat quality but influenced (P < 0.05) fatty acid profiles. The longissimus thoracis (LT) from barley-fed steers had lower n-6 fatty acid content and n-6/n-3 ratio compared to LT from corn and blended grain-fed steers. Vis-NIRS coupled with partial least square discriminant analysis (PLS-DA) and support vector machine in the linear (L-SVM) kernel classified with approximately 70% overall accuracy subcutaneous fat and intact LT samples, respectively, from barley, corn, and blended-fed cattle. When only barley and corn samples were considered, fat and intact LT samples were correctly classified with overall accuracy >94% with PLS-DA and radial/L-SVM, and approximately 90% with PLS-DA and L-SVM, respectively. Ground LT samples were classified with ≤70% overall accuracy. Vis-NIRS measurements on fat and intact LT have potential to discriminate between corn and barley-fed beef.