On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses

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.

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.

The application of computer vision systems in meat science and industry - A review

Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

Correlation between the Korean pork grade system and the amount of pork primal cut estimated with AutoFom III

It is impossible to know the amount of pork primal cut by pig carcass grade which is determined only by carcass weight and backfat thickness in the Korean Pig Carcass System. The aim of this study was to investigate the correlation between the pig carcass grade and the amount of pork primal cut estimated with AutoFom III. A total of 419,321 Landrace, Yorkshire, and Duroc (LYD) pigs were graded with the Korean Pig Carcass Grade System. Amounts of belly, neck, loin, tenderloin, spare ribs, shoulder, and ham were estimated with AutoFom III. Regression equations for seven primal cuts according to each grade were derived. There were significant differences among the three carcass grades due to heteroscedasticity variance (p < 0.0001). Three regression equations were derived from AutoFom III estimation of primal cuts according to carcass grades. The coefficient of determination of the regression equation was 0.941 for grade 1⁺, 0.982 for grade 1, and 0.993 for grade 2. Regression equations obtained from this study are suitable for AutoFom III software, a useful tool for the analysis of each pig carcass grade in the Korean Pig Carcass Grade System. The high reliability of predicting the amount of primal cut with AutoFom III is advantageous for the management of slaughterhouses to optimize their product sorting in Korea.

The Implications of Nutritional Strategies that Modify Dietary Energy and Lysine for Growth Performance in Two Different Swine Production Systems

Simple Summary

Reducing dietary energy is a common practice for dealing with the price volatility of high energy sources, such as fats and oils, which are the costliest constraints in swine feed formulation. Theoretically, pigs can overcome a reduced energy density by increasing feed intake; however, as other factors like fibrous ingredients limit feed intake physically rather than metabolically, reducing dietary energy could also entail a lower energy intake. The expected effect on feed intake also influences lysine intake, and therefore, when NE trials are conducted, it is necessary to ensure that lysine is not a limiting factor for growth. In the present work, the effects of two dietary energy and lysine levels were tested in a factorial arrangement. The same approach of different levels was analyzed in two different swine production systems targeting different carcass traits. The experiment showed that in one system, reducing energy density did not impair growth; however, in the other system, it limited growth slightly by limiting fat deposition. Although reducing energy density increased feed intake, pigs could not reach a similar energy intake, and consequently were more efficient using energy for growth.

Abstract

This work aimed to determine the impacts of lowering dietary net energy (NE) density in two swine production systems that produce pigs with different carcass traits. To ensure that dietary lysine was not limiting growth, two studies were conducted in a 2 × 2 factorial arrangement with NE and standardized ileal digestible lysine (SID Lys) as experimental factors. A total of 1248 pigs were used in each study, Pietrain (Exp. 1, males non-castrated) or Duroc (Exp. 2, males castrated) sired. Reducing NE resulted in a greater feed intake; however, this was not sufficient to reach the same NE intake. While in Exp. 1 a 3.2% lower NE intake did not impair average daily gain (ADG; p = 0.220), in Exp. 2 a 4.7% lower NE intake reduced ADG by 1.4% (p = 0.027). Furthermore, this effect on ADG entailed a reduced ham fat thickness (p = 0.004) of the first marketed pigs. Increasing SID Lys only had a positive effect in Exp. 1, but no significant interaction between NE and SID Lys was reported (p ≥ 0.100). Therefore, dietary NE can be reduced without impairing growth performance when pigs can increase feed intake sufficiently, and thus, limit energy deficiencies.

Characteristics of Pig Carcass and Primal Cuts Measured by the Autofom Ⅲ Depend on Seasonal Classification

The objective of this study was to investigate slaughtering performance, carcass grade, and quantitative traits of cuts according to seasonal influence by each month in pigs slaughtered in livestock processing complex (LPC) slaughterhouse in Korea, 2017. A total of 267,990 LYD (Landrace×Yorkshire×Duroc) pig data were used in this study. Results of slaughter heads, sex distribution, carcass weight, backfat thickness, grading class, total weight, and fat and lean meat percentages of each cut predicted by Autofom Ⅲ were obtained each month. The number of slaughtered pigs was the highest in early and late fall but the lowest in midsummer. Only in midsummer that the number of females was higher than that of castrates. During 2017, carcass weight was the lowest in late summer. Backfat thickness was in the range of 21–22 mm. In mid and late spring, pigs showed high 1+ grade ratio (37.05% and 36.15%, respectively). For traits of 11 cuts predicted by Autofom Ⅲ, porkbelly showed lower total weight, lean weight, and fat weight in midsummer to early fall but higher lean meat percentage compared to other seasons. Weights of deboned neck, loin, and lean meat were the highest in midfall compared to other seasons (p<0.05). In conclusion, characteristics of slaughtering, grading, and economic traits of pigs seemed to be highly seasonal. They were influenced by seasons. Results of this study could be used as basic data to develop seasonal specified management ways to improve pork production.

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

The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting.