Meat quality evaluation based on computer vision technique: A review

The application of omics technologies for understanding tropical plants-based bioactive compounds in ruminants: a review

Finding out how diet impacts health and metabolism while concentrating on the functional qualities and bioactive components of food is the crucial scientific objective of nutritional research. The complex relationship between metabolism and nutrition could be investigated with cutting-edge "omics" and bioinformatics techniques. This review paper provides an overview of the use of omics technologies in nutritional research, with a particular emphasis on the new applications of transcriptomics, proteomics, metabolomics, and genomes in functional and biological activity research on ruminant livestock and products in the tropical regions. A wealth of knowledge has been uncovered regarding the regulation and use of numerous physiological and pathological processes by gene, mRNA, protein, and metabolite expressions under various physiological situations and guidelines. In particular, the components of meat and milk were assessed using omics research utilizing the various methods of transcriptomics, proteomics, metabolomics, and genomes. The goal of this review is to use omics technologies-which have been steadily gaining popularity as technological tools-to develop new nutritional, genetic, and leadership strategies to improve animal products and their quality control. We also present an overview of the new applications of omics technologies in cattle production and employ nutriomics and foodomics technologies to investigate the microbes in the rumen ecology. Thus, the application of state-of-the-art omics technology may aid in our understanding of how species and/or breeds adapt, and the sustainability of tropical animal production, in the long run, is becoming increasingly important as a means of mitigating the consequences of climate change.

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

Technological tools for the measurement of sensory characteristics in food: A review

The use of technological tools, in the food industry, has allowed a quick and reliable identification and measurement of the sensory characteristics of food matrices is of great importance, since they emulate the functioning of the five senses (smell, taste, sight, touch, and hearing). Therefore, industry and academia have been conducting research focused on developing and using these instruments which is evidenced in various studies that have been reported in the scientific literature. In this review, several of these technological tools are documented, such as the e-nose, e-tongue, colorimeter, artificial vision systems, and instruments that allow texture measurement (texture analyzer, electromyography, others). These allow us to carry out processes of analysis, review, and evaluation of food to determine essential characteristics such as quality, composition, maturity, authenticity, and origin. The determination of these characteristics allows the standardization of food matrices, achieving the improvement of existing foods and encouraging the development of new products that satisfy the sensory experiences of the consumer, driving growth in the food sector. However, the tools discussed have some limitations such as acquisition cost, calibration and maintenance cost, and in some cases, they are designed to work with a specific food matrix.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Quantitative characterizations of visual fibrousness in meat analogues using automated image analysis

A desirable quality of plant-based meat analogues is to resemble the fibrous structure of cooked muscle meat. While texture analysis can characterize fibrous structures mechanically, assessment of visual fibrous structures remains subjective. Quantitative assessment of visual fibrous structures of meat analogues relies on expert knowledge, is resource-intensive, and time-consuming. In this study, a novel image-based method (Fiberlyzer) is developed to provide automated, quantitative, and standardized assessment of visual fibrousness of meat analogues. The Fiberlyzer method segments fibrous regions from 2D images and extracts fiber shape features to characterize the fibrous structure of meat analogues made from mung bean, soy, and pea protein. The computed fiber scores (the ratio between fiber length and width) demonstrate a strong correlation with expert panel evaluations, particularly on a per-formulation basis (r2  = 0.93). Additionally, the Fiberlyzer method generates fiber shape features including fiber score, fiber area, and the number of fiber branches, facilitating comparisons of structural similarity between meat analogue samples and cooked chicken meat as a benchmark. With a simple measurement setup and user-friendly interface, the Fiberlyzer method can become a standard tool integrated into formulation development, quality control, and production routines of plant-based meat analogue. This method offers rapid, cheap, and standardized quantification of visual fibrousness, minimizing the need for expert knowledge in the process of quality control.

Identifying the quality characteristics of pork floss structure based on deep learning framework

Pork floss is a traditional Chinese food with a long history. Nowadays, pork floss is known to consumers as a leisure food. It is made from pork through a unique process in which the muscle fibers become flaky or granular and tangled. In this study, a deep learning-based approach is proposed to detect the quality characteristics of pork floss structure. Describe that the experiments were conducted using widely recognized brands of pork floss available in the grocery market, omitting the use of abbreviations. A total of 8000 images of eight commercially available pork flosses were collected and processed using sharpening, image gray coloring, real-time shading correction, and binarization. After the machine learning model learned the features of the pork floss, the images were labeled using a manual mask. The coupling of residual enhancement mask and region-based convolutional neural network (CRE-MRCNN) based deep learning framework was used to segment the images. The results showed that CRE-MRCNN could be used to identify the knot features and pore features of different brands of pork floss to evaluate their quality. The combined results of the models based on the sensory tests and machine vision showed that the pork floss from TC was the best, followed by YJJ, DD and HQ. This also shows the potential of machine vision to help people recognize the quality characteristics of pork floss structure.

Evaluation of Dual-Band Near-Infrared Spectroscopy and Chemometric Analysis for Rapid Quantification of Multi-Quality Parameters of Soy Sauce Stewed Meat

The objective of this study was to evaluate the performance of near-infrared spectroscopy (NIRS) systems operated in dual band for the non-destructive measurement of the fat, protein, collagen, ash, and Na contents of soy sauce stewed meat (SSSM). Spectra in the waveband ranges of 650-950 nm and 960-1660 nm were acquired from vacuum-packed ready-to-eat samples that were purchased from 97 different brands. Partial least squares regression (PLSR) was employed to develop models predicting the five critical quality parameters. The results showed the best predictions were for the fat (Rp = 0.808; RMSEP = 2.013 g/kg; RPD = 1.666) and protein (Rp = 0.863; RMSEP = 3.372 g/kg; RPD = 1.863) contents, while barely sufficient performances were found for the collagen (Rp = 0.524; RMSEP = 1.970 g/kg; RPD = 0.936), ash (Rp = 0.384; RMSEP = 0.524 g/kg; RPD = 0.953), and Na (Rp = 0.242; RMSEP = 2.097 g/kg; RPD = 1.042) contents of the SSSM. The quality of the content predicted by the spectrum of 960-1660 nm was generally better than that for the 650-950 nm range, which was retained in the further prediction of fat and protein. To simplify the models and make them practical, regression models were established using a few wavelengths selected by the random frog (RF) or regression coefficients (RCs) method. Consequently, ten wavelengths (1048 nm, 1051 nm, 1184 nm, 1191 nm, 1222 nm, 1225 nm, 1228 nm, 1450 nm, 1456 nm, 1510 nm) selected by RF and eight wavelengths (1019 nm, 1097 nm, 1160 nm, 1194 nm, 1245 nm, 1413 nm, 1441 nm, 1489 nm) selected by RCs were individually chosen for the fat and protein contents to build multi-spectral PLSR models. New models led to the best predictive ability of Rp, RMSEP, and RPD of 0.812 and 0.855, 1.930 g/kg and 3.367 g/kg, and 1.737 and 1.866, respectively. These two simplified models both yielded comparable performances to their corresponding full-spectra models, demonstrating the effectiveness of these selected variables. The overall results indicate that NIRS, especially in the spectral range of 960-1660 nm, is a potential tool in the rapid estimation of the fat and protein contents of SSSM, while not providing particularly good prediction statistics for collagen, ash, and Na contents.

A Comparative Study on the Adipogenic and Myogenic Capacity of Muscle Satellite Cells, and Meat Quality Characteristics between Hanwoo and Vietnamese Yellow Steers

Myogenesis and adipogenesis are the important processes determining the muscle growth and fat accumulation livestock, which ultimately affecting their meat quality. Hanwoo is a popular breed and its meat has been exported to other countries. The objective of this study was to compare the myogenesis and adipogenesis properties in satellite cells, and meat quality between Hanwoo and Vietnamese yellow cattle (VYC). Same 28-months old Hanwoo (body weight: 728±45 kg) and VYC (body weight: 285±36 kg) steers (n=10 per breed) were used. Immediately after slaughter, tissue samples were collected from longissimus lumborum (LL) muscles for satellite cells isolation and assays. After 24 h post-mortem, LL muscles from left carcass sides were collected for meat quality analysis. Under the same in vitro culture condition, the proliferation rate was higher in Hanwoo compared to VYC (p<0.05). Fusion index was almost 3 times greater in Hanwoo (42.17%), compared with VYC (14.93%; p<0.05). The expressions of myogenesis (myogenic factor 5, myogenic differentiation 1, myogenin, and myogenic factor 6)- and adipogenesis (peroxisome proliferator-activated receptor gamma)-regulating genes, and triglyceride content were higher in Hanwoo, compared with VYC (p<0.05). Hanwoo beef had a higher intramuscular fat and total monounsaturated fatty acids contents than VYC beef (p<0.05). Whilst, VYC meat had a higher CIE a* and total polyunsaturated fatty acids content (p<0.05). Overall, there was a significant difference in the in vitro culture characteristics and genes expression of satellite cells, and meat quality between the Hanwoo and VYC.

Computer vision for non-contact monitoring of catalyst degradation and product formation kinetics

We report a computer vision strategy for the extraction and colorimetric analysis of catalyst degradation and product-formation kinetics from video footage. The degradation of palladium(ii) pre-catalyst systems to form 'Pd black' is investigated as a widely relevant case study for catalysis and materials chemistries. Beyond the study of catalysts in isolation, investigation of Pd-catalyzed Miyaura borylation reactions revealed informative correlations between colour parameters (most notably ΔE, a colour-agnostic measure of contrast change) and the concentration of product measured by off-line analysis (NMR and LC-MS). The breakdown of such correlations helped inform conditions under which reaction vessels were compromised by air ingress. These findings present opportunities to expand the toolbox of non-invasive analytical techniques, operationally cheaper and simpler to implement than common spectroscopic methods. The approach introduces the capability of analyzing the macroscopic 'bulk' for the study of reaction kinetics in complex mixtures, in complement to the more common study of microscopic and molecular specifics.

Prediction of primal and retail cut weights, tissue composition and yields of youthful cattle carcasses using computer vision systems; whole carcass camera and/or ribeye camera

The application of two computer vision systems (CVS) was evaluated to predict primal and retail cut composition in youthful beef carcasses. Left carcass sides from a total of 634 animals were broken down into primal cuts, scanned using dual-energy x-ray absorptiometry for the prediction of tissue composition and fabricated into retail cuts. Cold carcass camera (CCC) images led to higher R² values than hot carcass camera (HCC) images. The CVS coefficients of prediction for the primal cut weights ranged from 0.61 to 0.97. For the primal cut tissue composition predictions, R² values ranged from 0.09 for Brisket HCC bone prediction to 0.82 for Chuck CCC fat prediction. Retail cut weight estimations had similar R² values, ranging from 0.10 for IMPS 112 (Ribeye roll-denuded ribeye) to 0.99 for IMPS 113C (semi-boneless chuck) both using CCC. The results suggest the feasibility of CVS technologies to predict beef primal and retail cuts weights together with tissue composition, and yield percentages.

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.

Dietary supplementation with jasmine flower residue improves meat quality and flavor of goat

Jasmine flower residue (JFR) is a by-product retained in the production process of jasmine tea and can be used as an unconventional feed due to its rich nutrient value. This study aimed to evaluate the effects of the addition of JFR to the diet of goats on their meat quality and flavor. Twenty-four castrated Nubian male goats were randomly divided into two groups and fed a mixed diet containing 10% JFR (JFR, n = 12) or a conventional diet (CON, n = 12) for 45 days. Meat quality and flavor were measured at the end of the treatment. The addition of JFR to the diet could reduce the shear force of the longissimus dorsi muscle, as well as, the cross-sectional area and diameter of muscle fibers, indicating that the addition of JFR improved meat quality. JFR also increased the content of glutamic acid and ω-3 polyunsaturated fatty acid (C18:3n3 and C20:5N3) and reduced the content of C24:1 and saturated fatty acid (C20:0 and C22:0). In addition, the use of JFR increased the content of acetaldehyde and hexanal in the meat. Furthermore, JFR introduced new volatile components in the meat. The umami, saltiness, and richness of the meat also improved. In conclusion, the addition of jasmine flower residue to the diet can improve the meat quality and flavor of goat.

Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.

Metabolomics-Based Analysis of the Major Taste Contributors of Meat by Comparing Differences in Muscle Tissue between Chickens and Common Livestock Species

The taste of meat is the result of complex chemical reactions. In this study, non-target metabolomics was used to resolve the taste differences in muscle tissue of four major livestock species (chicken, duck, pork, and beef). The electronic tongue was then combined to identify the major taste contributors to meat. The results showed that the metabolism of chicken meat differed from that of duck, pork, and beef. The multivariate statistical analysis showed that the five important metabolites responsible for the differences were all related to taste, including creatinine, hypoxanthine, gamma-aminobutyric acid, L-glutamic acid, and L-aspartic acid. These five key taste contributors acted mainly through the amino acid metabolic pathways. In combination with electronic tongue (e-tongue) analysis, inosine monophosphate was the main contributor of umami. L-Glutamic acid and L-aspartic acid might be important contributors to the umami richness. Creatinine and hypoxanthine contributed more to the bitter aftertaste of meat.

Effectiveness and safety evaluation of graded levels of N-carbamylglutamate in growing-finishing pigs

The aim of this study was as follows: 1) to investigate the effects of graded levels of N-carbamylglutamate (NCG) on performance, blood biochemical indexes, carcass traits and related indicators in growing-finishing pigs, and 2) to determine the optimal supplemental level. The toxicity of high-dose (much higher than recommended levels) NCG was assessed by routine blood tests and blood biochemical and histopathologic examinations of the heart, liver, spleen, lung, kidney and stomach. One hundred and forty-four growing-finishing pigs (Duroc × Large White × Landrace, 32.24 ± 1.03 kg) were used in a 74-d experiment and each treatment was replicated 6 times with 4 pigs (2 barrows and 2 gilts) per replicate. The dietary treatments were a corn-soybean meal basal diet supplemented with 0% (control), 0.05%, 0.1%, 0.15%, 0.2% or 1% NCG. The first 5 groups were used to explore the optimal supplemental level of NCG, while the control, 0.1% and 1% NCG groups were used to explore the safety of high-dose NCG. Compared with the normal control group, the final body weight and average daily gain tended to be higher in the 0.1% group (P = 0.08), the lean percentage tended to be higher in the 0.05% group (P = 0.07), the levels of free amino acids in the blood significantly increased in the 0.1% group (P < 0.05), both 0.1% and 0.15% NCG supplementation increased the levels of nitric oxide (NO) in serum (P = 0.07) and muscle growth- and lipid metabolism-related gene expression (P < 0.05) and NCG supplementation improved C18:1N9C monounsaturated fatty acids (MUFA) in a dose-dependent manner (P = 0.08). In addition, routine blood tests, blood biochemical indexes and histopathological examination revealed no abnormalities. Overall, increasing the levels of NCG did not linearly improve the above indicators; the 0.1% dose showed the best effect, and a high dose (1%) did not pose a toxicity risk.

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

Consumer Perception of Beef Quality and How to Control, Improve and Predict It? Focus on Eating Quality

Quality refers to the characteristics of products that meet the demands and expectations of the end users. Beef quality is a convergence between product characteristics on one hand and consumers’ experiences and demands on the other. This paper reviews the formation of consumer beef quality perception, the main factors determining beef sensory quality, and how to measure and predict beef eating quality at scientific and industrial levels. Beef quality is of paramount importance to consumers since consumer perception of quality determines the decision to purchase and repeat the purchase. Consumer perception of beef quality undergoes a multi-step process at the time of purchase and consumption in order to achieve an overall value assessment. Beef quality perception is determined by a set of quality attributes, including intrinsic (appearance, safety, technological, sensory and nutritional characteristics, convenience) and extrinsic (price, image, livestock farming systems, commercial strategy, etc.) quality traits. The beef eating qualities that are the most valued by consumers are highly variable and depend mainly on the composition and characteristics of the original muscle and the post-mortem processes involved in the conversion of muscle into meat, the mechanisms of which are summarized in this review. Furthermore, in order to guarantee good quality beef for consumers in advance, the prediction of beef quality by combining different traits in scenarios where the animal, carcass, and muscle cuts can be evaluated is also discussed in the current review.

Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review

Fluorescence spectroscopy, color imaging and multispectral imaging (MSI) have emerged as effective analytical methods for the non-destructive detection of quality attributes of various white meat products such as fish, shrimp, chicken, duck and goose. Based on machine learning and convolutional neural network, these techniques can not only be used to determine the freshness and category of white meat through imaging and analysis, but can also be used to detect various harmful substances in meat products to prevent stale and spoiled meat from entering the market and causing harm to consumer health and even the ecosystem. The development of quality inspection systems based on such techniques to measure and classify white meat quality parameters will help improve the productivity and economic efficiency of the meat industry, as well as the health of consumers. Herein, a comprehensive review and discussion of the literature on fluorescence spectroscopy, color imaging and MSI is presented. The principles of these three techniques, the quality analysis models selected and the research results of non-destructive determinations of white meat quality over the last decade or so are analyzed and summarized. The review is conducted in this highly practical research field in order to provide information for future research directions. The conclusions detail how these efficient and convenient imaging and analytical techniques can be used for non-destructive quality evaluation of white meat in the laboratory and in industry.

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

Establish intelligent detection system to evaluate the sugar smoking of chicken thighs

The objective of this study was to establish a standardized color detection method to achieve low-cost, rapid, nonintrusive and accurate characterization of the color change of smoked chicken thighs during the smoking process. This study was based on machine vision technology using the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm to establish 3 colorimetric cards for the color of sugar-smoked chicken thighs. The accuracy of the 3 colorimetric cards was verified by the K-medoids algorithm and sensory analysis, respectively. Results showed that all 3 colorimetric cards had significant color gradient changes. From the K-medoids algorithm, the accuracy of the colorimetric card produced by the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm was 87.2, 95.1, and 96.7%, respectively. Meanwhile, the verification results of the sensory analysis showed that the accuracy of the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm colorimetric card was 69.4, 80.9, and 79.2%, respectively. A comparative analysis found that the colorimetric cards produced by the K-means algorithm and K-means algorithm + image noise reduction have excellent accuracy. These 2 colorimetric cards could become a suitable method for rapid, low-cost, and accurate online color monitoring of smoked chicken.

A Review on Meat Quality Evaluation Methods Based on Non-Destructive Computer Vision and Artificial Intelligence Technologies

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers’ urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Total volatile basic nitrogen and trimethylamine in muscle foods: Potential formation pathways and effects on human health

The use of total volatile basic nitrogen (TVB-N) as a quality parameter for fish is rapidly growing to include other types of meat. Investigations of meat quality have recently focused on TVB-N as an index of freshness, but little is known on the biochemical pathways involved in its generation. Furthermore, TVB-N and methylated amines have been reported to exert deterimental health effects, but the relationship between these compounds and human health has not been critically reviewed. Here, literature on the formative pathways of TVB-N has been reviewed in depth. The association of methylated amines and human health has been critically evaluated. Interventions to mitigate the effects of TVB-N on human health are discussed. TVB-N levels in meat can be influenced by the diet of an animal, which calls for careful consideration when using TVB-N thresholds for regulatory purposes. Bacterial contamination and temperature abuse contribute to significant levels of post-mortem TVB-N increases. Therefore, controlling spoilage factors through a good level of hygiene during processing and preservation techniques may contribute to a substantial reduction of TVB-N. Trimethylamine (TMA) constitutes a significant part of TVB-N. TMA and trimethylamine oxide (TMA-N-O) have been related to the pathogenesis of noncommunicable diseases, including atherosclerosis, cancers, and diabetes. Proposed methods for mitigation of TMA and TMA-N-O accumulation are discussed, which include a reduction in their daily dietary intake, control of internal production pathways by targeting gut microbiota, and inhibition of flavin monooxygenase 3 enzymes. The levels of TMA and TMA-N-O have significant health effects, and this should, therefore, be considered when evaluating meat quality and acceptability. Agreed international values for TVB-N and TMA in meat products are required. The role of feed, gut microbiota, and translocation of methylated amines to muscles in farmed animals requires further investigation.

Improving the similarity search between images of agricultural products: An approach based on fuzzy rough theory

Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods.

Software test quality evaluation based on fuzzy mathematics

The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.

Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed

This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups-0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R² and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model's polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R² and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.

Detecting cooking state of grilled chicken by electronic nose and computer vision techniques

Determination of food doneness remains a challenge for automation in the cooking industry. The complex physicochemical processes that occur during cooking require a combination of several methods for their control. Herein, we utilized an electronic nose and computer vision to check the cooking state of grilled chicken. Thermogravimetry, differential mobility analysis, and mass spectrometry were employed to deepen the fundamental insights towards the grilling process. The results indicated that an electronic nose could distinguish the odor profile of the grilled chicken, whereas computer vision could identify discoloration of the chicken. The integration of these two methods yields greater selectivity towards the qualitative determination of chicken doneness. The odor profile is matched with detected water loss, and the release of aromatic and sulfur-containing compounds during cooking. This work demonstrates the practicability of the developed technique, which we compared with a sensory evaluation, for better deconvolution of food state during cooking.

Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features

Minced meat substitution is one of the most common forms of food fraud in the meat industry. Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. However, conventional methods are based only on spectral information and ignore the spatial variability of the data. Moreover, these methods first tend to reduce the size of the data, which to some extent ignores the abstract level information and does not preserve the spatial information. Therefore, this work proposes a novel Isos-bestic wavelength reduction method for the different minced meat types, by retaining only Myoglobin pigments (Mb) in the meat spectra. A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. For each HSI cube, a set of preprocessing schemes is applied to extract the Region of Interest (ROI) and spectral preprocessing, i.e., Golay filtering. Later, these preprocessed HSI cubes are fed into a 3D-Convolutional Neural Network (3D-CNN) model for nonlinear feature extraction and classification. The proposed pipeline outperformed several state-of-the-art methods, with an overall accuracy of 94.0%.

Utilization of Bioelectrical Impedance to Predict Intramuscular Fat and Physicochemical Traits of the Beef Longissimus Thoracis et Lumborum Muscle

The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% ≤ CV ≤ 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (−0.50 ≤ r ≤ 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.