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

Shi, Yinyan; Wang, Xiaochan; Borhan, Saidul; Young, J. M.; Newman, David J.; Berg, E. P.; Sun, Xiaorui

doi:10.5851/kosfa.2021.e25

Cited by 41 publications

(26 citation statements)

References 124 publications

(135 reference statements)

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“…Image processing and machine learning algorithms have rapidly increased in food safety and food security applications, especially in meat processing (Li et al, 2021;Shi et al, 2021). Several studies have used threshold-based algorithms to detect contaminated areas, but determining threshold values is challenging, and inappropriate thresholds can contribute to false-positive and false-negative results.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services

et al. 2022

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Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in food-related services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system’s intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes, resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry.

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Section: Introductionmentioning

confidence: 99%

Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services

et al. 2022

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“…The most often used method of multivariate analysis for this technique is partial least-squares regression (PLSR) analysis [62]. Several authors additionally use chemometrics to extract representative information from Raman spectra of meat and analyze the relation between the molecular structure and different radical groups to determine and assess meat quality (Figure 6) [63,64].…”

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

“…When developing spectroscopic equipment for assessment of meat quality and composition, a special attention is being given to RSMs, as these methods do not require long and labor intensive sample preparation, are rapid and easy to perform (analysis can be done within several seconds). A trend towards promotion of the real-time automated control and quality control directly in production is seen worldwide [63]. At present, portable Raman spectroscopes with a robust water-proof casing for sensor protection have been developed for the use in the meat industry [100].…”

Section: Application Of Raman Spectroscopy In the Meat Industrymentioning

confidence: 99%

Raman spectroscopic techniques for meat analysis: A review

Пчелкина

Чернуха

Fedulova

et al. 2022

Teor. prakt. pererab. mâsa (Print)

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Raman spectroscopy (vibrational spectroscopy) proved to be an effective analytical approach in the field of geology, semiconductors, materials and polymers. Over the past decade, Raman spectroscopy has attracted the attention of researchers as a non-destructive, highly sensitive, fast and eco-friendly method and has demonstrated the unique capabilities of food analysis. The use of Raman spectroscopic methods (RSMs) to assess the quality of meat and finished products is rapidly expanding. From the analysis of one sample, you can get a large amount of information about the structure of proteins, the composition of fatty acids, organoleptic parameters, autolysis and spoilage indicators, authentication of raw materials, technological properties. An important advantage of the method is the comparability of the results obtained with the data of traditional analytical methods. Traditional methods of determining the quality of meat are often time-consuming, expensive and lead to irreversible damage to a sample. It is difficult to use them in production conditions directly on the meat processing lines. Technological advances have made it possible to develop portable Raman spectroscopes to use directly in production. The article presents the basic principles of Raman spectroscopy, system atizes the results of the use of RSMs for the analysis of meat quality from different types of slaughter animals and provides tools for analyzing the data of the obtained spectra. Raman spectra have many dependent variables, so chemometric assays are used to work with them. Literature analysis has shown that currently there is no unified database of meat spectra in the world, standardized protocols for conducting research and processing the obtained results. In Russia, the use of RSMs is a new,

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“…On the other hand, a wide range of analytical methods has been investigated over the years to characterize meat and meat products in terms of quality, safety, and authenticity. Yet, many of the conventional preservation, processing, and analytical methods are unable to cope with the well-known challenges (e.g., short shelf life and large heterogeneity) faced by the meat industry, making it difficult to preserve, process, and analyse these products [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Echegaray

Hassoun²,

Jagtap

et al. 2022

Applied Sciences

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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.

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

Cited by 41 publications

References 124 publications

Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services

Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services

Raman spectroscopic techniques for meat analysis: A review

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

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