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Fan Y, Dong R, Luo Y, Tan Y, Hong H, Ji Z, Shi C. Deep learning models with optimized fluorescence spectroscopy to advance freshness of rainbow trout predicting under nonisothermal storage conditions. Food Chem 2024; 454:139774. [PMID: 38810453 DOI: 10.1016/j.foodchem.2024.139774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/06/2024] [Accepted: 05/19/2024] [Indexed: 05/31/2024]
Abstract
This study established long short-term memory (LSTM), convolution neural network long short-term memory (CNN_LSTM), and radial basis function neural network (RBFNN) based on optimized excitation-emission matrix (EEM) from fish eye fluid to predict freshness changes of rainbow trout under nonisothermal storage conditions. The method of residual analysis, core consistency diagnostics, and split-half analysis of parallel factor analysis was used to optimize EEM data, and two characteristic components were extracted. LSTM, CNN_LSTM, and RBFNN models based on characteristic components of EEM used to predict the freshness indices. The results demonstrated the relative errors of RBFNN models with an R2 above 0.96 and relative errors less than 10% for K-value, total viable counts, and volatile base nitrogen, which were better than those of LSTM and CNN_LSTM models. This study presents a novel approach for predicting the freshness of rainbow trout under nonisothermal storage conditions.
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Affiliation(s)
- Yanwei Fan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
| | - Ruize Dong
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
| | - Yongkang Luo
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Yuqing Tan
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Hui Hong
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
| | - Zengtao Ji
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
| | - Ce Shi
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; Key Laboratory of Cold Chain Logistics Technology for Agro-product, Ministry of Agriculture and Rural Affairs, Beijing 100097, China.
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Zhan F, Li Z, Pan D, Benjakul S, Li X, Zhang B. Investigating the migration hypothesis: Effects of trypsin-like protease on the quality of muscle proteins of red shrimp ( Solenocera crassicornis) during cold storage. Food Chem X 2023; 20:100906. [PMID: 38144848 PMCID: PMC10740068 DOI: 10.1016/j.fochx.2023.100906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 12/26/2023] Open
Abstract
This study aimed to investigate the effect of trypsin-like protease (TLP) on the quality of muscle proteins in red shrimp (Solenocera crassicornis) during cold storage. The results indicated that the activity of TLP decreased significantly in the head of shrimp but increased significantly in the muscle tissues during the cold storage. The myofibril fragmentation index (MFI) value of intact shrimp was significantly higher than that of beheaded shrimp, while the Ca2+-ATPase activity of intact shrimp was significantly lower than that of beheaded shrimp. SDS-PAGE analysis showed that the molecular weight of purified TLP from the shrimp head was about 24 kDa, and the TLP showed high activity at 50 °C and pH 8, indicating that the TLP belongs to the trypsin family. Results from in vitro simulation experiments indicated that the process of TLP incubation significantly reduced the particle size and enlarged the distribution of myofibrillar proteins (MPs) in shrimp muscle tissues. The comparisons were made with respect to the control samples. It can be inferred that TLP migrated from the shrimp head to the muscle tissues during storage and thus promoted the degradation of MPs in red shrimp. The beheading treatment could be an effective mean to maintain better quality and extend the commercialization of shrimp products.
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Affiliation(s)
- Feili Zhan
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
- College of Food Science and Pharmacy, Ningbo University, Ningbo 315832, China
| | - Zhipeng Li
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
| | - Daodong Pan
- College of Food Science and Pharmacy, Ningbo University, Ningbo 315832, China
| | - Soottawat Benjakul
- International Center of Excellence in Seafood Science and Innovation, Faculty of Agro-Industry, Prince of Songkla University, Songkhla 90112, Thailand
| | - Xuepeng Li
- College of Food Science and Engineering, Bohai University, Jinzhou, Liaoning 121013, China
| | - Bin Zhang
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province, College of Food Science and Pharmacy, Zhejiang Ocean University, Zhoushan 316022, China
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Rahman MM, Shibata M, Nakazawa N, Rithu MNA, Okazaki E, Nakauchi S. Potential of fluorescence fingerprints for fish meat authentication: Differences in freshness evaluation in white and dark meat at frozen state. J Food Sci 2023; 88:5339-5354. [PMID: 37942954 DOI: 10.1111/1750-3841.16825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/10/2023]
Abstract
As dark meat has a faster deterioration rate and its unintentional mixing occurs during processing, it is crucial to know the status and freshness indicators of dark meat to ensure fishery product quality. In this method, fluorescence fingerprints (FFs) was applied as a rapid and noninvasive quality authentication method to determine differences between white and dark meat in the evaluation of freshness indicators at frozen state. Spotted mackerel (Scomber australasicus) fish chunks with different postmortem conditions (0-40 h ice stored) were obtained and frozen. A new generation of fluorescence spectrophotometer (F-7100) was used to acquire FFs of the frozen fish chunks (containing white and dark meat). Adenosine triphosphate metabolites and pH were determined in both white and dark meat using their relevant biochemical methods. Higher K-values in dark meat might be attributed to a higher accumulation rate of inosine (HxR) in dark meat than in white meat. The pH decrease rate in white meat was higher than that in dark meat during postmortem ice storage periods of fish. Principal component analysis of FFs spectra demonstrated clear discrimination (PC1 + PC2 = 91.7%) between white and dark meat of frozen fish due to the influence of freshness parameters based on the fluorescence features of fish meat. Furthermore, partial least squares regression validation models revealed that freshness indicators of white meat could be predicted more accurately at the frozen state than those of dark meat. This method could be applied during the processing of fishery products, thereby facilitating quality control activities and making it a promising authentication tool for the fisheries industries.
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Affiliation(s)
- Md Mizanur Rahman
- Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi, Japan
- Department of Fisheries Technology, Patuakhali Science and Technology University, Dumki, Patuakhali, Bangladesh
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato, Tokyo, Japan
| | - Mario Shibata
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato, Tokyo, Japan
| | - Naho Nakazawa
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato, Tokyo, Japan
| | - Mst Nazira Akhter Rithu
- Department of Ocean Sciences, Tokyo University of Marine Science and Technology, Minato, Tokyo, Japan
| | - Emiko Okazaki
- Department of Food Science and Technology, Tokyo University of Marine Science and Technology, Minato, Tokyo, Japan
| | - Shigeki Nakauchi
- Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi, Japan
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Radotić K, Stanković M, Bartolić D, Natić M. Intrinsic Fluorescence Markers for Food Characteristics, Shelf Life, and Safety Estimation: Advanced Analytical Approach. Foods 2023; 12:3023. [PMID: 37628022 PMCID: PMC10453546 DOI: 10.3390/foods12163023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Food is a complex matrix of proteins, fats, minerals, vitamins, and other components. Various analytical methods are currently used for food testing. However, most of the used methods require sample preprocessing and expensive chemicals. New analytical methods are needed for quick and economic measurement of food quality and safety. Fluorescence spectroscopy is a simple and quick method to measure food quality, without sample preprocessing. This technique has been developed for food samples due to the application of a front-face measuring setup. Fluorescent compounds-fluorophores in the food samples are highly sensitive to their environment. Information about molecular structure and changes in food samples is obtained by the measurement of excitation-emission matrices of the endogenous fluorophores and by applying multivariate chemometric tools. Synchronous fluorescence spectroscopy is an advantageous screening mode used in food analysis. The fluorescent markers in food are amino acids tryptophan and tyrosine; the structural proteins collagen and elastin; the enzymes and co-enzymes NADH and FAD; vitamins; lipids; porphyrins; and mycotoxins in certain food types. The review provides information on the principles of the fluorescence measurements of food samples and the advantages of this method over the others. An analysis of the fluorescence spectroscopy applications in screening the various food types is provided.
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Affiliation(s)
- Ksenija Radotić
- Institute for Multidisciplinary Research, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia; (M.S.); (D.B.)
- Center for Green Technologies, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia
| | - Mira Stanković
- Institute for Multidisciplinary Research, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia; (M.S.); (D.B.)
- Center for Green Technologies, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia
| | - Dragana Bartolić
- Institute for Multidisciplinary Research, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia; (M.S.); (D.B.)
- Center for Green Technologies, University of Belgrade, Kneza Višeslava 1, 11000 Belgrade, Serbia
| | - Maja Natić
- Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia;
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Zhao J, Ni Y, Tan L, Zhang W, Zhou H, Xu B. Recent advances in meat freshness "magnifier": fluorescence sensing. Crit Rev Food Sci Nutr 2023:1-17. [PMID: 37555377 DOI: 10.1080/10408398.2023.2241553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
To address the serious waste of meat resources and food safety problems caused by the decrease in meat freshness due to the action of microorganisms and enzymes, a low-cost, time-saving and high-efficiency freshness monitoring method is urgently needed. Fluorescence sensing could act as a "magnifier" for meat freshness monitoring due to its ability to sense characteristic signal produced by meat spoilage. Here, the magnification mechanism of meat freshness via sensing the water activity, adenosine triphosphate, hydrogen ion, total volatile basic nitrogen, hydrogen sulfide, bioamines was comprehensively analyzed. The existing "magnifier" forms including paper chips, films, labels, arrays, probes, and hydrogels as well as the application in livestock, poultry and aquatic meat freshness monitoring were reviewed. Future research directions involving innovation of principles, visualization and quantification capabilities for various meats freshness were provided. By critically evaluating the potential and limitations, efficient and reliable meat freshness monitoring strategies wish to be developed for the post-epidemic era.
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Affiliation(s)
- Jinsong Zhao
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Yongsheng Ni
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Lijun Tan
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Wendi Zhang
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Hui Zhou
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
| | - Baocai Xu
- School of Food and Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
- Engineering Research Center of Bio-Process of Ministry of Education, School of Food & Biological Engineering, Hefei University of Technology, Hefei, Anhui Province, China
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Zhou T, Ding YX, Benjakul S, Shui SS, Zhang B. Characterization of endogenous enzymes in sword prawn (Parapenaeopsis hardwickii) and their effects on the quality of muscle proteins during frozen storage. Lebensm Wiss Technol 2023. [DOI: 10.1016/j.lwt.2023.114563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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SHIBATA MARIO. III-1. Principle of fluorescence fingerprint and its application to food. NIPPON SUISAN GAKKAISHI 2022; 88:415-415. [DOI: 10.2331/suisan.wa2979-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Li Z, Zhou T, Wu Y, Shui S, Tu C, Benjakul S, Zhang B. Investigation of the activity of cathepsin B in red shrimp (
Solenocera crassicornis
) and its relation to the quality of muscle proteins during chilled and frozen storage. J Food Sci 2022; 87:1610-1623. [DOI: 10.1111/1750-3841.16105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/15/2022] [Accepted: 02/10/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Zhipeng Li
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Ting Zhou
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Yingru Wu
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Shanshan Shui
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Chuanhai Tu
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
| | - Soottawat Benjakul
- International Center of Excellence in Seafood Science and Innovation Faculty of Agro‐Industry Prince of Songkla University Songkhla Thailand
| | - Bin Zhang
- Key Laboratory of Health Risk Factors for Seafood of Zhejiang Province College of Food Science and Pharmacy Zhejiang Ocean University Zhoushan China
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Jovanska L, Chiu CH, Yeh YC, Chiang WD, Hsieh CC, Wang R. Development of a PCL-PEO double network colorimetric pH sensor using electrospun fibers containing Hibiscus rosa sinensis extract and silver nanoparticles for food monitoring. Food Chem 2022; 368:130813. [PMID: 34411860 DOI: 10.1016/j.foodchem.2021.130813] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 07/31/2021] [Accepted: 08/05/2021] [Indexed: 02/07/2023]
Abstract
Major anthocyanin, cyanidin-3-sophoroside (318.1 mg/mL), and other minor copigments were identified in the ethanol extract of Hibiscus rosa sinensis. The extracts can be coelectrospun with polycaprolactone and polyethylene oxide into fiber mats and were sensitive to pH changes from 1 to 13 with a unique color code (ΔE > 5). The pH sensor was used to monitor shrimp quality under isothermal conditions to obtain the respective activation energy (Ea in kJ/mol) of the sensors' color-change response (20.2), measured pH (20.6), and trimethylamine nitrogen (24.6), indole (27.1), and total microbial counts (30.8). Together with the Pearson correlation coefficient, the results showed high correlations between the sensors' color change and other quality parameters (p < 0.001). The regression equation developed by conducting the kinetic analysis was also suitable for predicting shrimp quality at refrigeration temperatures (4-10 °C) and can be used as a marker to monitor shrimp quality by visually inspecting the item condition.
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Affiliation(s)
- Lavernchy Jovanska
- Department of Animal Science and Biotechnology, Tunghai University, No. 1727, Sec. 4 Taiwan Boulevard, Xitun District, Taichung 40704, Taiwan; Department of Food Technology, Faculty of Agricultural Technology, Soegijapranata Catholic University, Semarang, Indonesia
| | - Chun-Hui Chiu
- Graduate Institute of Health Industry and Technology, Research Center for Chinese Herbal Medicine and Research Center for Food and Cosmetic Safety, College of Human Ecology, Chang Gung University of Science and Technology, Taoyuan City, Taiwan; Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Keelung City, Taiwan
| | - Yi-Cheun Yeh
- Institute of Polymer Science and Engineering, National Taiwan University, Taipei City, Taiwan
| | - Wen-Dee Chiang
- Department of Food Science, Tunghai University, Taichung, Taiwan
| | - Chang-Chi Hsieh
- Department of Animal Science and Biotechnology, Tunghai University, No. 1727, Sec. 4 Taiwan Boulevard, Xitun District, Taichung 40704, Taiwan
| | - Reuben Wang
- Master of Public Health Program, College of Public Health, National Taiwan University, Taipei City, Taiwan; Institute of Food Safety and Health, College of Public Health, National Taiwan University, Taipei City, Taiwan.
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Fan KJ, Su WH. Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review. BIOSENSORS 2022; 12:bios12020076. [PMID: 35200337 PMCID: PMC8869398 DOI: 10.3390/bios12020076] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 05/12/2023]
Abstract
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.
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