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Feng CH, Arai H, Rodríguez-Pulido FJ. Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging. Life (Basel) 2024; 14:647. [PMID: 38792667 PMCID: PMC11122534 DOI: 10.3390/life14050647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (Rc2) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry.
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Affiliation(s)
- Chao-Hui Feng
- School of Regional Innovation and Social Design Engineering, Faculty of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan;
- RIKEN Centre for Advanced Photonics, RIKEN, 519-1399 Aramaki-Aoba, Aoba-ku, Sendai 980-0845, Japan
| | - Hirofumi Arai
- School of Regional Innovation and Social Design Engineering, Faculty of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan;
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Xu Z, Han Y, Zhao D, Li K, Li J, Dong J, Shi W, Zhao H, Bai Y. Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies. Foods 2024; 13:469. [PMID: 38338604 PMCID: PMC10855881 DOI: 10.3390/foods13030469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
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.
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Affiliation(s)
- Zeyu Xu
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Yu Han
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Dianbo Zhao
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Ke Li
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Junguang Li
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
| | - Junyi Dong
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
| | - Wenbo Shi
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
| | - Huijuan Zhao
- Henan Lianduoduo Supply Chain Management Co., Ltd., Hebi 458000, China;
| | - Yanhong Bai
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China; (Z.X.); (Y.H.); (D.Z.); (K.L.); (J.L.); (J.D.); (W.S.)
- Key Laboratory of Cold Chain Food Processing and Safety Control (Zhengzhou University of Light Industry), Ministry of Education, Zhengzhou 450000, China
- Henan Key Laboratory of Cold Chain Food Quality and Safety Control, Zhengzhou 450000, China
- Food Laboratory of Zhongyuan, Luohe 462000, China
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Saha D, Senthilkumar T, Singh CB, Manickavasagan A. Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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4
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Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci 2023; 202:109204. [PMID: 37146500 DOI: 10.1016/j.meatsci.2023.109204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/22/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
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Affiliation(s)
- Jiewen Zuo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wenlong Zou
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yahui Chen
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Daoyu Huo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Kuanglin Chao
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, United States
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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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6
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Saha D, Senthilkumar T, Sharma S, Singh CB, Manickavasagan A. Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:foods11223713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [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: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
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.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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8
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Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Kang Z, Huang T, Zeng S, Li H, Dong L, Zhang C. A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:5333. [PMID: 35891015 PMCID: PMC9315692 DOI: 10.3390/s22145333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
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Affiliation(s)
| | | | - Shan Zeng
- Correspondence: ; Tel.: +86-153-2728-6970
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10
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Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146986] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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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11
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Small genetic variation affecting mRNA isoforms associated with marbling and meat color in beef cattle. Funct Integr Genomics 2022; 22:451-466. [PMID: 35305194 DOI: 10.1007/s10142-022-00844-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 11/04/2022]
Abstract
The aim of this study was to identify mRNA isoforms and small genetic variants that may be affecting marbling and beef color in Nellore cattle. Longissimus thoracis muscle samples from 20 bulls with different phenotypes (out of 80 bulls set) for marbling (moderate (n = 10) and low (n = 10) groups) and beef color (desirable (n = 10) and undesirable (n = 9) group) traits were used to perform transcriptomic analysis using RNA sequencing. Fourteen and 15 mRNA isoforms were detected as differentially expressed (DE) (P-value ≤ 0.001) between divergent groups for marbling and meat color traits, respectively. Some of those DE mRNA isoforms have shown sites of splicing modified by small structural variants as single nucleotide variant (SNV), insertion, and/or deletion. Enrichment analysis identified metabolic pathways, such as O2/CO2 exchange in erythrocytes, tyrosine biosynthesis, and phenylalanine degradation. The results obtained suggest potential key regulatory genes associated with these economically important traits for the beef industry and for the consumer.
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12
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Rodrigues EM, Hemmer E. Trends in hyperspectral imaging: from environmental and health sensing to structure-property and nano-bio interaction studies. Anal Bioanal Chem 2022; 414:4269-4279. [PMID: 35175390 DOI: 10.1007/s00216-022-03959-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 11/01/2022]
Abstract
Hyperspectral imaging (HSI) is a technique that allows for the simultaneous acquisition of both spatial and spectral information. While HSI has been known for years in the field of remote sensing, for instance in geology, cultural heritage, or food industries, it recently emerged in the fields of nano- and micromaterials as well as bioimaging and -sensing. Herein, the attractiveness of HSI arises from the suitability for generating knowledge about environment-specific optical properties, such as photoluminescence of optical probes in a biological sample or at a single-crystal/particle level, to be leveraged into better understanding of structure-property relationships and nano-bio interactions, respectively. Moreover, given its excellent spectral resolution, HSI is highly suitable for optical multiplexing in multiple dimensions, as sought after for, e.g., high throughput biological imaging by simultaneous tracking of multiple targets. Overall, HSI is an emerging technique that has the potential to transform analytical approaches from biomedicine to advanced materials research. This Trends Article provides insight into the potential of HSI, highlighting selected examples from well-established fields including environmental monitoring and food quality control to set the stage for the discussion of emerging opportunities at the micro- and nanoscale. Herein, special focus is set on photoluminescent micro- and nanoprobes for health and spectral conversion applications.
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Affiliation(s)
- Emille Martinazzo Rodrigues
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario, K1N 6N5, Canada
| | - Eva Hemmer
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie Private, Ottawa, Ontario, K1N 6N5, Canada.
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Leighton PL, Segura JD, Lam SD, Marcoux M, Wei X, Lopez-Campos OD, Soladoye P, Dugan ME, Juarez M, PRIETO NURIA. Prediction of carcass composition and meat and fat quality using sensing technologies: A review. MEAT AND MUSCLE BIOLOGY 2021. [DOI: 10.22175/mmb.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Consumer demand for high-quality healthy food is increasing, thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.
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14
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Improving Intramuscular Fat Assessment in Pork by Synergy Between Spectral and Spatial Features in Hyperspectral Image. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-02113-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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He W, He H, Wang F, Wang S, Li R, Chang J, Li C. Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost). ANAL LETT 2021. [DOI: 10.1080/00032719.2021.1952214] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Weiwen He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Hongyuan He
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Fanglin Wang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Shuyue Wang
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Runkang Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
| | - Jing Chang
- Institute of Forensic Science, Ministry of Public Security, Beijing, China
| | - Chunyu Li
- School of Criminal Investigation, People’s Public Security University of China, Beijing, China
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