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Jeong CH, Lee SH, Kim HY. Proteolysis Analysis and Sensory Evaluation of Fermented Sausages using Strains Isolated from Korean Fermented Foods. Food Sci Anim Resour 2023; 43:877-888. [PMID: 37701739 PMCID: PMC10493556 DOI: 10.5851/kosfa.2023.e42] [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: 04/24/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 09/14/2023] Open
Abstract
We studied the proteolysis and conducted a sensory evaluation of fermented sausages using strains derived from Kimchi [Pediococcus pentosaceus-SMFM2021-GK1 (GK1); P. pentosaceus-SMFM2021-NK3 (NK3)], Doenjang [Debaryomyces hansenii-SMFM2021-D1 (D1)], and spontaneous fermented sausage [Penicillium nalgiovense-SMFM2021-S6 (S6)]. Fermented sausages were classified as commercial starter culture (CST), mixed with GK1, D1, and S6 (GKDS), and mixed with NK3, D1, and S6 (NKDS). The protein content and pH of GKDS and NKDS were significantly higher than those of CST on days 3 and 31, respectively (p<0.05). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis showed that the NKDS had higher molecular weight proteins than the GKDS and CST. The myofibrillar protein solubility of the GKDS and NKDS was significantly higher than that of the CST on day 31 (p<0.05). The GKDS displayed significantly higher pepsin and trypsin digestion than the NKDS on day 31 (p<0.05). The hardness, chewiness, gumminess, and cohesiveness of the GKDS were not significantly different from those of the CST. The GKDS exhibited the highest values for flavor, tenderness, texture, and overall acceptability. According to this study, sausages fermented using lactic acid bacteria (GK1), yeast (D1), and mold (S6) derived from Korean fermented foods displayed high proteolysis and excellent sensory evaluation results.
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Affiliation(s)
- Chang-Hwan Jeong
- Department of Animal Resources Science,
Kongju National University, Yesan 32439, Korea
| | - Sol-Hee Lee
- Department of Animal Resources Science,
Kongju National University, Yesan 32439, Korea
| | - Hack-Youn Kim
- Department of Animal Resources Science,
Kongju National University, Yesan 32439, Korea
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2
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Cao YM, Zhang Y, Yu ST, Wang KK, Chen YJ, Xu ZM, Ma ZY, Chen HL, Wang Q, Zhao R, Sun XQ, Li JT. Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp ( Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Foods 2023; 12:3154. [PMID: 37685087 PMCID: PMC10486347 DOI: 10.3390/foods12173154] [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: 07/24/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023] Open
Abstract
Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.
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Affiliation(s)
- Yi-Ming Cao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Yan Zhang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Shuang-Ting Yu
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
- Chinese Academy of Agricultural Sciences, Beijing 100181, China
| | - Kai-Kuo Wang
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Ying-Jie Chen
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Zi-Ming Xu
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Zi-Yao Ma
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Hong-Lu Chen
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Qi Wang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Ran Zhao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Xiao-Qing Sun
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Jiong-Tang Li
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
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3
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Vasconcelos L, Dias LG, Leite A, Ferreira I, Pereira E, Silva S, Rodrigues S, Teixeira A. SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology. Foods 2023; 12:foods12030470. [PMID: 36766001 PMCID: PMC9914495 DOI: 10.3390/foods12030470] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/05/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm-1 with a resolution of 4 cm-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R2 ≥ 0.95) except for the RT variable (RMSE of 0.891% and R2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R2 of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.
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Affiliation(s)
- Lia Vasconcelos
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Luís G. Dias
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Ana Leite
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Iasmin Ferreira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Etelvina Pereira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Severiano Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
| | - Sandra Rodrigues
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
| | - Alfredo Teixeira
- Mountain Reserach Center (CIMO), Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Laboratory for Sustainability and Technology in Mountain Regions, Polytechnic Institut of Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal
- Correspondence:
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4
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Dong K, Guan Y, Wang Q, Huang Y, An F, Zeng Q, Luo Z, Huang Q. Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process. Food Chem X 2022; 17:100541. [PMID: 36845518 PMCID: PMC9943752 DOI: 10.1016/j.fochx.2022.100541] [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: 08/25/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
This study examined the potential of hyperspectral techniques for the rapid detection of characteristic indicators of yak meat freshness during the oxidation of yak meat. TVB-N values were determined by significance analysis as the characteristic index of yak meat freshness. Reflectance spectral information of yak meat samples (400-1000 nm) was collected by hyperspectral technology. The raw spectral information was processed by 5 methods and then principal component regression (PCR), support vector machine regression (SVR) and partial least squares regression (PLSR) were used to build regression models. The results indicated that the full-wavelength based on PCR, SVR, and PLSR models were shown greater performance in the prediction of TVB-N content. In order to improve the computational efficiency of the model, 9 and 11 characteristic wavelengths were selected from 128 wavelengths by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The CARS-PLSR model exhibited excellent predictive power and model stability.
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Affiliation(s)
- Kai Dong
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China,Engineering Research Centre of Fujian-Taiwan Special Marine Food Processing and Nutrition of Ministry of Education, College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Yufang Guan
- The Food Processing Research Institute of Guizhou Province, Guizhou Academy of Agricultural Sciences/Potato Engineering Research Center of Guizhou Province/Guizhou Key Laboratory of Agricultural Biotechnology, Guiyang 550006, Guizhou, China
| | - Qia Wang
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China,Engineering Research Centre of Fujian-Taiwan Special Marine Food Processing and Nutrition of Ministry of Education, College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Yonghui Huang
- The Food Processing Research Institute of Guizhou Province, Guizhou Academy of Agricultural Sciences/Potato Engineering Research Center of Guizhou Province/Guizhou Key Laboratory of Agricultural Biotechnology, Guiyang 550006, Guizhou, China
| | - Fengping An
- Engineering Research Centre of Fujian-Taiwan Special Marine Food Processing and Nutrition of Ministry of Education, College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
| | - Qibing Zeng
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China,Corresponding authors at: Guizhou Medical University, Gui 'an New District, Guizhou Province 550025, China.
| | - Zhang Luo
- College of Food Science, Tibet Agriculture and Animal Husbandry University, Linzhi, Tibet Autonomous Region 860000, China,Corresponding authors at: Guizhou Medical University, Gui 'an New District, Guizhou Province 550025, China.
| | - Qun Huang
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China,Engineering Research Centre of Fujian-Taiwan Special Marine Food Processing and Nutrition of Ministry of Education, College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China,Institute for Egg Science and Technology, School of Food and Biological Engineering, Chengdu University, Chengdu 610106, China,Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang 550004, Guizhou, China,Corresponding authors at: Guizhou Medical University, Gui 'an New District, Guizhou Province 550025, China.
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5
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Pu H, Wei Q, Sun DW. Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Crit Rev Food Sci Nutr 2022; 63:1297-1313. [PMID: 36123794 DOI: 10.1080/10408398.2022.2121805] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China.,Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Ireland
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6
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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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7
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Rapid evaluation of texture parameters of Tan mutton using hyperspectral imaging with optimization algorithms. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108815] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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8
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Özdoğan G, Lin X, Sun DW. Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.044] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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9
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Effects of the curcumin-mediated photodynamic inactivation on the quality of cooked oysters with Vibrio parahaemolyticus during storage at different temperature. Int J Food Microbiol 2021; 345:109152. [PMID: 33725529 DOI: 10.1016/j.ijfoodmicro.2021.109152] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 01/23/2021] [Accepted: 03/01/2021] [Indexed: 01/26/2023]
Abstract
Photodynamic inactivation (PDI) is a promising method with multiple targets to inactivate bacteria on food using visible light. Inactivation potency of the curcumin-mediated blue light-emitting diode (LED) PDI against the pathogen Vibrio parahaemolyticus on cooked oysters and its effects on the storage quality were investigated by the microbiological, physical, chemical and histological methods during storage at 4 °C, 10 °C and 25 °C. Results showed that the PDI treatment obviously inhibited the recovery of V. parahaemolyticus on oysters during storage, and the maximal difference attained >1.0 Log10 CFU/g (> 90%) compared to control stored at 10 °C and 25 °C. Meanwhile, it displayed a potent ability (p < 0.05) to restrain the decrease of pH values, reduce the production of total volatile basic nitrogen (TVB-N), suppress the lipids oxidation, as well as retard the changes of color difference of the oysters. In addition, the PDI effectively maintained the integrity and initial attachments of muscle fibers, and hence decreased the loss of water in myofibrillar space and the texture softening of oysters during storage. On this basis, this study facilitates the understanding of the potency of bacterial inactivation and food preservation of PDI, and hence pave the way for its application in food industry.
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10
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Wang R, Ma Y, Ma Z, Du Q, Zhao Y, Chi Y. Changes in gelation, aggregation and intermolecular forces in frozen-thawed egg yolks during freezing. Food Hydrocoll 2020. [DOI: 10.1016/j.foodhyd.2020.105947] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Yang Y, Wang W, Zhuang H, Yoon SC, Jiang H. Prediction of quality traits and grades of intact chicken breast fillets by hyperspectral imaging. Br Poult Sci 2020; 62:46-52. [PMID: 32875810 DOI: 10.1080/00071668.2020.1817326] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
1. In this study, hyperspectral imaging was evaluated for its usefulness to predict quality traits and grading of intact chicken breast fillets. 2. Lightness of colour (L*) and pH of the fillets were measured as quality traits, and samples were then selected and graded to three different quality categories, i.e., dark, firm and dry (DFD), normal (NORM), and pale, soft and exudative (PSE) based on these two quality traits. Based on the prediction performance of full wavelength partial least square regression (PLSR) models, the spectral range of visible and near-infrared (Vis-NIR) was more suitable for the evaluation of quality traits and grading than the range of near-infrared (NIR). Key wavelengths of each quality trait and grade value were selected by the regression coefficient (RC) method. 3. The new key wavelength PLSR models showed good predictive performances (Rp = 0.85 and RMSEp = 2.18 for L*, Rp = 0.84, and RMSEp = 0.13 for pH, and Rp = 0.80 and RMSEp = 0.44 for quality grading). The classification accuracy for grades was 85.71% (calibration set) and 81.82% (prediction set), respectively. Finally, distribution maps showed that quality traits and grades of samples were able to be visualised. 4. These results suggested that hyperspectral imaging has the potential for quality prediction of fresh chicken meat.
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Affiliation(s)
- Y Yang
- College of Engineering, China Agricultural University , Beijing, China
| | - W Wang
- College of Engineering, China Agricultural University , Beijing, China
| | - H Zhuang
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS , Athens, GA, USA
| | - S-C Yoon
- Quality & Safety Assessment Research Unit, U. S. National Poultry Research Center, USDA-ARS , Athens, GA, USA
| | - H Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University , Nanjing, China
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12
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Zhang B, Gao S, Jia F, Liu X, Li X. Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13553] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Binhui Zhang
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Song Gao
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Fei Jia
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
| | - Xue Liu
- College of Information and Electrical Engineering China Agricultural University Beijing China
| | - Xingmin Li
- College of Food Science and Nutritional Engineering, China Agricultural University Beijing China
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13
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Lim J, Lee A, Kang J, Seo Y, Kim B, Kim G, Kim S. Non-Destructive Detection of Bone Fragments Embedded in Meat Using Hyperspectral Reflectance Imaging Technique. SENSORS 2020; 20:s20144038. [PMID: 32708061 PMCID: PMC7412504 DOI: 10.3390/s20144038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 11/27/2022]
Abstract
Meat consumption has shifted from a quantitative to a qualitative growth stage due to improved living standards and economic development. Recently, consumers have paid attention to quality and safety in their decision to purchase meat. However, foreign substances which are not normal food ingredients are unintentionally incorporated into meat. These should be eliminated as a hazard to quality or safety. It is important to find a fast, non-destructive, and accurate detection technique of foreign substance in the meat processing industry. Hyperspectral imaging technology has been regarded as a novel technology capable of providing large-scale imaging and continuous observation information on agricultural products and food. In this study, we considered the feasibility of the short-wave near infrared (SWIR) hyperspectral reflectance imaging technique to detect bone fragments embedded in chicken meat. De-boned chicken breast samples with thicknesses of 3, 6, and 9-mm and 5 bone fragments with lengths of about 20–30-mm are used for this experiment. The reflectance spectra (in the wavelength range from 987 to 1701-nm) of the 5 bone fragments embedded under the chicken breast fillet are collected. Our results suggested that these hyperspectral imaging technique is able to detect bone fragments in chicken breast, particularly with the use of a subtraction image (corresponding to image at 1153.8-nm and 1480.2-nm). Thus, the SWIR hyperspectral reflectance imaging technique can be potentially used to detect foreign substance embedded in meat.
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Affiliation(s)
- Jongguk Lim
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Ahyeong Lee
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Jungsook Kang
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Youngwook Seo
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Balgeum Kim
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Giyoung Kim
- Rural Development Administration, 310 Nongsaengmyeng-ro, Deokjin-gu, Jeonju 54875, Korea; (J.L.); (A.L.); (J.K.); (Y.S.); (B.K.); (G.K.)
| | - Seongmin Kim
- Department of Bioindustrial Machinery Engineering, College of Agriculture & Life Sciences, Chonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Korea
- Correspondence: ; Tel.: +82-63-270-2617; Fax: +82-63-270-2620
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Falkovskaya A, Gowen A. Literature review: spectral imaging applied to poultry products. Poult Sci 2020; 99:3709-3722. [PMID: 32616267 PMCID: PMC7597839 DOI: 10.1016/j.psj.2020.04.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 04/03/2020] [Indexed: 12/30/2022] Open
Abstract
Consumption of poultry products is increasing worldwide, leading to an increased demand for safe, fresh, high-quality products. To ensure consumer safety and meet quality standards, poultry products must be routinely checked for fecal matter, food fraud, microbiological contamination, physical defects, and product quality. However, traditional screening methods are insufficient in providing real-time, nondestructive, chemical and spatial information about poultry products. Novel techniques, such as hyperspectral imaging (HSI), are being developed to acquire real-time chemical and spatial information about products without destruction of samples to ensure safety of products and prevent economic losses. This literature review provides a comprehensive overview of HSI applications to poultry products. The studies used for this review were found using the Google Scholar database by searching the following terms and their synonyms: “poultry” and “hyperspectral imaging”. A total of 67 studies were found to meet the criteria. After all relevant literature was compiled, studies were grouped into categories based on the specific material or characteristic of interest to be detected, identified, predicted, or quantified by HSI. Studies were found for each of the following categories: food fraud, fecal matter detection, microbiological contamination, physical defects, and product quality. Key findings and technological advancements were briefly summarized and presented for each category. Since the first application to poultry products 20 yr ago, HSI has been shown to be a successful alternative to traditional screening methods.
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Affiliation(s)
- Anastasia Falkovskaya
- UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Aoife Gowen
- UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
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15
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Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp ( Cyprinus carpio). Foods 2020; 9:foods9040500. [PMID: 32316086 PMCID: PMC7230713 DOI: 10.3390/foods9040500] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 11/17/2022] Open
Abstract
The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400-1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky-Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.
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Luo W, Xue H, Xiong C, Li J, Tu Y, Zhao Y. Effects of temperature on quality of preserved eggs during storage. Poult Sci 2020; 99:3144-3157. [PMID: 32475451 PMCID: PMC7597647 DOI: 10.1016/j.psj.2020.01.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 01/01/2020] [Accepted: 01/09/2020] [Indexed: 01/07/2023] Open
Abstract
The effects of storage temperature (4°C, 25°C, and 35°C) on sensory quality, physicochemical properties, texture, molecular forces, flavor, and microbial indexes of preserved eggs were studied. The results showed that the sensory quality, weight loss rate, pH, and color of preserved eggs were significantly different at different storage temperatures (P < 0.05). Compared with high temperature and normal temperature storage, low temperature storage reduced weight loss rate by 55.15 and 64.1%, respectively, improved the sensory score (P < 0.05), inhibited the reduction of pH and the increase of total volatile base nitrogen (P < 0.05), and decreased the change of color (P < 0.05). During storage, there was no difference in the springiness of preserved egg white stored at different temperatures (P > 0.05). Hardness and chewiness at 3 different temperatures increased first and then decreased, and low temperature significantly inhibited the progress of these changes to a certain extent (P < 0.05). The content of ionic bond in egg white first decreased and then increased, and content of disulfide bond increased first and then decreased. Content of ionic bond in yolk decreased all the time, and high temperature could promote this change. Whatever the temperature was, the content of free amino acids in preserved egg white and yolk increased first and then decreased, and the total content of amino acids stored at different temperatures was significantly different (P < 0.05). The content of free fatty acids in yolk decreased. At the end of storage, no microorganisms were detected in 3 temperatures during the storage period of 84 D. The results showed that low temperature storage is more conducive for preservation of preserved eggs.
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Affiliation(s)
- Wenxiang Luo
- Engineering Research Center of Biomass Conversion, Ministry of Education, Nanchang University, Nanchang 330047, China; State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
| | - Hui Xue
- Engineering Research Center of Biomass Conversion, Ministry of Education, Nanchang University, Nanchang 330047, China; State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
| | - Chunhong Xiong
- State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
| | - Jianke Li
- Engineering Research Center of Biomass Conversion, Ministry of Education, Nanchang University, Nanchang 330047, China; State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China
| | - Yonggang Tu
- Jiangxi Key Laboratory of Natural Products and Functional Food, Jiangxi Agricultural University, Nanchang 330045, China.
| | - Yan Zhao
- Engineering Research Center of Biomass Conversion, Ministry of Education, Nanchang University, Nanchang 330047, China; State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China.
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Chen YW, Cai WQ, Shi YG, Dong XP, Bai F, Shen SK, Jiao R, Zhang XY, Zhu X. Effects of different salt concentrations and vacuum packaging on the shelf-stability of Russian sturgeon (Acipenser gueldenstaedti) stored at 4 °C. Food Control 2020. [DOI: 10.1016/j.foodcont.2019.106865] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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18
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Rady A, Adedeji AA. Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01719-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Chen Q, Zhang Y, Guo Y, Cheng Y, Qian H, Yao W, Xie Y, Ozaki Y. Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2019.109693] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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Yang Y, Zhao Y, Xu M, Wu N, Yao Y, Du H, Liu H, Tu Y. Changes in physico-chemical properties, microstructure and intermolecular force of preserved egg yolk gels during pickling. Food Hydrocoll 2019. [DOI: 10.1016/j.foodhyd.2018.10.016] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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21
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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Fu X, Chen J. A Review of Hyperspectral Imaging for Chicken Meat Safety and Quality Evaluation: Application, Hardware, and Software. Compr Rev Food Sci Food Saf 2019; 18:535-547. [DOI: 10.1111/1541-4337.12428] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 01/13/2023]
Affiliation(s)
- Xiaping Fu
- Faculty of Mechanical Engineering and Automation; Zhejiang Sci-Tech Univ.; 928 Second Avenue, Xiasha Higher Education Zone Hangzhou 310018 China
| | - Jinchao Chen
- Faculty of Mechanical Engineering and Automation; Zhejiang Sci-Tech Univ.; 928 Second Avenue, Xiasha Higher Education Zone Hangzhou 310018 China
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23
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Chen L, Li Z, Yu F, Zhang X, Xue Y, Xue C. Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas). FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1400-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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24
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25
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Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration. J FOOD QUALITY 2018. [DOI: 10.1155/2018/3487985] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E’jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5–95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination.
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26
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Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging. Meat Sci 2018; 139:82-90. [DOI: 10.1016/j.meatsci.2018.01.013] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 12/05/2017] [Accepted: 01/15/2018] [Indexed: 12/22/2022]
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27
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Spectral Detection Techniques for Non-Destructively Monitoring the Quality, Safety, and Classification of Fresh Red Meat. FOOD ANAL METHOD 2018. [DOI: 10.1007/s12161-018-1256-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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28
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Visible and Near-Infrared Hyperspectral Imaging for Cooking Loss Classification of Fresh Broiler Breast Fillets. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8020256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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29
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Botinestean C, Gomez C, Nian Y, Auty MAE, Kerry JP, Hamill RM. Possibilities for developing texture-modified beef steaks suitable for older consumers using fruit-derived proteolytic enzymes. J Texture Stud 2017; 49:256-261. [DOI: 10.1111/jtxs.12305] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 10/05/2017] [Accepted: 10/15/2017] [Indexed: 11/30/2022]
Affiliation(s)
| | - Carolina Gomez
- Teagasc Food Research Centre; Moorepark Fermoy Cork Ireland
| | - Yingqun Nian
- Teagasc Food Research Centre; Ashtown Dublin Ireland
- Food Packaging Group, School of Food and Nutritional Sciences; University College Cork; Cork Ireland
| | | | - Joseph P. Kerry
- Food Packaging Group, School of Food and Nutritional Sciences; University College Cork; Cork Ireland
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30
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Wu W, Chen GY, Wu MQ, Yu ZW, Chen KJ. Detection of diluted contaminants on chicken carcasses using a two-dimensional scatter plot based on a two-dimensional hyperspectral correlation spectrum. APPLIED OPTICS 2017; 56:D72-D78. [PMID: 28375374 DOI: 10.1364/ao.56.000d72] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A two-dimensional (2D) scatter plot method based on the 2D hyperspectral correlation spectrum is proposed to detect diluted blood, bile, and feces from the cecum and duodenum on chicken carcasses. First, from the collected hyperspectral data, a set of uncontaminated regions of interest (ROIs) and four sets of contaminated ROIs were selected, whose average spectra were treated as the original spectrum and influenced spectra, respectively. Then, the difference spectra were obtained and used to conduct correlation analysis, from which the 2D hyperspectral correlation spectrum was constructed using the analogy method of 2D IR correlation spectroscopy. Two maximum auto-peaks and a pair of cross peaks appeared at 656 and 474 nm. Therefore, 656 and 474 nm were selected as the characteristic bands because they were most sensitive to the spectral change induced by the contaminants. The 2D scatter plots of the contaminants, clean skin, and background in the 474- and 656-nm space were used to distinguish the contaminants from the clean skin and background. The threshold values of the 474- and 656-nm bands were determined by receiver operating characteristic (ROC) analysis. According to the ROC results, a pixel whose relative reflectance at 656 nm was greater than 0.5 and relative reflectance at 474 nm was lower than 0.3 was judged as a contaminated pixel. A region with more than 50 pixels identified was marked in the detection graph. This detection method achieved a recognition rate of up to 95.03% at the region level and 31.84% at the pixel level. The false-positive rate was only 0.82% at the pixel level. The results of this study confirm that the 2D scatter plot method based on the 2D hyperspectral correlation spectrum is an effective method for detecting diluted contaminants on chicken carcasses.
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31
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Cheng JH, Nicolai B, Sun DW. Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review. Meat Sci 2016; 123:182-191. [PMID: 27750085 DOI: 10.1016/j.meatsci.2016.09.017] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 09/26/2016] [Accepted: 09/29/2016] [Indexed: 12/20/2022]
Abstract
Muscle foods are very important for a well-balanced daily diet. Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods.
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Affiliation(s)
- Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Bart Nicolai
- MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001 Heverlee, Belgium
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering (ACFE), South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland.
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32
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Pan TT, Sun DW, Cheng JH, Pu H. Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation. Compr Rev Food Sci Food Saf 2016; 15:529-541. [DOI: 10.1111/1541-4337.12191] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2015] [Revised: 12/12/2015] [Accepted: 12/16/2015] [Indexed: 01/06/2023]
Affiliation(s)
- Ting-Tiao Pan
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Da-Wen Sun
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
- Food Refrigeration and Computerized Food Technology, Agriculture and Food Science Centre, Univ. College Dublin; National Univ. of Ireland; Belfield Dublin 4 Ireland
| | - Jun-Hu Cheng
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
| | - Hongbin Pu
- College of Food Science and Engineering, South China Univ. of Technology, Guangzhou 510641, China, and Academy of Contemporary Food Engineering; South China Univ. of Technology; Guangzhou 510641 China
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33
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Xiong Z, Sun DW, Pu H, Xie A, Han Z, Luo M. Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 2015; 179:175-81. [PMID: 25722152 DOI: 10.1016/j.foodchem.2015.01.116] [Citation(s) in RCA: 146] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 01/14/2015] [Accepted: 01/25/2015] [Indexed: 11/18/2022]
Abstract
This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction (Rp) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA-PLSR model based on the selected wavelengths was built and showed good results with Rp of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat.
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Affiliation(s)
- Zhenjie Xiong
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Da-Wen Sun
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China; Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland.
| | - Hongbin Pu
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Anguo Xie
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Zhong Han
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
| | - Man Luo
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China
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