1
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Chen S, Wang S. Assessment of comprehensive PAH index in roasted Tan lamb by two-dimensional correlation spectroscopy combined with hyperspectral imaging. J Food Sci 2024; 89:4791-4805. [PMID: 39037347 DOI: 10.1111/1750-3841.17135] [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: 12/28/2023] [Revised: 04/24/2024] [Accepted: 05/03/2024] [Indexed: 07/23/2024]
Abstract
Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near-infrared (Vis-NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two-dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579-737-631-449 nm. The results indicated that competitive adaptive reweighted sampling-multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426 µg/kg, R-squared prediction of 0.8469, and root mean square error of prediction of 2.4119 µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis-NIR HSI coupled with multivariate analysis methods.
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Affiliation(s)
- Sichun Chen
- School of Food Science and Engineering, Ningxia University, Yinchuan, China
| | - Songlei Wang
- School of Food Science and Engineering, Ningxia University, Yinchuan, China
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2
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Xie A, Zhang Y, Wu H, Chen M. Monitoring the Aging and Edible Safety of Pork in Postmortem Storage Based on HSI and Wavelet Transform. Foods 2024; 13:1903. [PMID: 38928844 PMCID: PMC11202915 DOI: 10.3390/foods13121903] [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: 05/25/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
The process of meat postmortem aging is a complex one, in which improved tenderness and aroma coincide with negative effects such as water loss and microbial growth. Determining the optimal postmortem storage time for meat is crucial but also challenging. A new visual monitoring technique based on hyperspectral imaging (HSI) has been proposed to monitor pork aging progress. M. longissimus thoracis from 15 pigs were stored at 4 °C for 12 days while quality indexes and HSI spectra were measured daily. Based on changes in physical and chemical indicators, 100 out of the 180 pieces of meat were selected and classified into rigor mortis, aged, and spoilt meat. Discrete wavelet transform (DWT) technology was used to improve the accuracy of classification. DWT separated approximate and detailed signals from the spectrum, resulting in a significant increase in classification speed and precision. The support vector machine (SVM) model with 70 band spectra achieved remarkable classification accuracy of 97.06%. The study findings revealed that the aging and microbial spoilage process started at the edges of the meat, with varying rates from one pig to another. Using HSI and visualization techniques, it was possible to evaluate and portray the postmortem aging progress and edible safety of pork during storage. This technology has the potential to aid the meat industry in making informed decisions on the optimal storage and cooking times that would preserve the quality of the meat and ensure its safety for consumption.
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Affiliation(s)
- Anguo Xie
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
| | - Yu Zhang
- School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473000, China;
| | - Han Wu
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
| | - Meng Chen
- Zhang Zhongjing School of Chinese Medicine, Nanyang Institute of Technology, Nanyang 473000, China; (H.W.); (M.C.)
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3
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Dong F, Bi Y, Hao J, Liu S, Yi W, Yu W, Lv Y, Cui J, Li H, Xian J, Chen S, Wang S. A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM. Food Chem 2024; 440:138040. [PMID: 38103505 DOI: 10.1016/j.foodchem.2023.138040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.
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Affiliation(s)
- Fujia Dong
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yongzhao Bi
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University (BTBU), Beijing 100048, China
| | - Jie Hao
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sijia Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Weiguo Yi
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Wenjie Yu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Hui Li
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jinhua Xian
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sichun Chen
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Songlei Wang
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
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4
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Shen Y, Chen Y, Zhang S, Wu Z, Lu X, Liu W, Liu B, Zhou X. Smartphone-based digital phenotyping for genome-wide association study of intramuscular fat traits in longissimus dorsi muscle of pigs. Anim Genet 2024; 55:230-237. [PMID: 38290559 DOI: 10.1111/age.13401] [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: 12/11/2023] [Revised: 12/11/2023] [Accepted: 01/17/2024] [Indexed: 02/01/2024]
Abstract
Intramuscular fat (IMF) content and distribution significantly contribute to the eating quality of pork. However, the current methods used for measuring these traits are complex, time-consuming and costly. To simplify the measurement process, this study developed a smartphone application (App) called Pork IMF. This App serves as a rapid and portable phenotyping tool for acquiring pork images and extracting the image-based IMF traits through embedded deep-learning algorithms. Utilizing this App, we collected the IMF traits of the longissimus dorsi muscle in a crossbred population of Large White × Tongcheng pigs. Genome-wide association studies detected 13 and 16 SNPs that were significantly associated with IMF content and distribution, respectively, highlighting NR2F2, MCTP2, MTLN, ST3GAL5, NDUFAB1 and PID1 as candidate genes. Our research introduces a user-friendly digital phenotyping technology for quantifying IMF traits and suggests candidate genes and SNPs for genetic improvement of IMF traits in pigs.
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Affiliation(s)
- Yang Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yuxi Chen
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Shufeng Zhang
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Ze Wu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Xiaoyu Lu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Weizhen Liu
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Bang Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xiang Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
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5
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Stewart SM, Polkinghorne R, Pethick DW, Pannier L. Carcass assessment and value in the Australian beef and sheepmeat industry. Anim Front 2024; 14:5-14. [PMID: 38633318 PMCID: PMC11018706 DOI: 10.1093/af/vfae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Affiliation(s)
- Sarah M Stewart
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
| | | | - David W Pethick
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
| | - Liselotte Pannier
- School of Agriculture, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Perth 6150, Australia
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6
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Yu Y, Chen W, Zhang H, Liu R, Li C. Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging. Foods 2024; 13:973. [PMID: 38611279 PMCID: PMC11011688 DOI: 10.3390/foods13070973] [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: 02/15/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024] Open
Abstract
The detection of the storage state of frozen meat, especially meat frozen-thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen-stored (F-S), frozen-thawed three times (F-T-3) and frozen-thawed five times (F-T-5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze-thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.
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Affiliation(s)
- Yuewen Yu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Wenliang Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hanwen Zhang
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Rong Liu
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Chenxi Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
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7
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Wei W, Zhang F, Fu F, Sang S, Qiao Z. Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:9584. [PMID: 38067956 PMCID: PMC10708565 DOI: 10.3390/s23239584] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023]
Abstract
The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard-Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.
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Affiliation(s)
- Wensong Wei
- Key Laboratory of Agricultural Product Processing, Ministry of Agriculture/Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China
- Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China; (F.Z.); (F.F.); (S.S.); (Z.Q.)
| | - Fengjuan Zhang
- Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China; (F.Z.); (F.F.); (S.S.); (Z.Q.)
| | - Fangting Fu
- Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China; (F.Z.); (F.F.); (S.S.); (Z.Q.)
| | - Shuo Sang
- Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China; (F.Z.); (F.F.); (S.S.); (Z.Q.)
| | - Zhen Qiao
- Zibo Institute for Digital Agriculture and Rural Research, Zibo 255051, China; (F.Z.); (F.F.); (S.S.); (Z.Q.)
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8
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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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9
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Pu H, Yu J, Sun DW, Wei Q, Shen X, Wang Z. Distinguishing Fresh and Frozen-thawed Beef Using Hyperspectral Imaging Technology Combined with Convolutional Neural Networks. Microchem J 2023. [DOI: 10.1016/j.microc.2023.108559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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10
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Dixit Y, Reis M. Hyperspectral imaging for assessment of total fat in salmon fillets: A comparison between benchtop and snapshot systems. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111212] [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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11
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Hassoun A, Jagtap S, Garcia-Garcia G, Trollman H, Pateiro M, Lorenzo JM, Trif M, Rusu AV, Aadil RM, Šimat V, Cropotova J, Câmara JS. Food quality 4.0: From traditional approaches to digitalized automated analysis. J FOOD ENG 2023. [DOI: 10.1016/j.jfoodeng.2022.111216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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12
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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: 4] [Impact Index Per Article: 2.0] [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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13
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Lv Y, Dong F, Cui J, Hao J, Luo R, Wang S, Rodas-Gonzalez A, Liu S. Fusion of Spectral and Textural Data of Hyperspectral Imaging for Glycine Content Prediction in Beef Using SFCN Algorithms. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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14
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Dong F, Bi Y, Hao J, Liu S, Lv Y, Cui J, Wang S, Han Y, Rodas-González A. A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef. BIOSENSORS 2022; 12:bios12111043. [PMID: 36421161 PMCID: PMC9688476 DOI: 10.3390/bios12111043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 05/31/2023]
Abstract
Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef.
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Affiliation(s)
- Fujia Dong
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yongzhao Bi
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Jie Hao
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Sijia Liu
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Songlei Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yafang Han
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Argenis Rodas-González
- Department of Animal Science, Faculty of Agricultural and Food Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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15
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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; 11:3713. [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] [Grants] [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
| | - 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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16
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Pewan SB, Otto JR, Kinobe RT, Adegboye OA, Malau-Aduli AEO. Fortification of diets with omega-3 long-chain polyunsaturated fatty acids enhances feedlot performance, intramuscular fat content, fat melting point, and carcass characteristics of Tattykeel Australian White MARGRA lambs. Front Vet Sci 2022; 9:933038. [PMID: 36172615 PMCID: PMC9510752 DOI: 10.3389/fvets.2022.933038] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/22/2022] [Indexed: 11/17/2022] Open
Abstract
Meat eating quality indices such as intramuscular fat content (IMF) and fat melting point (FMP) of the Longissimus thoracis et lumborum muscle and the feedlot performance, carcass traits, and commercial wholesale cuts of lot-fed Tattykeel Australian White (TAW) MARGRA lambs as a result of dietary fortification of the diet with omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) were evaluated. A total of 75 TAW MARGRA lambs at 6 months of age with an average liveweight of 30 ± 1.2 kg were used. The lambs were randomly allocated to the following three dietary treatments of 25 lambs each in a 47-day feeding trial using a completely randomized experimental design: (1) control diet of hay plus pellets without omega-3 oil, (2) hay plus commercial whole grain pellets (MSM) without omega-3 oil, and (3) hay plus pellets fortified with omega-3 oil. It was hypothesized that dietary supplementation with omega-3 fortified pellets will improve feedlot performance, meat-eating quality indices of IMF, FMP, and carcass characteristics. Lot-fed lambs on the MSM whole grain had the highest feed intake of 1.69 kg/day, followed by the control at 1.57 kg/day and the lowest in the omega-3 diet at 1.01 kg/day (p = 0.0001). However, the omega-3 diet had the highest average daily gain of 230 g/head/day (p = 0.0001), indicating the greatest feed efficiency since it had the best growth response with minimal feed intake. Post-slaughter evaluation of the Longissimus thoracis et lumborum muscle revealed significant treatment variations in IMF (p = 0.0001), FMP (p = 0.0001), pH (p = 0.0380), and wholesale French rack primal cut (p = 0.0001). Strong correlations (p < 0.05) between liveweight, temperature, pH, FMP, and IMF were observed. Similarly, significant correlations between carcass characteristics of total saleable meat yield, lean trim, fat trims, bones, and leg shank were evident (p < 0.05). However, there were no treatment differences in the final liveweight, GR fat depth, hot standard carcass weight, or dressing percentage. The findings indicate that feedlot performance, meat-eating quality traits such as IMF and FMP, and commercial wholesale French rack cuts can be further improved during feedlot finishing of TAW MARGRA lambs through dietary supplementation with omega-3 oils, and hence the tested hypothesis of improved meat quality attributes is partially confirmed.
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Affiliation(s)
- Shedrach Benjamin Pewan
- Animal Genetics and Nutrition, Veterinary Sciences Discipline, College of Public Health, Medical and Veterinary Sciences, Division of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
- National Veterinary Research Institute, Vom, Plateau State, Nigeria
| | - John Roger Otto
- Animal Genetics and Nutrition, Veterinary Sciences Discipline, College of Public Health, Medical and Veterinary Sciences, Division of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
| | - Robert Tumwesigye Kinobe
- Animal Genetics and Nutrition, Veterinary Sciences Discipline, College of Public Health, Medical and Veterinary Sciences, Division of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
| | - Oyelola Abdulwasiu Adegboye
- Public Health and Tropical Medicine Discipline, College of Public Health, Medical and Veterinary Sciences, Division of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
| | - Aduli Enoch Othniel Malau-Aduli
- Animal Genetics and Nutrition, Veterinary Sciences Discipline, College of Public Health, Medical and Veterinary Sciences, Division of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
- *Correspondence: Aduli Enoch Othniel Malau-Aduli
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17
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Li D, Li L. Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:5809. [PMID: 35957365 PMCID: PMC9370975 DOI: 10.3390/s22155809] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/30/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction (Rp2) was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the Rp2 was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.
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Affiliation(s)
| | - Lina Li
- Correspondence: ; Tel.: +86-13-395023485
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18
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A decision fusion method based on hyperspectral imaging and electronic nose techniques for moisture content prediction in frozen-thawed pork. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Modzelewska-Kapituła M, Jun S. The application of computer vision systems in meat science and industry - A review. Meat Sci 2022; 192:108904. [PMID: 35841854 DOI: 10.1016/j.meatsci.2022.108904] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/19/2022]
Abstract
Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.
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Affiliation(s)
- Monika Modzelewska-Kapituła
- Department of Meat Technology and Chemistry, Faculty of Food Sciences, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-719 Olsztyn, Poland.
| | - Soojin Jun
- Department of Human Nutrition, Food and Animal Sciences, University of Hawaii, Honolulu, HI 96822, USA
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20
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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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21
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Allen P. Recent developments in the objective measurement of carcass and meat quality for industrial application. Meat Sci 2021; 181:108601. [PMID: 34182344 DOI: 10.1016/j.meatsci.2021.108601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 10/21/2022]
Abstract
This paper summarises the contents of this Special Edition. The papers cover a range of advanced technologies for the objective measurement of carcass characteristics that influence the yield and potential eating quality of beef and lamb carcasses. All the research has been carried out in Australia and New Zealand and has been centrally funded with collaboration between various groups. This Special Edition is timely since the meat industry is coming under pressure on environmental grounds in addition to health warnings about excessive meat consumption. In this respect it is encouraging that so many of the papers relate to eating quality. The emphasis on objective methods is also important as moving away from traditional subjective grading will improve accuracy and consistency and thereby increase efficiency. Some differences in the approach taken in other parts of the world are discussed.
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Affiliation(s)
- P Allen
- Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland.
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22
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Gardner GE, Apps R, McColl R, Craigie CR. Objective measurement technologies for transforming the Australian & New Zealand livestock industries. Meat Sci 2021; 179:108556. [PMID: 34023677 DOI: 10.1016/j.meatsci.2021.108556] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 01/04/2023]
Abstract
This paper introduces the special edition of Meat Science focused upon the development, calibration and validation of technologies that measure traits influencing meat eating quality, or carcass fat and lean composition. These papers reflect the combined research efforts of groups in Australia, through the Advanced Livestock Measurement Technologies project, and New Zealand through AgResearch. We describe the various technologies being developed, how these devices are being trained upon common gold-standard measurements, and how their outputs are being simultaneously integrated into existing industry systems. We outline how this enhances the industry uptake and adoption of these technologies, and how this is further accelerated by education programs and strategic industry investment into their commercialisation.
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Affiliation(s)
- G E Gardner
- Murdoch University, School of Veterinary & Life Sciences, Western Australia 6150, Australia.
| | - R Apps
- Meat and Livestock Australia, North Sydney, NSW 2060, Australia
| | - R McColl
- Meat Industry Association of New Zealand, 154 Featherston Street, Wellington 6011, New Zealand
| | - C R Craigie
- AgResearch Limited, 1365 Springs Road, Lincoln 7674, New Zealand
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