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Stewart SM, Corlett MT, Gardner GE, Ura A, Nishiyama K, Shibuya T, McGilchrist P, Steel CC, Furuya A. Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb. Meat Sci 2024; 214:109517. [PMID: 38696994 DOI: 10.1016/j.meatsci.2024.109517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/04/2024]
Abstract
The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.
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Affiliation(s)
- S M Stewart
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia.
| | - M T Corlett
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia
| | - G E Gardner
- Advanced Livestock Measurement Technologies (ALMTech) Project, Murdoch University, School of Agriculture, Western Australia 6150, Australia
| | - A Ura
- SOMA Optics, Ltd., Tokyo 190-0182, Japan
| | | | - T Shibuya
- Fujihira Industry Co., Ltd. (FHK), Tokyo 113-0033, Japan
| | - P McGilchrist
- Universiy of New England, School of Environmental and Rural Sciences, Armidale, NSW 2350, Australia
| | - C C Steel
- Universiy of New England, School of Environmental and Rural Sciences, Armidale, NSW 2350, Australia
| | - A Furuya
- Fujihira Industry Co., Ltd. (FHK), Tokyo 113-0033, Japan
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2
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Yan X, Liu S, Wang S, Cui J, Wang Y, Lv Y, Li H, Feng Y, Luo R, Zhang Z, Zhang L. Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging. Foods 2024; 13:424. [PMID: 38338559 PMCID: PMC10855435 DOI: 10.3390/foods13030424] [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: 11/17/2023] [Revised: 12/26/2023] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.
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Affiliation(s)
- Xiuwei Yan
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Sijia Liu
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Songlei Wang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Jiarui Cui
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yongrui Wang
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Yu Lv
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Hui Li
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Yingjie Feng
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
| | - Ruiming Luo
- College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China; (J.C.); (Y.W.); (R.L.)
| | - Zhifeng Zhang
- College of Aquaculture, Huazhong Agricultural University, Wuhan 430070, China;
| | - Lei Zhang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; (X.Y.); (S.L.); (Y.L.); (H.L.); (Y.F.); (L.Z.)
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3
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Johnson PL, McEwan JC, Hickey SM, Dodds KG, Hitchman S, Agnew MP, Bain WE, Newman SAN, Pickering NK, Craigie CR, Clarke SM. Potential of in-plant intramuscular fat predictions to enable sheep breeders to incorporate consumer preferences in breeding programmes. Meat Sci 2023; 199:109140. [PMID: 36822055 DOI: 10.1016/j.meatsci.2023.109140] [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: 08/10/2022] [Revised: 02/09/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
The inclusion of eating quality traits in sheep genetic improvement programmes is desirable. Intramuscular fat (IMF) plays a key role in ensuring consumer satisfaction when eating lamb, but genetic progress for IMF is constrained by a lack of routine data collection. This study investigated the potential for IMF predictor traits to substitute for measured IMF in genetic improvement programmes. Carcass and predicted IMF (near-infrared estimated IMF and marbling score) data were available on 10,113 New Zealand lambs, 1678 of which also had measured chemical IMF on a slice of M. longissimus lumborum on which the predictions of IMF had been made. Genetic antagonisms were observed between carcass lean traits and IMF. The genetic correlation between the predictors and measured IMF approached one, indicating that predictors of IMF can be used in genetic improvement programmes. Through using selection indexes, simultaneous increases in IMF and the existing terminal selection index are possible, provided all traits are measured. This study highlights the importance and potential of predicted IMF to achieve genetic improvement in traits of importance to consumers.
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Affiliation(s)
- P L Johnson
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand.
| | - J C McEwan
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand
| | - S M Hickey
- AgResearch Ruakura Research Centre, 10 Bisley Road, Hamilton 3214, New Zealand
| | - K G Dodds
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand
| | - S Hitchman
- AgResearch Grasslands, Palmerston North, 4410, New Zealand
| | - M P Agnew
- AgResearch Grasslands, Palmerston North, 4410, New Zealand
| | - W E Bain
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand
| | - S-A N Newman
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand
| | | | - C R Craigie
- AgResearch Lincoln, Springs Road, New Zealand
| | - S M Clarke
- AgResearch, Invermay Agricultural Centre, Private Bag 50-034, Mosgiel 9053, New Zealand
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4
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Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci 2023; 202:109204. [PMID: 37146500 DOI: 10.1016/j.meatsci.2023.109204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/22/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
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Affiliation(s)
- Jiewen Zuo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wenlong Zou
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yahui Chen
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Daoyu Huo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Kuanglin Chao
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, United States
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5
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Zhang R, Pavan E, Ross AB, Deb-Choudhury S, Dixit Y, Mungure TE, Realini CE, Cao M, Farouk MM. Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics. J Proteomics 2023; 276:104836. [PMID: 36764652 DOI: 10.1016/j.jprot.2023.104836] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/11/2023]
Abstract
Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.
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Affiliation(s)
- Renyu Zhang
- Food Technology & Processing, AgResearch Ltd, Palmerston North, New Zealand.
| | - Enrique Pavan
- Food Technology & Processing, AgResearch Ltd, Palmerston North, New Zealand; Unidad Integrada Balcarce (FCA, UNMdP - INTA, EEA Balcarce), Ruta 226 km 73.5, CP7620 Balcarce, Argentina
| | - Alastair B Ross
- Proteins and Metabolites, AgResearch Ltd, Lincoln, New Zealand
| | | | - Yash Dixit
- Food informatics, AgResearch Ltd, Palmerston North, New Zealand
| | | | - Carolina E Realini
- Food Technology & Processing, AgResearch Ltd, Palmerston North, New Zealand
| | - Mingshu Cao
- Data Science, AgResearch Ltd, Palmerston North, New Zealand
| | - Mustafa M Farouk
- Food Technology & Processing, AgResearch Ltd, Palmerston North, New Zealand
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6
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Robert C, Bain WE, Craigie C, Hicks TM, Loeffen M, Fraser-Miller SJ, Gordon KC. Fusion of three spectroscopic techniques for prediction of fatty acid in processed lamb. Meat Sci 2023; 195:109005. [DOI: 10.1016/j.meatsci.2022.109005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022]
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7
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Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Wan G, Fan S, Liu G, He J, Wang W, Li Y, lijuan Cheng, Ma C, Guo M. Fusion of spectra and texture data of hyperspectral imaging for prediction of myoglobin content in nitrite-cured mutton. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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9
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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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10
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Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Tian XY, Aheto JH, Huang X, Zheng K, Dai C, Wang C, Bai JW. An evaluation of biochemical, structural and volatile changes of dry-cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:5972-5983. [PMID: 33856705 DOI: 10.1002/jsfa.11251] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/04/2021] [Accepted: 04/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2-thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography-ion mobility spectrometry (GC-IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm. RESULTS Prediction results for MC and TBARS using multiplicative scatter correction pre-processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC-IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity. CONCLUSION The synergistic application of HSI, CLSM and GC-IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Kaiyi Zheng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, PR China
| | - Chengquan Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China
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12
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Short communication: Long term performance of near infrared spectroscopy to predict intramuscular fat content in New Zealand lamb. Meat Sci 2021; 181:108376. [DOI: 10.1016/j.meatsci.2020.108376] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 01/27/2023]
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13
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Thampi A, Hitchman S, Coen S, Vanholsbeeck F. Towards real time assessment of intramuscular fat content in meat using optical fiber-based optical coherence tomography. Meat Sci 2021; 181:108411. [DOI: 10.1016/j.meatsci.2020.108411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 12/31/2022]
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14
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Dixit Y, Hitchman S, Hicks T, Lim P, Wong C, Holibar L, Gordon K, Loeffen M, Farouk M, Craigie C, Reis M. Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant. Meat Sci 2021; 181:108410. [DOI: 10.1016/j.meatsci.2020.108410] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 11/23/2020] [Accepted: 12/10/2020] [Indexed: 11/28/2022]
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15
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Dixit Y, Al-Sarayreh M, Craigie C, Reis M. A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging. Meat Sci 2021; 181:108405. [DOI: 10.1016/j.meatsci.2020.108405] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 11/26/2020] [Accepted: 12/07/2020] [Indexed: 01/06/2023]
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16
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Wang Y, Wang C, Dong F, Wang S. Integrated spectral and textural features of hyperspectral imaging for prediction and visualization of stearic acid content in lamb meat. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:4157-4168. [PMID: 34554149 DOI: 10.1039/d1ay00757b] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Stearic acid content is an important factor affecting mutton odor. To determine the distribution and content of stearic acid (C18:0) in lamb meat fast and nondestructively, a method integrating spectral and textural data of hyperspectral imaging (900-1700 nm) was proposed in this paper. Firstly, spectral information was obtained and preprocessed. Then, the spectral features were extracted by variable combination population analysis-genetic algorithm (VCPA-GA) and interval variable iterative space shrinking analysis (IVISSA). Subsequently, the prediction models of partial least squares regression (PLSR) and least-squares support vector machines (LSSVMs) were established and compared. The model constructed with SNVD-VCPA-GA-PLSR achieved better performance. To improve the prediction results of the models, the textural features were extracted using a gray-level co-occurrence matrix (GLCM) and fused with spectral features. The optimized model achieved good results, with Rc of 0.8716, RMSEC of 0.0793 g/100 g, RPDc of 2.398, and Rp of 0.8121 with RMSEP of 0.1481 g/100 g and RPDp of 1.756. Finally, the spatial distribution of the C18:0 content in lamb meat was visualized using an optimal model. The result indicated that it was feasible to predict and visualize the C18:0 content in lamb meat, providing a way for real-time detection of volatile fatty acid compounds in meat.
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Affiliation(s)
- Yan Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Caixia Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Fujia Dong
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
| | - Songlei Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, PR China.
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17
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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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18
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Hyperspectral imaging and chemometrics as a non-invasive tool to discriminate and analyze iodine value of pork fat. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108145] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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19
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Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years. Meat Sci 2021; 179:108492. [DOI: 10.1016/j.meatsci.2021.108492] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/23/2022]
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20
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Robert C, Jessep W, Sutton JJ, Hicks TM, Loeffen M, Farouk M, Ward JF, Bain WE, Craigie CR, Fraser-Miller SJ, Gordon KC. Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat. Food Chem 2021; 361:130154. [PMID: 34077882 DOI: 10.1016/j.foodchem.2021.130154] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 10/21/2022]
Abstract
The implementation of Raman and infrared spectroscopy with three data fusion strategies to predict pH and % IMF content of red meat was investigated. Raman and FTIR systems were utilized to assess quality parameters of intact red meat. Quantitative models were built using PLS, with model performances assessed with respect to the determination coefficient (R2), root mean square error and normalized root mean square error (NRMSEP). Results obtained on validation against an independent test set show that the high-level fusion strategy had the best performance in predicting the observed pH; with RP2 and NRMSEP values of 0.73 and 12.9% respectively, whereas low-level fusion strategy showed promise in predicting % IMF (NRMSEP = 8.5%). The fusion of data from more than one technique at low and high level resulted in improvement in the model performances; highlighting the possibility of information enhancement.
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Affiliation(s)
- Chima Robert
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - William Jessep
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Joshua J Sutton
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand
| | - Talia M Hicks
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North 4410, New Zealand
| | - Mark Loeffen
- Delytics Ltd., Waikato Innovation Centre, Hamilton East, Hamilton 3216, New Zealand
| | - Mustafa Farouk
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton 3240, New Zealand
| | - James F Ward
- AgResearch, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand
| | - Wendy E Bain
- AgResearch, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand
| | - Cameron R Craigie
- AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | - Sara J Fraser-Miller
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
| | - Keith C Gordon
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
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21
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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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22
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Tian XY, Aheto JH, Dai C, Ren Y, Bai JW. Monitoring microstructural changes and moisture distribution of dry-cured pork: a combined confocal laser scanning microscopy and hyperspectral imaging study. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:2727-2735. [PMID: 33124042 DOI: 10.1002/jsfa.10899] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/13/2020] [Accepted: 10/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Various spectral profiles, including reflectance, absorbance, and Kubelka-Munk spectra, have been derived from hyperspectral images and used to develop multivariate models to evaluate changes in the quality of meat and meat products as a function of processing. However, none of these has the capacity to produce images of the structural changes often associated with processing. This study explored the feasibility of combining hyperspectral imaging (HSI) with confocal laser scanning microscopy (CLSM) to examine the impact of processing on microstructural changes and the evolution of moisture. Reflectance spectra features were obtained and transformed into absorbance and Kubelka-Munk spectra and their ability to predict moisture content using models established on partial least-squares regression were evaluated. RESULTS The partial least-squares regression model (full-band wavelength) dubbed Rs-MSC yielded the best result, with R c 2 = 0.967 , RMSEC = 0.127, R cv 2 = 0.949 , RMSECV = 0.418, R p 2 = 0.937 , RMSEP = 0.824. Next, a total of 16 optimum wavelengths were selected using the competitive adaptive reweighted sampling algorithm. These wavelengths also yielded good results for Rs-MSC, with R c 2 = 0.958 , RMSEC = 0.840, R cv 2 = 0.931 , RMSECV = 0.118, R p 2 = 0.926 , RMSEP = 0.121. Regarding moisture distribution and microstructure analysis, HSI and CLSM were able to reveal moisture content distribution and conformational differences in microstructure in the test samples. CONCLUSION Using HSI in synergy with CLSM may offer a reliable means for assessing both the chemical and structural changes that occur in other congener food products during processing. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Xiao-Yu Tian
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Joshua H Aheto
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Chunxia Dai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, P. R. China
| | - Yi Ren
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
- School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, Suzhou, P. R. China
| | - Jun-Wen Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, P. R. China
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23
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Caballero D, Pérez-Palacios T, Caro A, Antequera T. Use of Magnetic Resonance Imaging to Analyse Meat and Meat Products Non-destructively. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1912085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Daniel Caballero
- Chemometrics and Analytical Technology, Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark
- Media Engineering Group (GIM), Department of Computer Science, Research Institute of Meat and Meat Product (IproCar), University of Extrema, Cáceres, Spain
| | - Trinidad Pérez-Palacios
- Department of Food Technology, Research Institute of Meat and Meat Products (Iprocar) University of Extremadura, Cáceres, Spain
| | - Andrés Caro
- Media Engineering Group (GIM), Department of Computer Science, Research Institute of Meat and Meat Product (IproCar), University of Extrema, Cáceres, Spain
| | - Teresa Antequera
- Department of Food Technology, Research Institute of Meat and Meat Products (Iprocar) University of Extremadura, Cáceres, Spain
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24
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Kucha CT, Liu L, Ngadi M, Gariépy C. Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics. Meat Sci 2021; 176:108458. [PMID: 33647629 DOI: 10.1016/j.meatsci.2021.108458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 10/22/2022]
Abstract
The fibrous structure of meat muscle makes it an anisotropic optical material. As such, spectral information varies with the orientation of the muscle. In this study, spectral data from pork cuts were obtained by a transverse scan (TRANSCAN), radial scan (RADISCAN), and longitudinal scan (LONGSCAN) by using hyperspectral imaging. The information was used to develop and compare the prediction models for intramuscular (IMF) content prediction by partial least square regression (PLSR), support vector machines regression (SVMR), and backpropagation artificial neural network (BPANN). The three modeling algorithms showed equal capability for modeling IMF in pork. The accuracy of the prediction models from the three scans was in the order of TRANSCAN ≥ RADISCAN ≥ LONGSCAN. Successive projection algorithm reduced the wavelengths to 93%. The reduced wavelengths were used to build new models that showed similar accuracy to the models of the original wavelengths. This study shows that muscle orientation influences the accuracy of the prediction models.
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Affiliation(s)
- Christopher T Kucha
- Department of Bioresource Engineering, McGill University, Macdonald Campus, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Li Liu
- Department of Bioresource Engineering, McGill University, Macdonald Campus, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Michael Ngadi
- Department of Bioresource Engineering, McGill University, Macdonald Campus, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada.
| | - Claude Gariépy
- Agriculture and Agri-Food Canada, 3600 Cassavant West, Saint-Hyacinthe, QC J2S 8E3, Canada
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25
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Antequera T, Caballero D, Grassi S, Uttaro B, Perez-Palacios T. Evaluation of fresh meat quality by Hyperspectral Imaging (HSI), Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI): A review. Meat Sci 2021; 172:108340. [DOI: 10.1016/j.meatsci.2020.108340] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/23/2020] [Accepted: 10/09/2020] [Indexed: 12/31/2022]
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26
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Realini CE, Pavan E, Johnson PL, Font-I-Furnols M, Jacob N, Agnew M, Craigie CR, Moon CD. Consumer liking of M. longissimus lumborum from New Zealand pasture-finished lamb is influenced by intramuscular fat. Meat Sci 2020; 173:108380. [PMID: 33288363 DOI: 10.1016/j.meatsci.2020.108380] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/01/2020] [Accepted: 11/20/2020] [Indexed: 01/19/2023]
Abstract
Palatability of meat is known to be affected by intramuscular fat (IMF), but the effect in relatively low-fat New Zealand lamb is unknown. This study evaluated the eating quality of 108 loins (M. longissimus lumborum) from a single flock of ewe-lambs. Loins ranged from 1.09-5.68% IMF and were stratified into 6 groups: 1.65, 2.12, 2.65, 3.20, 3.58 and 4.40%. Consumers' (n = 165) overall liking of lamb increased significantly at around 3% IMF, achieving maximum scores at 4% IMF. One consumer cluster (n = 111) showed a linear increase in overall liking with increasing IMF%, regarded as 'IMF lovers: the more the better', while a second cluster (n = 54) preferred 2.5-3.5% IMF, described as 'IMF optimizers: just the right amount'. IMF% was modestly correlated (~ + 0.25) with all sensory attributes except juiciness. Liking scores were modestly correlated with monounsaturated (~ + 0.25) and polyunsaturated (~ - 0.20) fatty acids. Results suggest aiming for IMF% levels in New Zealand lamb beyond 3% to maximize eating quality for premium markets in particular.
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Affiliation(s)
- C E Realini
- AgResearch Grasslands, Palmerston North 4410, New Zealand.
| | - E Pavan
- AgResearch Grasslands, Palmerston North 4410, New Zealand
| | - P L Johnson
- AgResearch Invermay, Puddle Alley, Mosgiel, New Zealand
| | - M Font-I-Furnols
- Institut de Recerca i Tecnologia Agroalimentaries (IRTA), Finca Camps i Armet, 17121 Monells, Spain
| | - N Jacob
- AgResearch Grasslands, Palmerston North 4410, New Zealand
| | - M Agnew
- AgResearch Grasslands, Palmerston North 4410, New Zealand
| | - C R Craigie
- AgResearch Grasslands, Palmerston North 4410, New Zealand
| | - C D Moon
- AgResearch Grasslands, Palmerston North 4410, New Zealand
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27
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Robert C, Fraser-Miller SJ, Jessep WT, Bain WE, Hicks TM, Ward JF, Craigie CR, Loeffen M, Gordon KC. Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy. Food Chem 2020; 343:128441. [PMID: 33127228 DOI: 10.1016/j.foodchem.2020.128441] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 12/24/2022]
Abstract
With increasing demand for fast and reliable techniques for intact meat discrimination, we explore the potential of Raman spectroscopy in combination with three chemometric techniques to discriminate beef, lamb and venison meat samples. Ninety (90) intact red meat samples were measured using Raman spectroscopy, with the acquired spectral data preprocessed using a combination of rubber-band baseline correction, Savitzky-Golay smoothing and standard normal variate transformation. PLSDA and SVM classification were utilized in building classification models for the meat discrimination, whereas PCA was used for exploratory studies. Results obtained using linear and non-linear kernel SVM models yielded sensitivities of over 87 and 90 % respectively, with the corresponding specificities above 88 % on validation against a test set. The PLSDA model yielded over 80 % accuracy in classifying each of the meat specie. PLSDA and SVM classification models in combination with Raman spectroscopy posit an effective technique for red meat discrimination.
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Affiliation(s)
- Chima Robert
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9016, New Zealand.
| | - Sara J Fraser-Miller
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9016, New Zealand
| | - William T Jessep
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9016, New Zealand
| | - Wendy E Bain
- AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | - Talia M Hicks
- Delytics Ltd, Waikato Innovation Park, Hamilton 3216, New Zealand
| | - James F Ward
- AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | - Cameron R Craigie
- AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch 8140, New Zealand
| | - Mark Loeffen
- Delytics Ltd, Waikato Innovation Park, Hamilton 3216, New Zealand
| | - Keith C Gordon
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Chemistry, University of Otago, PO Box 56, Dunedin 9016, New Zealand.
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28
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Lambe NR, Clelland N, Draper J, Smith EM, Yates J, Bunger L. Prediction of intramuscular fat in lamb by visible and near-infrared spectroscopy in an abattoir environment. Meat Sci 2020; 171:108286. [PMID: 32871540 DOI: 10.1016/j.meatsci.2020.108286] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 08/19/2020] [Accepted: 08/19/2020] [Indexed: 11/29/2022]
Abstract
The study used visible and near-infrared spectroscopy (Vis-NIR) in a large commercial processing plant, to test a system for meat quality (intramuscular fat; IMF) data collection within a supply chain for UK lamb meat. Crossbred Texel x Scotch Mule lambs (n = 220), finished on grass on 4 farms and slaughtered across 2 months, were processed through the abattoir and cutting plant and recorded using electronic identification. Vis-NIR scanning of the cut surface of the M. longissimus lumborum produced spectral data that predicted laboratory-measured IMF% with moderate accuracy (R2 0.38-0.48). Validation of the Vis-NIR prediction equations on an independent sample of 30 lambs slaughtered later in the season, provided similar accuracy of IMF prediction (R2 0.54). Values of IMF from four different laboratory tests were highly correlated with each other (r 0.82-0.95) and with Vis-NIR predicted IMF (r 0.66-0.75). Results suggest scope to collect lamb loin IMF data from a commercial UK abattoir, to sort cuts for different customers or to feed back to breeding programmes to improve meat quality.
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Affiliation(s)
- N R Lambe
- SRUC Hill and Mountain Research Centre, Kirkton farm, Crianlarich, West Perthshire, Scotland FK20 8RU, UK.
| | - N Clelland
- SRUC, JF Niven Building, Auchincruive, by Ayr, KA6 5HW, UK
| | - J Draper
- ABP, Birmingham Business Park, Birmingham B37 7YB, UK
| | - E M Smith
- The Texel Sheep Society, Stoneleigh Park, Kenilworth, Warwickshire CV8 2LG, UK
| | - J Yates
- The Texel Sheep Society, Stoneleigh Park, Kenilworth, Warwickshire CV8 2LG, UK
| | - L Bunger
- Animal Genetics Consultancy, Edinburgh, Scotland, UK
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29
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Kucha CT, Liu L, Ngadi M, Gariépy C. Assessment of Intramuscular Fat Quality in Pork Using Hyperspectral Imaging. FOOD ENGINEERING REVIEWS 2020. [DOI: 10.1007/s12393-020-09246-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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30
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Silva S, Guedes C, Rodrigues S, Teixeira A. Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review. Foods 2020; 9:E1074. [PMID: 32784641 PMCID: PMC7466308 DOI: 10.3390/foods9081074] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 07/25/2020] [Accepted: 07/27/2020] [Indexed: 02/06/2023] Open
Abstract
In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.
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Affiliation(s)
- Severiano Silva
- Veterinary and Animal Research Centre (CECAV) Universidade Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal;
| | - Cristina Guedes
- Veterinary and Animal Research Centre (CECAV) Universidade Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal;
| | - Sandra Rodrigues
- Mountain Research Centre (CIMO), Escola Superior Agrária/Instituto Politécnico de Bragança, Campus Sta Apolónia Apt 1172, 5301-855 Bragança, Portugal; (S.R.); (A.T.)
| | - Alfredo Teixeira
- Mountain Research Centre (CIMO), Escola Superior Agrária/Instituto Politécnico de Bragança, Campus Sta Apolónia Apt 1172, 5301-855 Bragança, Portugal; (S.R.); (A.T.)
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31
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Wang C, Wang S, He X, Wu L, Li Y, Guo J. Combination of spectra and texture data of hyperspectral imaging for prediction and visualization of palmitic acid and oleic acid contents in lamb meat. Meat Sci 2020; 169:108194. [PMID: 32521405 DOI: 10.1016/j.meatsci.2020.108194] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 05/14/2020] [Accepted: 05/14/2020] [Indexed: 01/13/2023]
Abstract
The feasibility of combining spectral and textural information from hyperspectral imaging to improve the prediction of the C16:0 and C18:1 n9 contents for lamb was explored. 29 and 22 optimal wavelengths were selected for the C16:0 and C18:1 n9 contents, respectively, by conducting the variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) algorithm. To extract the textural features of images, a gray-level co-occurrence matrix (GLCM) analysis was implemented in the first principal component image. The least squares support vector machine (LSSVM) model and the partial least squares regression (PLSR) model were developed to predict the C16:0 and C18:1 n9 contents from the spectra and the fusion data. The distribution map was visualized using the best model with the imaging process. The results showed that the combination of the spectral and textural information of hyperspectral imaging coupled with the VCPA-IRIV algorithm had strong potential for the prediction and visualization of the C16:0 and C18:1 n9 contents of lamb.
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Affiliation(s)
- Caixia Wang
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China
| | - Songlei Wang
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China.
| | - Xiaoguang He
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China
| | - Longguo Wu
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China
| | - Yalei Li
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China
| | - Jianhong Guo
- School of Agriculture, Ningxia University, Yinchuan 750021,PR China
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32
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Teng F, Reis MG, Yang L, Ma Y, Day L. Structural characteristics of triacylglycerols contribute to the distinct in vitro gastric digestibility of sheep and cow milk fat prior to and after homogenisation. Food Res Int 2020; 130:108911. [DOI: 10.1016/j.foodres.2019.108911] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 12/07/2019] [Accepted: 12/15/2019] [Indexed: 12/25/2022]
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33
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Ye Y, Schreurs N, Johnson P, Corner-Thomas R, Agnew M, Silcock P, Eyres G, Maclennan G, Realini C. Carcass characteristics and meat quality of commercial lambs reared in different forage systems. Livest Sci 2020. [DOI: 10.1016/j.livsci.2019.103908] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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34
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Dixit Y, Pham HQ, Realini CE, Agnew MP, Craigie CR, Reis MM. Evaluating the performance of a miniaturized NIR spectrophotometer for predicting intramuscular fat in lamb: A comparison with benchtop and hand-held Vis-NIR spectrophotometers. Meat Sci 2019; 162:108026. [PMID: 31816518 DOI: 10.1016/j.meatsci.2019.108026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 11/15/2022]
Abstract
This study compares a miniaturized spectrophotometer to benchtop and hand-held Vis-NIR instruments in the spectral range of 900-1700 nm for prediction of intramuscular fat (IMF) content of freeze-dried ground lamb meat; and their ability to differentiate fresh lamb meat based on animal age (4 vs 12 months). The performance of the miniaturized spectrophotometer was not affected by sample temperature equilibration time. Partial Least Square regression models for IMF showed Rcv2 = 0.86-0.89 and RMSECV = 0.36-0.40 values for all instruments. Day-to-day instrumental variation adversely affected performance of the miniaturized spectrophotometer (R2p = 0.27, RMSEP = 1.28). This negative effect was overcome by representing day-to-day variation in the model. The benchtop spectrophotometer and miniaturized spectrophotometer differentiated lamb meat by animal age. The miniaturized spectrophotometer has potential to be a fast, ultra-compact and cost-effective device for predicting IMF in freeze-dried ground lamb meat and for age classification of fresh lamb meat.
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Affiliation(s)
- Y Dixit
- Agresearch Grasslands, Palmerston North, 4410, New Zealand
| | - H Q Pham
- Agresearch Grasslands, Palmerston North, 4410, New Zealand; Massey University, Palmerston North, New Zealand
| | - C E Realini
- Agresearch Grasslands, Palmerston North, 4410, New Zealand
| | - M P Agnew
- Agresearch Grasslands, Palmerston North, 4410, New Zealand
| | - C R Craigie
- Agresearch Lincoln, Lincoln, 7674, New Zealand
| | - M M Reis
- Agresearch Grasslands, Palmerston North, 4410, New Zealand.
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35
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Aheto JH, Huang X, Tian X, Ren Y, Bonah E, Alenyorege EA, Lv R, Dai C. Combination of spectra and image information of hyperspectral imaging data for fast prediction of lipid oxidation attributes in pork meat. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13225] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Joshua H. Aheto
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xingyi Huang
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Xiaoyu Tian
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
| | - Yi Ren
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Suzhou Polytechnic Institute of Agriculture; Suzhou China
| | - Ernest Bonah
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Laboratory Services Department; Food and Drugs Authority; Accra Ghana
| | - Evans A. Alenyorege
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- Faculty of Agriculture; University for Development Studies; Tamale Ghana
| | - Riqin Lv
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Biological Science and Food Engineering; Chuzhou University; No. 1528 Fengle Avenue, Yu District, Zhangzhou City China
| | - Chunxia Dai
- School of Food and Biological Engineering; Jiangsu University; Zhenjiang Jiangsu China
- School of Electrical and Information Engineering; Jiangsu University; Zhenjiang Jiangsu China
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In-depth lipidomic analysis of tri-, di-, and mono-acylglycerols released from milk fat after in vitro digestion. Food Chem 2019; 297:124976. [PMID: 31253293 DOI: 10.1016/j.foodchem.2019.124976] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 05/27/2019] [Accepted: 06/10/2019] [Indexed: 12/27/2022]
Abstract
Milk fat is arguably one of the most complex fats found in nature and varies widely between animal species. Analysis of its digestion products is tremendously challenging, due to the complexity, diversity, and large range of concentrations of triacylglycerols (TAGs) and their digestion products (i.e. diacylglycerols (DAGs), monoacylglycerols (MAGs), and free fatty acids (FFAs)). Therefore, a method combined the solid phase extraction (SPE), high-performance liquid chromatography (HPLC) and multi-dimension mass spectrometry (MDMS) was developed to identify and semi-quantify the TAGs, DAGs and MAGs in milk fat after in vitro digestion. Up to 105, 64, 14 and 30 species of TAGs, DAGs, MAGs, and FFAs were determined with their concentrations of 0.01-22.3, 0.01-39.2, 0.01-47.8, and 0.04-191.0 mg/g fat, respectively, during the in vitro digestion of cow and sheep milk. The validation of the method shows that this method was precise and reliable.
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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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Shorten PR, Leath SR, Schmidt J, Ghamkhar K. Predicting the quality of ryegrass using hyperspectral imaging. PLANT METHODS 2019; 15:63. [PMID: 31182971 PMCID: PMC6554905 DOI: 10.1186/s13007-019-0448-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 05/28/2019] [Indexed: 05/08/2023]
Abstract
BACKGROUND The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550-1700 nm wavelengths were obtained from 185 accessions of ryegrass (Lolium perenne), which were also analysed for 13 forage quality attributes. RESULTS Medium to high predictive power was observed for the HSI models of total sugars (R2 validation of 0.58), high molecular weight sugars (R2 validation of 0.63), %Ash (R2 validation of 0.50) and %Nitrogen (R2 validation of 0.70). Significant HSI models with low R2 validation of 0.1-0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. CONCLUSION This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.
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Affiliation(s)
- Paul R. Shorten
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240 New Zealand
| | - Shane R. Leath
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240 New Zealand
| | - Jana Schmidt
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Kioumars Ghamkhar
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442 New Zealand
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Abstract
The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.
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40
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Reis MM, Van Beers R, Al-Sarayreh M, Shorten P, Yan WQ, Saeys W, Klette R, Craigie C. Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat. Meat Sci 2018; 144:100-109. [PMID: 29960721 DOI: 10.1016/j.meatsci.2018.05.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/28/2018] [Accepted: 05/28/2018] [Indexed: 01/13/2023]
Abstract
Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat structure. The use of HSI in the meat and food industry has observed a significant growth in the last decade, yet its use for assessment of meat it is not optimal yet. The application of HSI for assessment of meat is reviewed with focus on its ability to capture meat unique chemical and structural characteristics. While HSI is widely used for assessment of chemical composition, a limited number of evidences on its ability to handle the effect of different sources of variation on the assessment is found. The use of spatially resolved spectroscopy has been able to detect structural information related to animal background, muscle type, rigor process and ageing. Similarly the use of texture features seem to capture unique characteristics of meat.
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Affiliation(s)
| | - Robbe Van Beers
- KU Leuven, Department of Biosystems, MeBioS, Leuven, Belgium
| | | | | | - Wei Qi Yan
- Auckland University of Technology, Auckland, New Zealand
| | - Wouter Saeys
- KU Leuven, Department of Biosystems, MeBioS, Leuven, Belgium
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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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Bermingham EN, Reis MG, Subbaraj AK, Cameron-Smith D, Fraser K, Jonker A, Craigie CR. Distribution of fatty acids and phospholipids in different table cuts and co-products from New Zealand pasture-fed Wagyu-dairy cross beef cattle. Meat Sci 2018; 140:26-37. [PMID: 29501930 DOI: 10.1016/j.meatsci.2018.02.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/18/2018] [Accepted: 02/19/2018] [Indexed: 01/21/2023]
Abstract
Wagyu beef products are marketed as luxury goods to discerning consumers and the lipid content and composition are important drivers of wagyu product value. Wagyu beef is an extensively marbled meat product, well characterised for its tenderness and flavour. In New Zealand, pasture-fed Wagyu-dairy beef production is increasing to meet demand for ultra-premium meat products. Important for these characteristics is the composition of lipid species and their distribution across the carcass. The aim of this study was to analyse the distribution of fatty acids and phospholipids in 26 table cuts, nine co-products and three fat deposits of carcasses from New Zealand pasture-fed Wagyu-dairy cross beef carcasses (n = 5). Phospholipid and fatty acid levels varied across different cuts of the carcass, but typically cuts with high levels of phospholipids also had high levels of omega-3 fatty acids and low levels of saturated fatty acids. This work will be used in the future to examine the potential health aspects of pasture-fed Wagyu beef.
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Affiliation(s)
- Emma N Bermingham
- Food Nutrition & Health Team, AgResearch, Palmerston North, New Zealand.
| | | | - Arvind K Subbaraj
- Food Nutrition & Health Team, AgResearch, Palmerston North, New Zealand
| | - David Cameron-Smith
- Food Nutrition & Health Team, AgResearch, Palmerston North, New Zealand; Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Karl Fraser
- Food Nutrition & Health Team, AgResearch, Palmerston North, New Zealand
| | - Arjan Jonker
- Animal Nutrition & Physiology Team, AgResearch, Palmerston North, New Zealand
| | - Cameron R Craigie
- Food Assurance & Meat Quality Team, AgResearch, Hamilton, New Zealand
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Feng CH, Makino Y, Yoshimura M, Thuyet DQ, García-Martín JF. Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions. J Food Sci 2017; 83:358-366. [PMID: 29278665 DOI: 10.1111/1750-3841.14024] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 11/18/2017] [Accepted: 12/01/2017] [Indexed: 01/06/2023]
Abstract
The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination (Rp2 ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions. PRACTICAL APPLICATION In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high Rp2 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems.
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Affiliation(s)
- Chao-Hui Feng
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan.,Coll. of Food Science, Sichuan Agricultural Univ., Yucheng District, Ya'an, Sichuan, China.,Coll. of Pharmacy and Biological Engineering, Chengdu Univ., Chengdu, Sichuan 610106, China
| | - Yoshio Makino
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Masatoshi Yoshimura
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Dang Quoc Thuyet
- Graduate School of Agricultural and Life Sciences, The Univ. of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
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