{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:47:00Z","timestamp":1778050020784,"version":"3.51.4"},"reference-count":127,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/foods9081074","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T07:25:03Z","timestamp":1597044303000},"page":"1074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review"],"prefix":"10.3390","volume":"9","author":[{"given":"Severiano","family":"Silva","sequence":"first","affiliation":[{"name":"Veterinary and Animal Research Centre (CECAV) Universidade Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal"}]},{"given":"Cristina","family":"Guedes","sequence":"additional","affiliation":[{"name":"Veterinary and Animal Research Centre (CECAV) Universidade Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3301-1729","authenticated-orcid":false,"given":"Sandra","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Mountain Research Centre (CIMO), Escola Superior Agr\u00e1ria\/Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus Sta Apol\u00f3nia Apt 1172, 5301-855 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4607-4796","authenticated-orcid":false,"given":"Alfredo","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Mountain Research Centre (CIMO), Escola Superior Agr\u00e1ria\/Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus Sta Apol\u00f3nia Apt 1172, 5301-855 Bragan\u00e7a, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Australian view on lamb carcass and meat quality \u2013 the role of measurement technologies in the Australian sheep industry. Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1071\/AN17773","article-title":"Maintaining the appeal of Australian lamb to the modern consumer","volume":"58","author":"Fowler","year":"2018","journal-title":"Anim. Prod. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Aboah, J., and Lees, N. (2020). Consumers use of quality cues for meat purchase: Research trends and future pathways. Meat Sci., 108142.","DOI":"10.1016\/j.meatsci.2020.108142"},{"key":"ref_4","unstructured":"Bazer, F.W., Lamb, G.C., and Wu, G. (2020). Introduction: Significance, challenges and strategies of animal production. Animal Agriculture, Academic Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.meatsci.2014.06.023","article-title":"Trends in meat science and technology: The future looks bright, but the journey will be long","volume":"98","author":"Kristensen","year":"2014","journal-title":"Meat Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/bs.afnr.2018.09.002","article-title":"2019. Advances and sheep and goat meat products research","volume":"Volume 87","author":"Toldra","year":"2019","journal-title":"Advances in Food and Nutrition Research"},{"key":"ref_7","unstructured":"Biswas, A.K., and Mandal, P.K. (2020). Nondestructive methods for carcass and meat quality evaluation. Meat Quality Analysis, Academic Press."},{"key":"ref_8","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Generic software modules for the meat industry. FAIM Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_9","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Artifact removal in differential phase contrast x-ray computed tomography. FAIM Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108011","DOI":"10.1016\/j.meatsci.2019.108011","article-title":"Comparison of the decision tree, artificial neural network and multiple regression methods for prediction of carcass tissues composition of goat kids","volume":"161","author":"Ekiz","year":"2020","journal-title":"Meat Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1111\/ijfs.14367","article-title":"Shining light into meat\u2013a review on the recent advances in in vivo and carcass applications of near infrared spectroscopy","volume":"55","author":"Chapman","year":"2020","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.jsams.2018.03.005","article-title":"Dual energy X-ray absorptiometry positioning protocols in assessing body composition: A systematic review of the literature","volume":"21","author":"Shiel","year":"2018","journal-title":"J. Sci. Med. Sport"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marra, M., Sammarco, R., De Lorenzo, A., Iellamo, F., Siervo, M., Pietrobelli, A., Donini, L.M., Santarpia, L., Cataldi, M., and Pasanisi, F. (2019). Assessment of body composition in health and disease using bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA): A critical overview. Contrast Media Mol. Imaging, 3548284.","DOI":"10.1155\/2019\/3548284"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1017\/S1751731115000336","article-title":"Non-invasive methods for the determination of body and carcass composition in livestock: Dual energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: Invited review","volume":"9","author":"Scholz","year":"2015","journal-title":"Animal"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1590\/s1806-92902017000700010","article-title":"Use of dual-energy x-ray absorptiometry in non-ruminant nutrition research","volume":"46","author":"Pomar","year":"2017","journal-title":"Rev. Bras. Zootec."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.meatsci.2018.06.020","article-title":"Calibration of an on-line dual energy X-ray absorptiometer for estimating carcase composition in lamb at abattoir chain-speed","volume":"144","author":"Gardner","year":"2018","journal-title":"Meat Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.smallrumres.2011.07.003","article-title":"Dual energy X-ray absorptiometry (DXA) can be used to predict live animal and whole carcass composition of sheep","volume":"100","author":"Hunter","year":"2011","journal-title":"Small Rum. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Connaughton, S.L., Williams, A., Anderson, F., Kelman, K.R., and Gardner, G.E. (2020). Dual energy X-ray absorptiometry precisely and accurately predicts lamb carcass composition at abattoir chain speed across a range of phenotypic and genotypic variables. Animal.","DOI":"10.1016\/j.meatsci.2020.108413"},{"key":"ref_19","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2013). Body composition in farm animals by dual energy X-ray absorptiometry. Farm Animal Imaging, SRUC Scotland."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.meatsci.2018.08.027","article-title":"Filling the out of season gaps for lamb and hogget production: Diet and genetic influence on carcass yield, carcass composition and retail value of meat","volume":"148","author":"Ponnampalam","year":"2019","journal-title":"Meat Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1071\/EA07039","article-title":"Accuracy of dual energy X-ray absorptiometry (DXA), weight, longissimus lumborum muscle depth and GR fat depth to predict half carcass composition in sheep","volume":"47","author":"Dunshea","year":"2007","journal-title":"Aust. J. Exp. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1139\/cjas-2017-0208","article-title":"Exploration of methods for lam carcass yield estimation in Canada","volume":"98","author":"Roberts","year":"2018","journal-title":"Can. J. Anim. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.22175\/rmc2018.162","article-title":"Predictions of Lean Meat Yield in Lambs Using Dexa and Chemical Analyses Proximate","volume":"2","author":"Justice","year":"2018","journal-title":"Meat Muscle Biol."},{"key":"ref_24","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). The development and calibration of a dual X-ray absorptiometer for estimating carcass composition at abattoir chain-speed. Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_25","unstructured":"Ullrey, D.E., Baer, C.K., and Pond, W.G. (2010). Body composition: Indirect measurement. Encyclopedia of Animal Science, CRC Press. [2nd ed.]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.meatsci.2008.08.004","article-title":"Dual X-ray absorptiometry accurately predicts carcass composition from live sheep and chemical composition of live and dead sheep","volume":"81","author":"Pearce","year":"2009","journal-title":"Meat Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.meatsci.2005.11.024","article-title":"The use of dual-energy X-ray absorptiometry to estimate the dissected composition of lamb carcasses","volume":"73","author":"Mercier","year":"2006","journal-title":"Meat Sci."},{"key":"ref_28","first-page":"272","article-title":"Application of dual-energy x-ray absorptiometry for ovine carcass evaluation","volume":"59","author":"Clarke","year":"1999","journal-title":"Proc. N. Z. Soc. Anim. Prod."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.1007\/s00330-019-06559-0","article-title":"Diagnostic accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid calculi: Systematic review and meta-analysis","volume":"30","author":"McGrath","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.cegh.2018.05.006","article-title":"3D scanning applications in medical field: A literature-based review","volume":"7","author":"Haleem","year":"2019","journal-title":"Clin. Epidemiol. Glob. Health"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bekhit, A., and El-Din, A. (2017). CT Scanning and Ultrasound Techniques for In Vivo Assessment in Meat Processing. Advances in Meat Processing Technology, CRC Press. [1st ed.].","DOI":"10.1201\/9781315371955-11"},{"key":"ref_32","unstructured":"Karuppasamy, S. (2011). Use of X-ray computed to.mography (CT) in UK sheep production and breeding. CT Scanning\u2014Techniques and Applications, Intech Open access."},{"key":"ref_33","unstructured":"Daumas, G., Donk\u00f3, T., Maltin, C., and B\u00fcnger, L. (2015). Imaging Facilities (CT & MRI) in EU for Measuring Body Composition, SRUC."},{"key":"ref_34","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Selecting terminal sire breed rams for lean meat percentage\u2013effects on their crossbred lambs. Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_35","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, J. (2014). Integrating Computed tomography into commercial sheep breeding in the UK: Cost and value. Farm Animal Imaging III, SRUC."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lerch, S., De La Torre, A., Huau, C., Monziols, M., Xavier, C., Louis, L., Cozler, Y.L., Faverdin, P., Lambertor, P., and Chery, I. (2020). Estimation of dairy goat body composition: A direct calibration and comparison of eight methods. Methods.","DOI":"10.1016\/j.ymeth.2020.06.014"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hendriks, W.H., Verstegen, M.W.A., and Babinszky, L. (2019). Precision livestock feeding, principle and practice. Poultry and Pig Nutrition. Challenges of the 21st Century, Wageningen Academic.","DOI":"10.3920\/978-90-8686-884-1"},{"key":"ref_38","first-page":"205","article-title":"Prediction of carcass tissue weight in vivo using live weight, ultrasound or X-ray CT measurements","volume":"56","author":"Young","year":"1996","journal-title":"Proc. N. Z. Soc. Anim. Prod."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.smallrumres.2005.09.014","article-title":"In-vivo composition of carcass regions in lambs of two genetic lines, and selection of CT positions for estimation of each region","volume":"66","author":"Kvame","year":"2006","journal-title":"Small Rumin. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1079\/ASC200647","article-title":"Predicting carcass composition of terminal sire sheep using X-ray computed tomography","volume":"82","author":"Macfarlane","year":"2006","journal-title":"Anim. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jfoodeng.2008.01.021","article-title":"Virtual dissection of lamb carcasses using computer tomography (CT) and its correlation to manual dissection","volume":"88","author":"Kongsro","year":"2008","journal-title":"J. Food Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.tvjl.2017.10.012","article-title":"Evaluation of a semi-automated computer algorithm for measuring total fat and visceral fat content in lambs undergoing in vivo whole body computed tomography","volume":"228","author":"Rosenblatt","year":"2017","journal-title":"Vet. J."},{"key":"ref_43","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Using computer tomography to predict composition of light carcass kid goats. Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_44","first-page":"66","article-title":"Prediction of carcass composition of the bravia goat breed by computerized tomography","volume":"4","author":"Silva","year":"2019","journal-title":"Rev. Port. Zootec."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.meatsci.2018.03.003","article-title":"Prediction of intramuscular fat content and shear force in Texel lamb loins using combinations of different X-ray computed tomography (CT) scanning techniques","volume":"140","author":"Clelland","year":"2018","journal-title":"Meat Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1017\/S1357729800053467","article-title":"Body composition changes in Scottish Blackface ewes during one annual production cycle","volume":"76","author":"Lambe","year":"2003","journal-title":"Anim. Sci."},{"key":"ref_47","first-page":"100311","article-title":"Assessment of the value of informatiIon of precision livestock farming: A conceptual framework","volume":"90","author":"Niemi","year":"2019","journal-title":"NJAS Wagen. J. Life Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.compag.2017.06.003","article-title":"A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images","volume":"140","author":"Xiberta","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.meatsci.2018.09.011","article-title":"A CT-image based pig atlas model and its potential applications in the meat industry","volume":"148","author":"Ho","year":"2019","journal-title":"Meat Sci."},{"key":"ref_50","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Genetic control of CT-based spine traits in elite Texel rams. Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1017\/S1357729800053169","article-title":"The use of X-ray computer tomography for measuring the muscularity of live sheep","volume":"75","author":"Jones","year":"2002","journal-title":"Anim. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.meatsci.2006.09.007","article-title":"Accuracy of in vivo muscularity indices measured by computed tomography and their association with carcass quality in lambs","volume":"75","author":"Navajas","year":"2007","journal-title":"Meat Sci."},{"key":"ref_53","unstructured":"Macfarlane, J.M., Young, M.J., Lewis, R.M., Emmans, G.C., and Simm, G. (2005, January 5\u20138). Using X-Ray Computed Tomography to predict intramuscular fat content in terminal sire sheep. Proceedings of the 56th Annual Meeting of the European Association for Animal Production n\u00ba11, Uppsala, Sweden."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.meatsci.2014.06.004","article-title":"Prediction of intramuscular fat levels in Texel lamb loins using X-ray computed tomography scanning","volume":"98","author":"Clelland","year":"2014","journal-title":"Meat Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.meatsci.2016.09.008","article-title":"Prediction of intramuscular fat content using CT scanning of packaged lamb cuts and relationships with meat eating quality","volume":"123","author":"Lambe","year":"2017","journal-title":"Meat Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1017\/S175173111500049X","article-title":"The correlation of intramuscular fat content between muscles of the lamb carcass and the use of computed tomography to predict intramuscular fat percentage in lambs","volume":"9","author":"Anderson","year":"2014","journal-title":"Animal"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MSP.2019.2936964","article-title":"Mathematical models for magnetic resonance imaging reconstruction: An overview of the approaches, problems, and future research areas","volume":"37","author":"Doneva","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Weigand, A.C., Schweizer, H., Knob, D.A., and Scholz, A.M. (2020). Phenotyping of the Visceral Adipose Tissue Using Dual Energy X-Ray Absorptiometry (DXA) and Magnetic Resonance Imaging (MRI) in Pigs. Animals, 10.","DOI":"10.3390\/ani10071165"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.meatsci.2005.06.018","article-title":"Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging","volume":"72","author":"Monziols","year":"2006","journal-title":"Meat Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"181","DOI":"10.5194\/aab-44-181-2001","article-title":"Untersuchungen zur Schlachtk\u00f6rper-und Fleischqualit\u00e4t mit Hilfe von MR-Tomographie und MR-Spektroskopie","volume":"44","author":"Baulain","year":"2001","journal-title":"Arch. Anim. Breed."},{"key":"ref_61","first-page":"33","article-title":"Eignung der Magnetresonanztomographie zur Sch\u00e4tzung der Schlachtleistung von Merino-L\u00e4mmern","volume":"121","author":"Bernau","year":"2016","journal-title":"Nova Acta Leopold."},{"key":"ref_62","first-page":"382","article-title":"Application of Magnetic Resonance Imaging and ultrasound to determine carcass quality in lamb","volume":"77","author":"Korn","year":"2005","journal-title":"Zuchtungskunde"},{"key":"ref_63","first-page":"7","article-title":"Messmethoden zur Beurteilung des Schlachtk\u00f6rperwertes beim Lamm im Vergleich","volume":"47","author":"Mendel","year":"2007","journal-title":"DGfZ Schr."},{"key":"ref_64","first-page":"392","article-title":"Untersuchungen zur K\u00f6rperzusammensetzung wachsender L\u00e4mmer mit Hilfe der Magnet-Resonanz-Tomographie (MRT)","volume":"67","author":"Streitz","year":"1995","journal-title":"Z\u00fcchtungskunde"},{"key":"ref_65","first-page":"12","article-title":"Non-invasive techniques for exact phenotypic assessment of carcass composition and tissue growth in domestic animals","volume":"5","author":"Scholz","year":"2016","journal-title":"Acta Agric. Slov."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1111\/jhn.12372","article-title":"Body composition and functional assessment of nutritional status in adults: A narrative review of imaging, impedance, strength and functional techniques","volume":"29","author":"Smith","year":"2016","journal-title":"J. Hum. Nutr. Diet."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Caballero, D. (2020). Radial textures: A new algorithm to analyze meat quality on MRI. Multimed. Tools Appl.","DOI":"10.1007\/s11042-020-08924-4"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1080\/87559129.2019.1584814","article-title":"Advances in nondestructive methods for meat quality and safety monitoring","volume":"35","author":"Kutsanedzie","year":"2019","journal-title":"Food Rev. Int."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.biosystemseng.2019.04.013","article-title":"Comparison of rapid techniques for classification of ground meat","volume":"183","author":"Rocco","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.meatsci.2004.01.013","article-title":"Development of optimal protocol for visible and near-infrared reflectance spectroscopic evaluation of meat quality","volume":"68","author":"Shackelford","year":"2004","journal-title":"Meat Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1255\/jnirs.924","article-title":"A review of near infrared spectroscopy in muscle food analysis: 2005\u20132010","volume":"19","author":"Weeranantanaphan","year":"2011","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108026","DOI":"10.1016\/j.meatsci.2019.108026","article-title":"Evaluating the performance of a miniaturized NIR spectrophotometer for predicting intramuscular fat in lamb: A comparison with benchtop and hand-held Vis-NIR spectrophotometers","volume":"162","author":"Dixit","year":"2020","journal-title":"Meat Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1017\/S1751731115001172","article-title":"Comparison of visible and near infrared reflectance spectroscopy on fat to authenticate dietary history of lambs","volume":"9","author":"Huang","year":"2015","journal-title":"Animal"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.smallrumres.2015.03.006","article-title":"An approach to predict chemical composition of goat longissimus thoracis et lumborum muscle by near infrared reflectance spectroscopy","volume":"126","author":"Teixeira","year":"2015","journal-title":"Small Rumin. Res."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Fowler, S.M., Morris, S., and Hopkins, D.L. (2020). Preliminary investigation for the prediction of intramuscular fat content of lamb in-situ using a hand-held NIR spectroscopic device. Meat Sci., 108153.","DOI":"10.1016\/j.meatsci.2020.108153"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1016\/j.foodchem.2011.01.084","article-title":"Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS)","volume":"127","author":"Guy","year":"2011","journal-title":"Food Chem."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.smallrumres.2005.12.019","article-title":"Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy","volume":"69","author":"Viljoen","year":"2007","journal-title":"Small Rumin. Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.meatsci.2007.01.011","article-title":"Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy","volume":"76","author":"Murray","year":"2007","journal-title":"Meat Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.meatsci.2019.05.009","article-title":"Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb","volume":"155","author":"Knight","year":"2019","journal-title":"Meat Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.meatsci.2014.10.008","article-title":"On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy","volume":"100","author":"Pullanagari","year":"2015","journal-title":"Meat Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","article-title":"Hypersectral imaging for military and security applications: Combining myriad processing and sensing techniques","volume":"7","author":"Shimoni","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_82","unstructured":"Toldra, F. (2017). Phenotyping of Animals and Their Meat: Applications of Low-Power Ultrasounds, Near-Infrared Spectroscopy, Raman Spectroscopy, and Hyperspectral Imaging. Lawrie\u2019s Meat Science, Elsevier Ltd."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.tifs.2017.08.013","article-title":"Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications","volume":"69","author":"Liu","year":"2017","journal-title":"Trends Food Sci. Technol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.meatsci.2016.09.017","article-title":"Hyperspectral imaging with multivariate analysis for technological parameters prediction and classification of muscle foods: A review","volume":"123","author":"Cheng","year":"2017","journal-title":"Meat Sci."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Toldra, F., and Nollet, L.M.L. (2017). Hyperspectral Imaging Technique for Online Monitoring of Meat Quality and Safety. Advanced Technologies for Meat Processing, CRC Press. [2nd ed.].","DOI":"10.1201\/9781315152752"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.foodqual.2017.02.017","article-title":"Sensory attributes shaping consumers\u2019 willingness-to-pay for newly developed processed meat products with natural compounds and a reduced level of nitrite","volume":"70","author":"Hung","year":"2018","journal-title":"Food Qual. Prefer."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1017\/S1751731118001672","article-title":"Beef-eating quality: A European journey","volume":"12","author":"Farmer","year":"2018","journal-title":"Animal"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1515\/teme-2015-0043","article-title":"Visible hyperspectral imaging for lamb quality prediction","volume":"82","author":"Qiao","year":"2015","journal-title":"TM Tech. Mess."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.foodchem.2013.02.094","article-title":"Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging","volume":"141","author":"Kamruzzaman","year":"2013","journal-title":"Food Chem."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.ifset.2012.06.003","article-title":"Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression","volume":"16","author":"Kamruzzaman","year":"2012","journal-title":"Innov. Food Sci. Emerg. Technol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.jfoodeng.2014.06.025","article-title":"Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging","volume":"143","author":"Pu","year":"2014","journal-title":"J. Food Eng."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Selamat, J., and Iqbal, S. (2016). Food adulteration and authenticity. Food Safety, Springer.","DOI":"10.1007\/978-3-319-39253-0"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.meatsci.2017.04.010","article-title":"Application of Hyperspectral imaging to predict the pH, intramuscular fatty acid content and composition of lamb M. longissimus lumborum at 24 h post mortem","volume":"132","author":"Craigie","year":"2017","journal-title":"Meat Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aca.2011.11.037","article-title":"Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis","volume":"714","author":"Kamruzzaman","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.lwt.2015.11.021","article-title":"Hyperspectral imaging for real-time monitoring of water holding capacity in red meat","volume":"66","author":"Kamruzzaman","year":"2016","journal-title":"LWT\u2014Food Sci. Technol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.meatsci.2016.02.004","article-title":"Online monitoring of red meat color using hyperspectral imaging","volume":"116","author":"Kamruzzaman","year":"2016","journal-title":"Meat Sci."},{"key":"ref_97","first-page":"987","article-title":"Study on Tan-lamb mutton tenderness by using the fusion of hyperspectral spectrum and image information","volume":"27","author":"Wang","year":"2016","journal-title":"J. Optoelectron. Laser"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.talanta.2012.10.020","article-title":"Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis","volume":"103","author":"Kamruzzaman","year":"2013","journal-title":"Talanta"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Al-Sarayreh, M., Reis, M.M., Yan, W.Q., and Klette, R. (2018). Detection of red-meat adulteration by deep spectral\u2013spatial features in hyperspectral images. J. Imaging, 4.","DOI":"10.3390\/jimaging4050063"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jfoodeng.2015.11.024","article-title":"Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms","volume":"174","author":"Sanz","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.jfoodeng.2010.12.024","article-title":"Application of NIR hyperspectral imaging for discrimination of lamb muscles","volume":"104","author":"Kamruzzaman","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_102","unstructured":"Qiao, L., Peng, Y., Wei, W., and Li, C. (2015, January 26\u201329). Identification of main meat species based on spectral characteristics. Proceedings of the 2015 ASABE Annual International Meeting, New Orleans, LA, USA. Publication n\u00ba152189619."},{"key":"ref_103","unstructured":"Kim, M.S., Chao, K., and Chin, B.A. (2016, January 20\u201321). Rapid discrimination of main red meat species based on near-infrared hyperspectral imaging technology. Proceedings of the SPIE\u2014Sensing for Agricultural and Food Quality and Safety VIII, Baltimore, MD, USA. Publication n\u00ba98640U."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1007\/s12161-019-01577-6","article-title":"Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef","volume":"12","author":"Jiang","year":"2019","journal-title":"Food Anal. Methods"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Jiang, H., Cheng, F., and Shi, M. (2020). Rapid identification and visualization of jowl meat adulteration in pork using hyperspectral imaging. Foods, 9.","DOI":"10.3390\/foods9020154"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Yu, H., Zhang, S., Du, Y., Sheng, Z., Chu, Y., Zhang, D., Guo, L., and Deng, L. (2020). Visualization accuracy improvem;ent of spectral quantitative analysis for meat adulteration using Gaussian distribution of regression coefficients in hyperspectral imaging. Optik, 164737.","DOI":"10.1016\/j.ijleo.2020.164737"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.aca.2014.08.043","article-title":"Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review","volume":"853","author":"Kamruzzaman","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_108","unstructured":"Toldra, F., and Nollet, L.M.L. (2018). Raman spectroscopy for predicting meat quality traits. Advanced Technologies for Meat Processing, CRC Press. [2nd ed.]."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Kucha, C.T., Liu, L., and Ngadi, M.O. (2018). Non-destructive spectroscopic techniques and multivariate analysis for assessment of fat quality in pork and pork products: A review. Sensors, 18.","DOI":"10.3390\/s18020377"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2618","DOI":"10.1039\/C8AN01958D","article-title":"Investigation of chemical composition of meat using spatially off-set Raman spectroscopy","volume":"144","author":"Fowler","year":"2019","journal-title":"Analyst"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Beganovi\u0107, A., Hawthorne, L.M., Bach, K., and Huck, C.W. (2019). Critical review on the utilization of handheld and portable Raman spectrometry in meat science. Foods, 8.","DOI":"10.3390\/foods8020049"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.meatsci.2018.06.016","article-title":"Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy","volume":"145","author":"Andersen","year":"2018","journal-title":"Meat Sci."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Cama-Moncunill, R., Cafferky, J., Augier, C., Sweeney, T., Allen, P., Ferragina, A., Sullivan, C., Cromie, A., and Hamill, R.M. (2020). Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics. Meat Sci., 108157.","DOI":"10.1016\/j.meatsci.2020.108157"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1002\/jrs.5830","article-title":"Differentiating various beef cuts using spatially offset Raman spectroscopy","volume":"51","author":"Fowler","year":"2020","journal-title":"J. Raman Spectrosc."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1007\/s00217-014-2168-1","article-title":"A rapid method for determination of the origin of meat and meat products based on the extracted fat spectra by using of Raman spectroscopy and chemometric method","volume":"238","author":"Boyaci","year":"2014","journal-title":"Eur. Food Res. Technol."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Saleem, M., Amin, A., and Irfan, M. (2020). Raman spectroscopy based characterization of cow, goat and buffalo fats. J. Food Sci. Technol.","DOI":"10.1007\/s13197-020-04535-x"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.meatsci.2014.06.042","article-title":"Raman spectroscopy compared against traditional predictors of shear force in lamb m. longissimus lumborum","volume":"98","author":"Fowler","year":"2014","journal-title":"Meat Sci."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.meatsci.2012.08.019","article-title":"Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss","volume":"93","author":"Schmidt","year":"2013","journal-title":"Meat Sci."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.meatsci.2014.02.018","article-title":"Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy","volume":"97","author":"Fowler","year":"2014","journal-title":"Meat Sci."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.meatsci.2015.06.010","article-title":"Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device","volume":"108","author":"Fowler","year":"2015","journal-title":"Meat Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.meatsci.2015.06.016","article-title":"Prediction of intramuscular fat content and major fatty acid groups of lamb M. longissimus lumborum using Raman spectroscopy","volume":"110","author":"Fowler","year":"2015","journal-title":"Meat Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s11745-007-3059-z","article-title":"Classification of adipose tissue species using Raman spectroscopy","volume":"42","author":"Beattie","year":"2007","journal-title":"Lipids"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.meatsci.2018.05.021","article-title":"Predicting aged pork quality using a portable Raman device","volume":"145","author":"Santos","year":"2018","journal-title":"Meat Sci."},{"key":"ref_124","unstructured":"Vo-Dinh, T., Lieberman, R.A., and Gauglitz, G. (2009, January 13\u201317). Handheld Raman sensor head for in-situ characterization of meat quality applying a mircosystem 671nm diode laser. Proceedings of the SPIE\u2014Sensing for Agricultural and Food Quality and Safety VII, Orlando, FL, USA. Publication n\u00ba73120H."},{"key":"ref_125","unstructured":"Maltin, C., Craigie, C., and B\u00fcnger, L. (2015). Spectral imaging techniques for predicting meat quality\u2014An Australasian perspective. FAIM Farm Animal Imaging\u2014A Summary Report, SRUC."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.foodcont.2017.07.013","article-title":"Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances","volume":"84","author":"Feng","year":"2018","journal-title":"Food Control"},{"key":"ref_127","unstructured":"Font-i-Furnols, M., \u010candek-Potokar, M., Maltin, C., and Prevolnik Pov\u0161e, M. (2015). Future trends in non-invasive technologies suitable for quality determinations. A Handbook of Reference Methods for Meat Quality Assessment, SRUC."}],"container-title":["Foods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2304-8158\/9\/8\/1074\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:57:42Z","timestamp":1760176662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2304-8158\/9\/8\/1074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,7]]},"references-count":127,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["foods9081074"],"URL":"https:\/\/doi.org\/10.3390\/foods9081074","relation":{},"ISSN":["2304-8158"],"issn-type":[{"value":"2304-8158","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,7]]}}}