Application of Near Infrared Reflectance Spectroscopy to Predict Meat Chemical Compositions: A Review
TAO Lin-li1, 2, YANG Xiu-juan1, 2, DENG Jun-ming1, 2, ZHANG Xi1, 2*
1. Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China 2. Key Laboratory of Animal Nutrition and Feed Science of Yunnan Province, Kunming 650201, China
Abstract：In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular, lack of homogeneity of the meat samples influenced the accuracy of estimation of chemical components. In general the predicting results of intramuscular fat，fatty acid and moisture are best, the predicting results of crude protein and myoglobin are better, while the predicting results of ash and collagen are less accurate.
[1] LU Wan-zhen(陆婉珍). Modern Near Infrared Spectroscopy Analytical Technology(现代近红外光谱分析技术). 2nd ed.(第2版). Beijing：China Petrochemical Press(北京：中国石化出版社)，2010. 13. [2] Jerry Workman Jr ，Lois Weyer. Practical Guide to Interpretive Near-Infrared Spectroscopy(近红外光谱解析实用指南). Translated by CHU Xiao-li，XU Yu-peng，TIAN Gao-you(褚小立，许育鹏，田高友，译). Beijing：Chemical Industry Press(北京：化学工业出版社)，2009. 8. [3] Viljoen M，Hoffman L C，Brand T S. Small Ruminant Research，2007，69：88. [4] Prieto N，Andrés S，Giráldez F J，et al. Meat Science，2008，79：692. [5] Andrés S，Murray I，Navajas E A，et al. Meat Science，2007，76：509. [6] Cozzolino D，Murray I. Swiss Society of Food Science and Technology，2004，37：447. [7] ZHANG Ning，ZHANG De-quan, LI Shu-rong, et al(张 宁，张德权，李淑荣，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2008，24(12)：309. [8] Osorio M T，Zumalacárregui J M，Prieto N，et al. Small Ruminant Research，2007，72：221. [9] Savenije B，Geesink G H，van der Palen J G P，et al. Meat Science，2006，73：181. [10] KAI Han，KONG Bao-hua(开 晗，孔保华). Meat Research(肉类研究)，2011，25(9)：35. [11] Prieto N，Roehe R，Lavin P，et al. Meat Science，2009，83：175. [12] De Marchi M，Berzaghi P，Boukha A，et al. Italian Journal of Animal Science，2007，6：421. [13] YANG Jian-song，MENG Qing-xiang，REN Li-ping，et al(杨建松，孟庆翔，任丽萍，等). Spectroscopy and Spectral Analysis(光谱学与学谱分析)，2010，30(3)：685. [14] Prieto N，Andrés S，Giráldez F J，et al. Meat Science，2006，74：487. [15] Alomar D，Gallo C，Castaňeda M，et al. Meat Science，2003，63：441. [16] Cozzolino D，Murray I. Journal of Near Infrared Spectroscopy，2002，10：37. [17] Cozzolino D，De Mattos D，Martins V. Animal Science，2002，74：477. [18] Tgersen G，Arnesen J F，Nielsen B N，et al. Meat Science，2003，63：515. [19] Sanderson R，Lister S J，Dhanoa M S，et al. Animal Science，1997，65：45. [20] Tgersen G，Isaksson T，Nielsen B N，et al. Meat Science，1999，51：97. [21] Ripoll G，Alberti P，Panea B，et al. Meat Science，2008，80：697. [22] LIU Wei，YU Xiang-lin，SUN Dong-dong，et al(刘 炜，俞湘麟，孙东东，等). Swine Production(养猪)，2005，(3)：47. [23] Barbin D F, ElMasry G, Sun D W, et al. Food Chemistry, 2013, 138: 1162. [24] Eduardo Z R，Ana G V，Emiliano De P S，et al. Food Chemistry，2011，129：1889. [25] Liao Yitao，Fan Yuxia, Cheng Fang. Meat Science，2010，86：901. [26] Chan D E，Walker P N，Mills E W. American Society of Agricultural Engineers，2002，45：1519. [27] LIU Kui-wu，CHENG Fang，LIN Hong-jian，et al(刘魁武，成 芳，林宏建，等). Spectroscopy and Spectral Analysis(光谱学与学谱分析)，2009，29(1)：102. [28] Viljoen M，Hoffman L C，Brand T S. Meat Science，2005，69：255. [29] Berzaghi P，Dalla Zotte A，Jansson L M，et al. Poultry Science，2005，84：128. [30] McDevitt R M，Gavin A J，Andres S，et al. Journal of Near Infrared Spectroscopy，2005，13：109. [31] Abeni F，Bergoglio G. Meat Science，2001，57：133. [32] Kamruzzaman M，ElMasry G，Sun D W，et al. Innovative Food Science and Emerging Technologies，2012，16(10)：218. [33] Anderson N M，Walker P N. American Society of Agricultural Engineers，2003，46：117. [34] Rodbotten R，Nilsen B N，Hildrum K I. Food Chemistry，2000，69：427. [35] Prevolnik M，andek-Potokar M，korjanc D，et al. Journal of Near Infrared Spectroscopy，2005，13：77. [36] Eduardo Z R，Ana G V，Emiliano De P S，et al. Food Chemistry，2011，129：889. [37] Barlocco N，Vadell A，Ballesteros F，et al. Animal Science，2006，82：111. [38] Brndum J，Munck L，Henckel P，et al. Meat Science，2000，55：177. [39] Hoving-Bolink A H，Vedder H W，Merks J W M，et al. Meat Science，2005，69：417. [40] Windham W R，Lawrence K C，Feldner P W. Journal of Applied Poultry Research，2003，12：69. [41] Cozzolino D，Murray I，Scaife J R，et al. Animal Science，2000，70：417. [42] Guy F，Prache S，Thomas A，et al. Food Chemistry，2011，127：1280. [43] Prieto N，Ross D W，Navajas EA，et al. Animal，2011，5(1)：155. [44] Gjerlaug-Enger E，Kongsro J，Aass L，degrd J，et al. Animal，2011，5(11)：1829. [45] LUO Xiang，LIU Bo-ping，ZHANG Xiao-lin，et al(罗 香，刘波平，张小林，等). Chinese Journal of Analysis Laboratory(分析试验室)，2007，26(10)：25. [46] González-Martín I，González-Pérez C，Alvarez-García N，et al. Meat Science，2005，69：243. [47] Ripoch A，Guillard A S. Meat Science，2001，58：299. [48] Prieto N，Dugan M E R，López-Campos O，et al. Meat Science，2012，90：43. [49] Sierra V，Aldai N，Castro P，et al. Meat Science，2008，78：248. [50] Realini C E，Duckett S K，Windham W R. Meat Science，2004，68：35. [51] Cecchinato A，De Marchi M，Penasa M，et al. Bittante G. Journal Animal Science，2012，90：429. [52] De Marchi M，Riovanto R，Penasa M，et al. Meat Science，2012，90：653. [53] Zhou L J，Wu H，Li J T，et al. Meat Science，2012，90：658. [54] Riovanto R，De Marchi M，Cassandro M，et al. Food Chemistry，2012，134：2459. [55] Hans B P. Food Chemistry，2003，82：107. [56] Prieto N，Andres S，Giraldez F J，et al. Meat Science，2008，79：198. [57] Mitsumoto M，Maeda S，Mitsuhashi T，et al. Journal of Food Science，1991，56(6)：1493. [58] Hong J H，Yasumoto K. Journal of Food Composition and Analysis，1996，9：127. [59] Cozzolino D，Barlocco N，Vadell A，et al. Lebensmittel-Wissenschaft und-Technologie-Food Science Technology，2003，36：195.
