Abstract：The prediction model of beef’s storage time was established based on multi indexes of fresh beef, such as TVB-N, colony total, pH value, and L* parameter. Visible and near-infrared spectroscopy (Vis/NIR) combined with interval PLS(iPLS)and genetic algorithm(GA) was investigated for establishing PLS calibration model of above 4 indexes, respectively, and rapid and nondestructive prediction of the storage time of fresh beef stored at 4 ℃ was realized. PLS models of 4 indexes were built with full spectrum and effective variables selected by iPLS and iPLS-GA method, respectively. The performance of each model was evaluated according to two correlations coefficients(R) and standard error (SE) of calibration and prediction sets. Experimental results showed that the performance of all models built with effective variable selected by iPLS-GA was better than full spectrum and iPLS. The storage time of calibration and prediction sets of beef samples was predicted by storage time model with predicted values of above 4 indexes, and was achieved as follows: Rc=0.903, Rp=0.897, SEC=1.88 and SEP=2.24. The study demonstrated that the beef’s storage time can be synthetically predicted with multi-index by using visible and near-infrared spectroscopy combined with the prediction model of beef’s storage time. This provides a new method for rapid and non-destructive detection of beef’s storage time or shelf life.
Key words：Visible and near-infrared spectroscopy;Beef storage time;Multiple determination;Variable selection;PLS
马世榜1, 2，徐 杨1*，汤修映1，田潇瑜1，付 姓1 . 利用可见近红外光谱多指标综合预测生鲜牛肉储存期 [J]. 光谱学与光谱分析, 2012, 32(12): 3242-3246.
MA Shi-bang1, 2, XU Yang1*, TANG Xiu-ying1, TIAN Xiao-yu1, FU Xing1 . Prediction of Storage Time of Fresh Beef with Multi-Index Using Visible and Near-Infrared Spectroscopy . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(12): 3242-3246.
[1] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍，袁洪福，徐广通，等). Modern Near Infrared Spectroscopy Analytical Technology(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京：中国石化出版社)，2006. 10. [2] Savenije B, Geesink G H, van der Palen J G P, et al.Meat Science, 2006, (73): 181. [3] Andrés S, Silva A, Soares-Pereira A L, et al. Meat Science, 2008, (78): 217. [4] Prieto N, Andrés S, Gira′ldez F J, et al. Meat Science, 2008, (79): 692. [5] Holmer S F, McKeith R O, Boler D D, et al. Meat Science, 2009, (82): 86. [6] Raúl G, Antonio J S, Joel G, et al. Food Research International, 2011, (44): 331. [7] Norgaard L, Saudland A, Wagneret J, et al. Applied Spectroscopy, 2000, (54): 413. [8] CAI Jian-rong, WAN Xin-min, CHEN Quan-sheng(蔡健荣, 万新民, 陈全胜). Acta Optica Sinica(光学学报)，2009, 2(10)：2808. [9] Leardi R, González A L. Chemometrics and Intelligent Laboratory Systems, 1998, (41): 195. [10] Leardi R. Journal of Chemonetrics, 2000, (14): 643. [11] Ying Yibin, Liu Yande. Journal of Food Engineering, 2008，(84): 206. [12] Xu H R, Qi B, Sun T, et al. Journal of Food Engineering, 2012, (109): 142. [13] Chen Q S, Jiang P, Zhao J W. Spectrochimica Acta Part A, 2010, (76): 50. [14] Zou X B, Zhao J W, Huang X Y, et al. Chemometrics and Intelligent Laboratory Systems, 2007, (87): 43. [15] Christoffer Abrahamsson, Jonas Johansson, Anders Sparén, et al. Chemometrics and Intelligent Laboratory Systems, 2003, (69): 3.
