Near-Infrared Hyperspectral Imaging Combined with CARS Algorithm to Quantitatively Determine Soluble Solids Content in “Ya” Pear
LI Jiang-bo1, 2, PENG Yan-kun2, CHEN Li-ping1, HUANG Wen-qian1*
1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China 2. College of Engineering, China Agricultural University, Beijing 100083, China
Abstract：The present study proposed competitive adaptive reweighted sampling (CARS) algorithm to be used to select the key variables from near-infrared hyperspectral imaging data of “Ya” pear. The performance of the developed model was evaluated in terms of the coefficient of determination(r2), and the root mean square error of prediction (RMSEP) and the ratio (RPD) of standard deviation of the validation set to standard error of prediction were used to evaluate the performance of proposed model in the prediction process. The selected key variables were used to build the PLS model, called CARS-PLS model. Comparing results obtained from CARS-PLS model and results obtained from full spectra PLS, it was found that the better results (r2pre=0.908 2, RMSEP=0.312 0 and RPD=3.300 5) were obtained by CARS-PLS model based on only 15.6% information of full spectra. Moreover, performance of CARS-PLS model was also compared with PLS models built by using variables got by Monte Carlo-uninformative variable elimination (MC-UVE) and genetic algorithms (GA) method. The result found that CARS variable selection algorithm not only can remove the uninformative variables in spectra, but also can reduce the collinear variables from informative variables. Therefore, this method can be used to select the key variables of near-infrared hyperspectral imaging data. This study showed that near-infrared hyperspectral imaging technology combined with CARS-PLS model can quantitatively predict the soluble solids content (SSC) in “Ya” pear. The results presented from this study can provide a reference for predicting other fruits quality by using the near-infrared hyperspectral imaging.
[1] Lorente D, Aleixos N, Gómez-Sanchis J, et al. Food Bioprocess Technol, 2012, 5: 1121. [2] LI Jiang-bo, RAO Xiu-qin, YING Yi-bin(李江波, 饶秀勤, 应义斌). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 8(31): 2021. [3] Balabin M R, Smirnov S V. Analytica Chimica Acta, 2011, 692: 63. [4] Ngaard L, Saudland A, Wagner J, et al. Applied Spectroscopy, 2000, 54: 413. [5] 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, 29(10): 2808. [6] Jiang J H, Berry R J, Siesler H W, et al. Analytical Chemistry, 2002, 74(14): 3555. [7] Delphine J R, Massart D L, Leardi R，et al. Analytical Chemistry, 1995, 67(23): 4295. [8] Di Nezio M S, Pistonesi M F, Fragoso W D, et al. MicroChemical Journal, 2007, 85: 194. [9] Liu F, He Y, Wang L, et al. Journal of Food Engineering, 2007, 83: 430. [10] Wu D, He Y, Feng S. Analytica Chimica Acta, 2008, 610(2): 232. [11] Wu D, He Y, Nie P C, et al. Analytica Chimica Acta, 2010, 659: 229. [12] Cai W S, Li Y K, Shao X G. Chemometrics and Intelligent Laboratory Systems, 2008, 90: 188. [13] Lu Renfu, Peng Yankun. Biosystems Engineering, 2006, 93(2): 161. [14] Li Hongdong, Liang Yizeng, Xu Qingsong, et al. Analytica Chimica Acta, 2009, 648: 77. [15] Kawano S, Abe H, Iwamoto M. Journal of Near Infrared Spectroscopy, 1995, 3(4): 211. [16] Cozzolino D, Liu L, Cynkar W U, et al. Analytica Chimica Acta, 2007, 588(2): 224. [17] Roberto Kawakami Harrop Galvo, Mário César Ugulino Araujo, Gledson Emídio José, et al. Talanta, 2005, 67: 736. [18] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Application(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京：化学工业出版社), 2011. [19] Nicola B M, Beullens K, Bobelyn E, et al. Postharvest Biology and Technology, 2007, 46: 99.
