Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry
LI Jiang-bo, GUO Zhi-ming, HUANG Wen-qian, ZHANG Bao-hua, ZHAO Chun-jiang*
Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Abstract:In using spectroscopy to quantitatively or qualitatively analyze the quality of fruit, how to obtain a simple and effective correction model is very critical for the application and maintenance of the developed model. Strawberry as the research object, this research mainly focused on selecting the key variables and characteristic samples for quantitatively determining the soluble solids content. Competitive adaptive reweighted sampling (CARS) algorithm was firstly proposed to select the spectra variables. Then, Samples of correction set were selected by successive projections algorithm (SPA), and 98 characteristic samples were obtained. Next, based on the selected variables and characteristic samples, the second variable selection was performed by using SPA method. 25 key variables were obtained. In order to verify the performance of the proposed CARS algorithm, variable selection algorithms including Monte Carlo-uninformative variable elimination (MC-UVE) and SPA were used as the comparison algorithms. Results showed that CARS algorithm could eliminate uninformative variables and remove the collinearity information at the same time. Similarly, in order to assess the performance of the proposed SPA algorithm for selecting the characteristic samples, SPA algorithm was compared with classical Kennard-Stone algorithm. Results showed that SPA algorithm could be used for selection of the characteristic samples in the calibration set. Finally, PLS and MLR model for quantitatively predicting the SSC (soluble solids content) in the strawberry were proposed based on the variables/samples subset (25/98), respectively. Results show that models built by using the 0.59% and 65.33% information of original variables and samples could obtain better performance than using the ones obtained by using all information of the original variables and samples. MLR model was the best with R2pre=0.909 7, RMSEP=0.348 4 and RPD=3.327 8.
李江波,郭志明,黄文倩,张保华,赵春江* . 应用CARS和SPA算法对草莓SSC含量NIR光谱预测模型中变量及样本筛选 [J]. 光谱学与光谱分析, 2015, 35(02): 372-378.
LI Jiang-bo, GUO Zhi-ming, HUANG Wen-qian, ZHANG Bao-hua, ZHAO Chun-jiang* . Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 372-378.
[1] Nicola B M, Beullens K, Bobelyn E, et al. Postharvest Biology and Technology, 2007, 46: 99. [2] Lin H J, Ying Y B. Sensing and Instrumentation for Food Quality and Safety, 2009, 3(2): 130. [3] Balabin M R, Smirnov S V. Analytica Chimica Acta, 2011, 692: 63. [4] Xu H R, Qi B, Sun T, et al. Journal of Food Engineering, 2012, 109: 142. [5] HUANG Wen-qian, LI Jiang-bo, CHEN Li-ping, et al(黄文倩, 李江波, 陈立平, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(10): 2843. [6] Galvo R K H, Araújo M C U, Fragoso W D, et al. Chemometrics and Intelligent Laboratory Systems, 2008, 92: 83. [7] Li Hongdong, Liang Yizeng, Xu Qingsong, et al. Analytica Chimica Acta, 2009, 648: 77. [8] ZHOU Zhu, LI Xiao-yu, GAO Hai-long, et al(周 竹, 李小昱, 高海龙, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(2): 128. [9] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Application(化学计量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. [10] Feudale R N, Woody N A, Tan H, et al, Chemom. Intell. Lab. Syst.,2002, 64: 181. [11] Kawano S, Abe H, Iwamoto M. Journal of Near Infrared Spectroscopy, 1995, 3(4): 211. [12] Cozzolino D, Liu L, Cynkar W U, et al. Analytica Chimica Acta, 2007, 588(2): 224. [13] CHEN Bin, MENG Xiang-long, WANG Hao(陈 斌, 孟祥龙, 王 豪). Journal of Instrumental Analysis(分析测试学报), 2007, 26(1): 66. [14] Galvo R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736. [15] Pereira A F C, Pontes M J C, Neto F F G, et al. Food Research International, 2008, 41(4): 341. [16] Araújo M C U, Saldanha T C B, Galvo R K H, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 57: 65. [17] Picard R R, Cook R D. Journal of the American Statistical Association, 1984, 79 (387): 575. [18] Cai W S, Li Y K, Shao X G. Chemometrics and Intelligent Laboratory Systems, 2008, 90: 188. [19] Nicola B M, Beullens K, Bobelyn E, et al. Postharvest Biology and Technology, 2007, 46(2): 99.