高光谱技术结合CARS算法的库尔勒香梨可溶性固形物定量测定

doi:10.3964/j.issn.1000-0593(2014)10-2752-06

Abstract
References
Related Citation (15)

Download: PDF (3583 KB)
Export: BibTeX | EndNote (RIS)

Abstract Hyperspectral imaging has large data volume and high dimensionality, and original spectra data includes a lot of noises and severe scattering. And, quality of acquired hyperspectral data can be influenced by non-monochromatic light, external stray light and temperature, which resulted in having some non-linear relationship between the acquired hyperspectral data and the predicted quality index. Therefore, the present study proposed that competitive adaptive reweighted sampling (CARS) algorithm is used to select the key variables from visible and near infrared hyperspectral data. The performance of CARS was compared with full spectra, successive projections algorithm (SPA), Monte Carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and GA-SPA (genetic algorithm-successive projections algorithm). Two hundred Korla fragrant pears were used as research object. SPXY algorithm was used to divided sample set to correction set with 150 samples and prediction set with 50 samples, respectively. Based on variables selected by different methods, linear PLS and nonlinear LS-SVM models were developed, respectively, and the performance of models was assessed using parameters r², RMSEP and RPD. A comprehensive comparison found that GA, GA-SPA and CARS can effectively select the variables with strong and useful information. These methods can be used for selection of Vis-NIR hyperspectral data variables, particularly for CARS. LS-SVM model can obtain the best results for SSC prediction of Korla fragrant pear based on variables obtained from CARS method. r², RMSEP and RPD were 0.851 2, 0.291 3 and 2.592 4, respectively. The study showed that CARS is an effectively hyperspectral variable selection method, and nonlinear LS-SVM model is more suitable than linear PLS model for quantitatively determining the quality of fragrant pear based on hyperspectral information.

Key words： Vis-NIR hyperspectral imaging Korla fragrant pear SSC Variable selection Modeling analysis

Received: 2014-05-16 Accepted: 2014-07-24

ZTFLH:

TP79

Corresponding Authors: NI Jun-hui E-mail: 56445627@qq.com

Cite this article:

ZHAN Bai-shao,NI Jun-hui,LI Jun. Hyperspectral Technology Combined with CARS Algorithm to Quantitatively Determine the SSC in Korla Fragrant Pear[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(10): 2752-2757.

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2014)10-2752-06 OR http://www.gpxygpfx.com/EN/Y2014/V34/I10/2752

[1] Zhang B, Huang W, Li J, et al. Food Research International, 2014, 62: 326.
[2] Elmasry G, Kamruzzaman M, Sun D W, et al. Critical Reviews in Food Science and Nutrition, 2012, 52(11): 999.
[3] Lorente D, Aleixos N, Gómez-Sanchis J, et al. Food Bioprocess Technol., 2012, 5(4): 1121.
[4] Zhang R, Ying Y, Rao X, et al. Journal of the Science of Food and Agriculture, 2012, 92(12): 2397.
[5] Balabin M R, Smirnov S V. Analytica Chimica Acta, 2011, 692(1): 63.
[6] Zou X B, Zhao J W, Malcolm J W, et al. Analytica Chimica Acta, 2011, 667(1): 14.
[7] Li Jiangbo, Zhao Chunjiang, Huang Wenqian, et al. Analytical Methods, 2014, 6: 2170.
[8] Nicola B M., Beullens K, Bobelyn E, et al. Postharvest Biology and Technology, 2007, 46(2): 99.
[9] Cao F, Wu D, He Y. Computers and Electronics in Agriculture, 2010, 71(Suppl.): S15.
[10] Sun X D, Zhang H L, Liu Y D. International Journal of Agricultural and Biological Engineering, 2009, 2(1): 65.
[11] Shao Y N, Bao Y D, He Y. Food and Bioprocess Technology, 2011, 4(8): 1376.
[12] Mireei S A, Mohtasebi S S, Sadeghi M. International Journal of Food Properties，2004，17(6)：1199.
[13] Li Hongdong, Liang Yizeng, Xu Qingsong, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[14] Di Nezio M S, Pistonesi M F, Fragoso W D, et al. Microchemical Journal, 2007, 85(2): 194.
[15] Cai W S, Li Y K, Shao X G. Chemometrics and Intelligent Laboratory Systems, 2008, 90(2): 188.
[16] ZHU Wei-xing, JIANG Hui, CHEN Quan-sheng(朱伟兴, 江辉, 陈全胜). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 368.
[17] 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.
[18] Pereira A F C, Pontes M J C, Neto F F G, et al. Food Research International, 2008, 41(4): 341.
[19] Chauchard F, Cogdillb R, Roussel R, et al. Chemometrics and Intelligent Laboratory Systems, 2004，71(2): 141.
[20] Li Jiangbo, Huang Wenqian, Chen Liping, et al. Food Analytical Methods，2014, doi:10.1007/s12161-014-9832-8.
[21] Wu Di, Chen Jianyang, Lu Baiyi, et al. Food Chemistry, 2012, 135(4): 2147.
[22] Liu Fei, He Yong. Food Chemistry, 2009, 115(4): 1430.