Abstract：The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350~1 000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate that Vis/NIR spectroscopy combined with UVE-ICA-LS-SVM is suitable for assessing SSC of Nanfeng mandarin, and the precision of prediction is high. UVE-ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.
孙 通，许文丽，胡 田，刘木华* . 基于UVE-ICA和支持向量机的南丰蜜桔可溶性固形物可见-近红外检测 [J]. 光谱学与光谱分析, 2013, 33(12): 3235-3239.
SUN Tong, XU Wen-li, HU Tian, LIU Mu-hua* . Determination of Soluble Solids Content in Nanfeng Mandarin by Vis/NIR Spectroscopy and UVE-ICA-LS-SVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(12): 3235-3239.
[1] Sanchez M T, De la Haba M J, Benitez-Lopez M, et al. Journal of Food Engineering, 2012, 110(1): 102. [2] SUN Xu-dong, HAO Yong, CAI Li-jun, et al(孙旭东, 郝 勇, 蔡丽君, 等). Acta Biophysica Sinica(生物物理学报), 2011, 27(8): 727. [3] Sun T, Lin H, Xu H, et al. Postharvest Biology and Technology, 2009, 51(1): 86. [4] WANG Jia-hua, QI Shu-ye, TANG Zhi-hui, et al(王加华, 戚淑叶, 汤智辉, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(5): 1431. [5] YUAN Lei-ming, GAO Hai-ning, Lü Song, et al(袁雷明, 高海宁, 吕 松, 等). Journal of Food Safety and Quality(食品安全质量检测学报), 2012, 3(5): 448. [6] Shao Y N, He Y, Bao Y D, et al. International Journal of Food Properties, 2009, 12(3): 644. [7] ZHANG Peng, LI Jiang-kuo, MENG Xian-jun, et al(张 鹏, 李江阔, 孟宪军, 等). Food Science(食品科学), 2011, 32(6): 191. [8] LUO Yuan, WANG Ling-xue, JIN Wei-qi(骆 媛, 王岭雪, 金伟其). Optical Technique(光学技术), 2012, 38(5): 520. [9] YUAN Li-fen，LIU Hui，CHENG Jun(袁莉芬, 刘 辉, 程 俊). Journal of Natural Science of Hunan Normal University(湖南师范大学自然科学学报), 2011, 34(3): 24. [10] DU Yan-qin, HUANG Hua(杜艳琴, 黄 华). Journal of Biomedical Engineering(生物医学工程学杂志), 2011, 28(5): 941. [11] HOU Zhen-yu, YAO Shu-wen, GU Yong-qing, et al(侯振雨, 姚树文, 谷永庆, 等). Journal of Henan Normal University·Natural Science(河南师范大学学报·自然科学版), 2006, 34(2): 75. [12] FANG Li-min, LIN Min(方利民，林 敏). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(6): 815. [13] Shao Y N, Bao Y D, He Y. Food and Bioprocess Technology, 2011, 4: 1376. [14] XIE Xiu-juan，ZHAO Long-lian(谢秀娟, 赵龙莲). Acta Agriculturae Universitatis Jiangxiensi(江西农业大学学报), 2012, 34(4): 828. [15] Centner V, Massart D L, deNoord O E, et al. Analytical chemistry, 1996, 68(21)：3851. [16] Hyvarinen A, Oja E. Neural Networks, 2000, 13: 411. [17] Suykens J A K, Van Gestel T, De Brabanter J, et al. Least Squares Support Vector Machines. Singapore: World Scientific, 2002.
