基于变量优选和快速独立成分分析的黄花梨可溶性固形物可见/近红外光谱检测

doi:10.3964/j.issn.1000-0593(2014)12-3253-04

Abstract
References
Related Citation (15)

Download: PDF (1025 KB)
Export: BibTeX | EndNote (RIS)

Abstract The purpose of this study was to establish a mathematical model of the visible/near-infrared (Vis/NIR) diffuse transmission spectroscopy with fine stability and precise predictability for the non destructive testing of the soluble solids content of huanghua pear, through comparing the effects of various pretreatment methods, variable optimization method, fast independent principal component analysis (FICA) and least squares support vector machines (LS-SVM) on mathematica model for SSC of huanghua pear, and the best combination of methods to establish model for SSC of huanghua pear was got. Vis/NIR diffuse transmission spectra of huanghua pear were acquired by a Quality Spec spectrometer, three methods including genetic algorithm, successive projections algorithm and competitive adaptive reweighted sampling (CARS) were used firstly to select characteristic variables from spectral data of huanghua pears in the wavelength range of 550~950 nm, and then FICA was used to extract factors from the characteristic variables, finally, validation model for SSC in huanghua pears was built by LS-SVM on the basic of those parameters got above. The results showed that using LS-SVM on the foundation of the 21 variables screened by CARS and the 12 factors selected by FICA, the CARS-FICA-LS-SVM regression model for SSC in huanghua pears was built and performed best, the coefficient of determination and root mean square error of calibration and prediction sets were R²_C=0.974, RMSEC=0.116%, R²_P=0.918, and RMSEP=0.158% respectively, and compared with the mathematical model which uses PLS as modeling method, the number of variables was down from 401 to 21, the factors were also down from 14 to 12, the coefficient of determination of modeling and prediction sets were up to 0.023 and 0.019 respectively, while the root mean square errors of calibration and prediction sets were reduced by 0.042% and 0.010% respectively. These experimental results showed that using CARS-FICA-LS-SVM to build regression model for the forecast of SSC in huanghua pears can simplify the prediction model and improve the detection precision.

Key words：Visible/near infrared;Huanghua pear;CARS;FICA;LS-SVM;Soluble solids content

Received: 2013-10-09 Accepted: 2014-03-05

ZTFLH:

S123

Corresponding Authors: LIU Mu-hua E-mail: suikelmh@sina.com

Cite this article:

XU Wen-li,SUN Tong,HU Tian, et al. Huanghua Pear Soluble Solids Contents Vis/NIR Spectroscopy by Analysis of Variables Optimization and FICA [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(12): 3253-3256.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2014)12-3253-04 OR https://www.gpxygpfx.com/EN/Y2014/V34/I12/3253

[1] JIE Deng-fei, XIE Li-juan, RAO Xiu-qin, et al(介邓飞, 谢丽娟, 饶秀勤，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2013, 29(12): 264.
[2] Sanchez M T, De la Haba M J, Benitez-Lopez M, et al. Journal of Food Engineering, 2012, 110(1): 102.
[3] Jha S N, Jaiswal P, Narsaiah K, et al. Scientia Horticulturae, 2012, 138: 171.
[4] Lü Qiang, HE Shao-lan, LIU Bin, et al(吕强, 何绍兰, 刘斌, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(Suppl. 1): 211.
[5] FU Yi, ZHANG Yong-jun, CHEN Hua-cai, et al(傅谊, 张拥军, 陈华才, 等). Food Science and Technology(食品科技), 2012, 37(5): 42.
[6] Xu H R, Qi B, Sun T, et al. Journal of Food Engineering, 2012, 109(1): 142.
[7] Antonucci F, Pallottino F, Paglia G, et al. Food and Bioprocess Technology, 2011, 4(5): 809.
[8] Moghimi A, Aghkhani M H, Sazgarnia A, et al. Biosystems Engineering, 2010, 106(3): 295.
[9] OUYANG Ai-guo, XIE Xiao-qiang, ZHOU Yan-rui, et al(欧阳爱国, 谢小强, 周延睿, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(10): 2690.
[10] ZHAO Cong-hui, ZHANG Shu-juan, ZHANG Hai-hong, et al(赵聪慧, 张淑娟, 张海红, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2012, 12(3): 210.
[11] WU Feng, HUANG Li-ya(邬峰, 黄丽亚). Icrocomputer & Its Applications(微型机与应用), 2010, (9): 84.
[12] WU Di, WU Hong-xi, CAI Jing-bo, et al(吴迪，吴洪喜，蔡景波，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(6): 423.
[13] Li H, Liang Y, Xu Q, et al. Analytica Chimica Acta, 2009, 648(1): 77.
[14] 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.