近红外光谱快速检测马铃薯全粉还原糖
还原糖含量是评价马铃薯全粉品质的重要指标之一,该文研究基于近红外光谱技术结合最小二乘支持向量机(least squares support vector machine,LSSVM)算法的马铃薯全粉还原糖含量非线性数学模型。采用移动窗口偏最小二乘法(moving windows partial least square,MWPLS)和连续投影算法(successive projections algorithm,SPA)组合方法筛选出20个特征变量,作为LSSVM的输入向量。优化径向基函数(radial basis function,RBF)的惩罚因子和核参数,训练LSSVM校正模型。经比较,...
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Published in | 农业工程学报 Vol. 29; no. 14; pp. 262 - 268 |
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Main Author | |
Format | Journal Article |
Language | Chinese |
Published |
华东交通大学机电工程学院,南昌,330013%华东交通大学外国语学院,南昌,330013
2013
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Subjects | |
Online Access | Get full text |
ISSN | 1002-6819 |
DOI | 10.3969/j.issn.1002-6819.2013.14.033 |
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Abstract | 还原糖含量是评价马铃薯全粉品质的重要指标之一,该文研究基于近红外光谱技术结合最小二乘支持向量机(least squares support vector machine,LSSVM)算法的马铃薯全粉还原糖含量非线性数学模型。采用移动窗口偏最小二乘法(moving windows partial least square,MWPLS)和连续投影算法(successive projections algorithm,SPA)组合方法筛选出20个特征变量,作为LSSVM的输入向量。优化径向基函数(radial basis function,RBF)的惩罚因子和核参数,训练LSSVM校正模型。经比较,LSSVM校正模型预测结果最优,预测相关系数为0.984,预测标准差为0.223%,相对分析误差(standard deviation ratio,SDR)为5.62。结果表明:近红外光谱结合LSSVM算法提高了马铃薯全粉还原糖含量的预测精度。 |
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AbstractList | 还原糖含量是评价马铃薯全粉品质的重要指标之一,该文研究基于近红外光谱技术结合最小二乘支持向量机(least squares support vector machine,LSSVM)算法的马铃薯全粉还原糖含量非线性数学模型。采用移动窗口偏最小二乘法(moving windows partial least square,MWPLS)和连续投影算法(successive projections algorithm,SPA)组合方法筛选出20个特征变量,作为LSSVM的输入向量。优化径向基函数(radial basis function,RBF)的惩罚因子和核参数,训练LSSVM校正模型。经比较,LSSVM校正模型预测结果最优,预测相关系数为0.984,预测标准差为0.223%,相对分析误差(standard deviation ratio,SDR)为5.62。结果表明:近红外光谱结合LSSVM算法提高了马铃薯全粉还原糖含量的预测精度。 TP374+.52%TS235.2; 还原糖含量是评价马铃薯全粉品质的重要指标之一,该文研究基于近红外光谱技术结合最小二乘支持向量机(least squares support vector machine,LSSVM)算法的马铃薯全粉还原糖含量非线性数学模型。采用移动窗口偏最小二乘法(moving windows partial least square,MWPLS)和连续投影算法(successive projections algorithm, SPA)组合方法筛选出20个特征变量,作为LSSVM的输入向量。优化径向基函数(radial basis function,RBF)的惩罚因子和核参数,训练LSSVM校正模型。经比较,LSSVM校正模型预测结果最优,预测相关系数为0.984,预测标准差为0.223%,相对分析误差(standard deviation ratio,SDR)为5.62。结果表明:近红外光谱结合LSSVM算法提高了马铃薯全粉还原糖含量的预测精度。 |
Abstract_FL | Reducing sugar content is one of the important indicators for evaluating the quality of potato granules. Near-infrared (NIR) spectroscopy has been attempted to determine reducing sugar content in potato granules using near-infrared (NIR) spectroscopy combined with least squares support vector machine (LSSVM) algorithm. NIR spectra were recorded in the wavenumber range of 10 000~4 000 cm-1 at a 4 cm-1 interval. The 110 samples were divided into calibration and prediction sets in terms of their respective actual value for avoiding bias in subset division. One of every four samples was divided into the prediction set according the range of actual value in calibration set covering the range in the prediction set. The calibration set contained 83 samples, and the remaining 27 samples constituted the prediction set. Three different variable selection methods, namely the moving windows partial least square (MWPLS), MWPLS-genetic algorithm (MWPLS-GA), and MWPLS-successive projection algorithm (MWPLS-SPA), were performed comparatively to choose spectral variables associated with reducing sugar content distributions. The partial least square (PLS) models were developed with these selection spectral variables with the number of PLS components optimized according to root mean square error of cross validation (RMSECV) in the calibration set. The results derived by variable selection techniques were then compared with the performance of PLS models with new samples in the prediction set. The PLS calibration model exhibited a higher correlation coefficient of prediction (Rp) of 0.976, lower standard error of prediction (SEP) of 0.273%, and ratio of SEP and standard deviation (SDR) of 4.593, which was built using 20 spectral variables selected by the MWPLS-SPA method. Nonlinear models of the least squares support vector machine (LSSVM) were developed using different spectral variables selected by MWPLS, MWPLS-GA, and MWPLS-SPA. The main parameters of penalty factor (γ) and nuclear parameters (σ2) of the nuclear function for the radial basis function (RBF) were optimized by a two-step search method. Through comparison the performance of LSSVM models with new samples, the optimal LSSVM models for reducing sugar content were obtained with Rp of 0.984, SEP of 0.223%, and SDR of 5.62, which were developed with 20 spectral variables selected by the MWPLS-SPA method. The results indicated that: 1) the accuracy of the quantitative analysis conducted by NIR spectroscopy can be improved through appropriate wavelength selection with the MWPLS-SPA method;and 2) the implementation of LSSVM nonlinear models could predict reducing sugar content in potato granules more accurately than a linear model of PLS. It was concluded that NIR spectroscopy combined with MWPLS-SPA and LSSVM methods has significant potential to quantitatively analyze reducing sugar content in potato granules, and this real time, in situ measurement will significantly improve the efficiency of quality control and assurance. |
Author | 孙旭东 董小玲 |
AuthorAffiliation | 华东交通大学机电工程学院,南昌330013 华东交通大学外国语学院,南昌330013 |
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Author_FL | Sun Xudong Dong Xiaoling |
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DocumentTitleAlternate | Rapid detection of reducing sugar for potato granules by near infrared spectroscopy |
DocumentTitle_FL | Rapid detection of reducing sugar for potato granules by near infrared spectroscopy |
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Keywords | 算法 nondestructive examination 变量选择 近红外光谱 potato reducing sugar 还原糖 near infrared spectroscopy 无损检测 variable selection 马铃薯 algorithm |
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Notes | Sun Xudong, Dong Xiaoling (1. School of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, China; 2. School of Foreign Language, East China Jiaotong University, Nanchang 330013, China) 11-2047/S Reducing sugar content is one of the important indicators for evaluating the quality of potato granules. Near-infrared (NIR) spectroscopy has been attempted to determine reducing sugar content in potato granules using near-infrared (NIR) spectroscopy combined with least squares support vector machine (LSSVM) algorithm. NIR spectra were recorded in the wavenumber range of 10 000-4 000 cm-1 at a 4 cm-1 interval. The 110 samples were divided into calibration and prediction sets in terms of their respective actual value for avoiding bias in subset division. One of every four samples was divided into the prediction set according the range of actual value in calibration set covering the range in the prediction set. The calibration set contained 83 samples, and the remaining 27 samples constituted the p |
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SubjectTerms | 变量选择 无损检测 算法 近红外光谱 还原糖 马铃薯 |
Title | 近红外光谱快速检测马铃薯全粉还原糖 |
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