基于可见-近红外光谱技术的水稻穗颈瘟染病程度分级方法研究

采用ViS-NIR技术对水稻穗颈瘟染病程度分级方法进行了研究.分别基于原始光谱,变量标准化(SNV)预处理后和多元散射校正(MSC)预处理后的光谱,应用无信息变量消除法(UVE)结合连续投影算法(SPA)对Vis-NIR光谱区进行有效波长的选择.选择后的波长作为输入变量建立最小二乘-支持向量机(LS-SVM)模型.结果表明SNV-UVE-SPA建市的LS-SVM模型预测效果最好.通过SNV-UVE-SPA从全波段600个波长中选择了6个最能够反应光谱信息的波长(459,546,569,590,775和981 nm).SNV-UVE-SPA-LS-SVM组合模型对预测集样本预测得到的确定系数(R_p~2),预测集的预测标准差(RMSEP)和剩余预测偏筹(RPD)分别达到了 0.979,0.507和6.580.结果表明,采用Vis-NIR光谱技术对水稻穗颈瘟染病程度进行分级是可行的.通过UVE-SPA得到的有效波长能够很好地代替全波长.     

基于高光谱散射图像技术的UVE-LLE苹果粉质化分类

利用高光谱散射图像技术研究了苹果的粉质化无损检测.提出了一种无信息变量消除法和局部线性嵌入相结合的苹果粉质化分类的新方法.经无信息变量消除法筛选后的波段降为全谱的23.5%.将波段选择后的原始图像数据用局部线性嵌入降维作为偏最小二乘判别分析的输入变量并建模.无信息变量消除法与局部线性嵌入相结合算法和局部线性嵌入降维方法得到的粉质化分类测试准确度分别是79.0%和79.0%|无信息变量消除法与平均反射法相结合和平均反射法特征提取得到的是77.4%和75.8%.结果表明,无信息变量消除法与局部线性嵌入想结合的方法可以大大地降低高光谱散射图像的数据量,同时保证了分类准确度,为在线检测、分类和高光谱数据的存储提供了一种实时、有效的方法.     

中红外光谱对甲醇汽油甲醇含量检测研究

甲醇汽油是一种清洁能源，甲醇汽油中甲醇的含量决定了汽油的性能。通过中红外光谱对甲醇汽油中甲醇含量进行定量检测和分析。首先，对采集的甲醇汽油原始中红外光谱进行平滑处理（smoothing）、多元散射校正（MSC）、基线校正（baseline）、归一化（normalization）等预处理，再建立PLS模型，对比选择最佳预处理方法，结果表明：在多元散射校正（MSC）处理后建立的PLS模型效果最好，模型的预测集相关系数r为0.918，预测均方根误差RMSEP为2.107。为进一步简化模型，提高预测精度，采用无信息变量消除(uninformative variable elimination, UVE)方法对波长进行筛选，将UVE波段筛选之后的作为模型的输入变量，采用偏最小二乘法（partial least squares, PLS）、主成分回归(principal components regression, PCR)和最小二乘支持向量机（least square support vector machine, LSSVM）三种方法分别建立甲醇汽油中甲醇含量的定量预测模型，并比较不同模型的预测效果和结果。结果表明，使用无信息变量消除可以较好提高数据的运算速度，其中，UVE-PLS模型建模效果最好，r和RMSEP分别为0.923和2.075。该实验表明中红外光谱检测甲醇汽油中甲醇含量是可行的并可以得到较好的效果；UVE是一种对甲醇汽油的中红外光谱非常有效的波段筛选方法，该模型的建立对石油化工领域具有较为重要的意义。     

基于高光谱散射图像技术的UVE-LLE苹果粉质化分类

利用高光谱散射图像技术研究了苹果的粉质化无损检测.提出了一种无信息变量消除法和局部线性嵌入相结合的苹果粉质化分类的新方法.经无信息变量消除法筛选后的波段降为全谱的23.50％.将波段选择后的原始图像数据用局部线性嵌入降维作为偏最小二乘判别分析的输入变量并建模.无信息变量消除法与局部线性嵌入相结合算法和局部线性嵌入降维方...     

A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables

An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.     

可见/近红外联合UVE-PLS-LDA鉴别压榨和浸出山茶油

山茶油是我国特有的优质食用油,而压榨山茶油营养品质优于浸出山茶油。采用可见/近红外光谱技术对压榨和浸出山茶油进行鉴别研究。在350～1 800nm波段范围内采集压榨和浸出山茶油样本的透射光谱,利用无信息变量消除(UVE)方法进行波长变量优选,剔除无用波长变量,并应用偏最小二乘-线性判别分析(PLS-LDA)建立鉴别分类模型。最后,利用鉴别分类模型对未参与建模的26个预测集样本进行鉴别。研究结果表明,UVE-PLS-LDA是一种有效的鉴别分类方法,所建立的鉴别分类模型能较好地对压榨和浸出山茶油进行鉴别,校正集和预测集样本的鉴别正确率均为100%。因此,可见/近红外光谱联合UVE-PLS-LDA方法鉴别压榨和浸出山茶油是可行的。     

Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination

This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of α-linolenic and linoleic acid in eight types of edible vegetable oils and their blending. For this purpose, a combination of spectral wavelength selection by wavelet transform (WT) and elimination of uninformative variables (UVE) was proposed to obtain simple partial least square (PLS) models based on a small subset of wavelengths. WT was firstly utilized to compress full NIR spectra which contain 1413 redundant variables, and 42 wavelet approximate coefficients were obtained. UVE was then carried out to further select the informative variables. Finally, 27 and 19 wavelet approximate coefficients were selected by UVE for α-linolenic and linoleic acid, respectively. The selected variables were used as inputs of PLS model. Due to original spectra were compressed, and irrelevant variables were eliminated, more parsimonious and efficient model based on WT-UVE was obtained compared with the conventional PLS model with full spectra data. The coefficient of determination (r²) and root mean square error prediction set (RMSEP) for prediction set were 0.9345 and 0.0123 for α-linolenic acid prediction by WT-UVE-PLS model. The r² and RMSEP were 0.9054, 0.0437 for linoleic acid prediction. The good performance showed a potential application using WT-UVE to select NIR effective variables. WT-UVE can both speed up the calculation and improve the predicted results. The results indicated that it was feasible to fast determine α-linolenic acid and linoleic acid content in edible oils using NIR spectroscopy.     

近红外分析中光谱预处理及波长选择方法进展与应用

光谱预处理和波长选取方法在近红外光谱分析技术中相当重要。本文综述了常用的NIR预处理和波长选取方法及这一领域的最新进展，详细介绍正交信号校正(OSC)、净分析信号(NAS)和小波变换(WT)等新光谱预处理方法以及无信息变量消除(UVE)和遗传算法(GA)等波长选取方法，并给出了这些方法的具体算法和一些应用实例。