基于拉曼光谱法所建的多元校正模型预测烟草中绿原酸和芸香苷的含量北大核心CSCD

为了满足现场批量检测的需求,基于拉曼光谱建立了多元校正模型,实现了烟草中绿原酸和芸香苷含量的预测。120个烟草样品(包含90个校正集样品和30个验证集样品)用50%(体积分数)甲醇溶液萃取后注入拉曼光谱液体池中,在325 nm激发波长下采集800~2000 cm^(-1)内的拉曼光谱,采用Savitzky-Golay卷积平滑法预处理所得原始拉曼光谱,用Monte-Carlo交互检验法选择隐变量数目,并在1555.8~1652.9 cm^(-1)波段内建立偏最小二乘法(PLS)多元校正模型,以避免绿原酸和芸香苷拉曼光谱在1600 cm^(-1)附近的光谱重叠干扰。结果显示,所建绿原酸和芸香苷模型的预测均方根误差(RMSEP)分别为0.88和0.67,预测集决定系数(R_(p)^(2))分别为0.948和0.970,说明基于拉曼光谱和PLS所建模型,可以对烟草中多酚类化合物绿原酸和芸香苷含量实现准确可靠的预测。     

光谱预处理方法对混胺近红外定量模型影响的研究

本文探讨了一阶导数、Savitzky-Golay平滑、小波变换(WT)和正交信号校正(OSC)预处理方法对混胺组分近红外光谱的预处理效果。采用预测集标准偏差(SEP)对各种预处理方法的最佳参数进行选择。比较了各种预处理方法的去噪能力,对预处理方法组合后的效果进行了评价。研究表明,WT和OSC能有效去除噪声,提高分析模型的精度,WT和OSC结合后的预处理效果更好。     

近红外光谱测定人参与西洋参的主要皂甙总量

采用近红外光谱测定人参与西洋参的主要皂甙总量.采集人参与西洋参的漫反射光谱,分别对光谱进行正交信号校正(OSC)与常规预处理,建立了对应的偏最小二乘(PLS)回归模型.与常规最优预处理方法相比,OSC能很好地消除人参与西洋参的品种差异,显著提高了光谱与皂甙含量的相关系数,同时降低了PLS建模因子数,提高了模型的稳健性与...     

不同比色皿对近红外光谱法检测水质酸度的影响及校正分析北大核心CSCD

为减小不同比色皿对近红外光谱测量结果的影响,提高水质酸度定量分析模型的预测精度,探讨了正交信号校正(OSC)用于不同比色皿的光谱背景干扰的去除效果。用两个同一批次的石英比色皿对32个不同pH的水样装样,采集近红外光谱数据,采用OSC对原始光谱进行预处理。比较OSC预处理前后两组光谱间的差异,并建立了水质酸度的偏最小二乘(PLS)定量分析模型,分析了光谱差异对模型预测精度的影响。结果表明:经OSC预处理后,两组光谱的平均差异值由0.0042降低至0.0013,光谱校正率达90%;与原始光谱建立的PLS模型相比,基于OSC预处理后的光谱建立的PLS模型的预测精度显著提高,预测均方根误差差值由0.912降低至0.205,相关系数差值由0.364降低至7.00×10^(-3),二者分别减小了78%和98%。     

基于高光谱图像的生菜叶片氮素含量预测模型研究

为了便于更经济合理地为作物施肥,建立一种无损检测作物氮营养元素的高光谱图像模型。本实验以生菜为研究对象,无土栽培各氮素水平的生菜叶样本,在莲座期,采集生菜叶片样本的高光谱图像(390~1050 nm),同时采用凯氏定氮法测定对应生菜叶片样本的全氮含量。通过ENVI软件提取出生菜叶片中感兴趣区域的平均光谱作为该样本原始光谱信息,分别使用平滑处理(Smoothing)、多元散射矫正(MSC)、标准正态变量变换结合去趋势(SNV detrending)、一阶导数法(First derivative)、二阶导数法(Second derivative)、正交信号矫正(OSC)等预处理方法对样本原始光谱进行处理,然后利用偏最小二乘回归法(Partial least squares regression,PLSR)分别建立样本全波段光谱信息与氮含量的关系模型,研究各预处理方法对氮含量模型的影响,结果表明,使用OSC预处理的模型效果最好。为了简化模型,根据OSC预处理光谱后的模型的PLSR回归系数优选出敏感波长,利用训练集中样本的敏感波长光谱信息与氮含量数据重新构建PLSR回归模型,并利用测试集样本进行测试试验。结果表明,该模型得到校正集和预测集的决定系数(R2p)分别为0.89,0.81;均方根误差RMSEC,RMSEP分别为0.33,0.45。该回归模型大大降低了自变量个数,简化了模型,并且取得了较优的效果,这为生菜氮素含量预测提供了一种新的快速有效方法。     

基于小波变换-遗传算法-偏最小二乘的草莓糖度检测研究

采用近红外漫反射光谱分析技术,对草莓糖度进行了无损检测研究。利用便携式近红外光谱仪采集草莓样品在600～1 100 nm波段内的漫反射光谱数据。首先利用小波变换(WT)多分辨率方法对光谱数据进行去噪预处理,然后利用遗传算法(GA)优选特征波长,最后运用偏最小二乘法(PLS)建立草莓糖度的WT-GA-PLS校正模型。该模型校正集的相关系数R_C为0.9395,校正集的均方根误差RMSEC为0.1615,预测集的相关系数R_P为0.9652,预测集的均方根误差EMSEP为0.5042。与全光谱模型(FS-PLS)和小波变换模型(WT-PLS)相比,该模型预测能力更强,稳健性更优。     

近红外光谱法快速鉴定油菜杂交种的纯度

建立近红外光谱技术测定油菜杂交种纯度的方法。考察了样品杯类型、光谱预处理方法和波长范围对近红外模型预测性能的影响。结果发现,由不同样品杯采集近红外光谱所建立的校正模型,其预测性能存在较大的差异,旋转杯明显优于安瓿瓶;采用消除常数偏移量对光谱进行预处理能有效地提取光谱信息,选择5 000~8 000 cm–1波数范围作为建模谱区,其包含的有效信息率最高。在最佳条件下建立油菜杂交种纯度的校正模型,其决定系数(R2)为0.980 0,交互验证均方根误差(RMSECV)为0.008 59。利用该模型对预测集进行测定,预期均方根误差(RMSEP)为0.007 59,表明该模型具有很好的预测性能,近红外光谱法用于杂交种纯度的鉴定是可行的。     

胆酸含量的近红外分析数学模型

本文应用近红外技术研究了快速测定胆酸含量的方法.通过测定胆酸在10000~4000cm-1范围内的近红外透射光谱,基于偏最小二乘(PLS)算法,建立了胆酸含量的数学模型.以校正均方差(RMSEC)和相关系数(R)为指标,确定了用于建模的最优近红外波段和光谱预处理方法,并基于此模型预测了9个样品.结果显示,建模效果良好,...     

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。     

小波变换结合连续投影算法优化茶叶中咖啡碱的近红外分析模型

本文着重探讨小波变换及其它光谱预处理方法对连续投影算法(SPA)波长筛选优化及建模效果的影响。以158个不同茶叶样本作为研究对象,将各种预处理方法单独或组合后与SPA结合使用,并通过偏最小二乘法(PLS)建立咖啡碱定量模型。其中一阶微分(WT-1~(st)D)-SPA组合建立的模型最佳,预测相关系数达到0.9481,均方根误差达到0.3053,验证集相对分析误差达到3.1959,建模变量由1038减小为10,其挑选出的波长数10和通过交叉验证确定的最佳PLS成分数7也比较接近,并包含在茶叶咖啡碱主要吸收谱带范围内。结果表明,小波变换结合WT-1~(st)D方法在消除光谱部分散射误差和高频噪声的同时,能有效提高茶叶光谱的分辨率,有助于SPA算法筛选出更少、代表性和独立性更优的特征波长组合,并极大地改善了模型的精度,为茶叶中咖啡碱的近红外分析建模提供了一种快速、简便的方法。     

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

Simultaneous spectrophotometric determination of cobalt,copper and nickel using 1-（2-thiazolylazo）-2-naphthol by chemometrics methods

The simultaneous determination of cobalt, copper and nickel using 1-（2-thiazolylazo）-2-naphthol （first figure of this article） by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares （PLS） regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction （OSC） is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 550-750-nm range for 21 different mixtures of cobalt, copper and nickel. Calibration matrices were formed from samples containing 0.05-1.05, 0.05-1.30 and 0.05-0.80 μg·mL^-1 for cobalt, copper and nickel, respectively. The root mean square error of prediction （RMSEP） for cobalt, copper and nickel with OSC and without OSC were 0.007, 0.008, 0.011 and 0.031,0.037, 0.032 μg· mL^-1, respectively. This procedure allows the simultaneous determination of cobalt, copper and nickel in synthetic and real samples and good reliability of the determination was proved.     

Combining orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping spectra of three kinds of metal ions

A novel method named OSC-WPT-PLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. OSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C. Wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for total elements using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).     

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.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

Orthogonal signal correction-partial least squares method for simultaneous spectrophotometric determination of cypermethrin and tetramethrin

The simultaneous determination of cypermethrin and tetramethrin mixtures by using spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200-350 nm range for 25 different mixtures of cypermethrin and tetramethrin. Calibration matrices were containing 0.1-12.9 and 0.1-13.8 microg mL(-1) for cypermethrin and tetramethrin, respectively. The RMSEP for cypermethrin and tetramethrin with OSC and without OSC were 0.0884, 0.0614 and 0.2915, 0.2309, respectively. This procedure allows the simultaneous determination of cypermethrin and tetramethrin in synthetic and real samples good reliability of the determination was proved.