基于近红外光谱的维生素C片无损鉴别分析

基于近红外光谱技术与化学计量学方法,建立了一种国内外不同品牌维生素C片的无损鉴别方法。采集了国内外8个品牌的维生素C片共计40个样本的近红外光谱数据,比较了完整样品以及粉末样品的近红外光谱,采用连续小波变换技术消除背景干扰和基线漂移,基于标准偏差与相对标准偏差的变量筛选方法筛选出具有代表性的波数点,结合主成分分析方法对国内外不同品牌维生素C片进行鉴别分析。结果表明:原始光谱存在着明显的背景干扰和基线漂移现象,且粉末样品的重现性要优于完整样品;单纯使用原始光谱无法辨别来自不同品牌的维生素C片;连续小波变换可以有效消除背景干扰,提高模型鉴别能力;完整样品的鉴别准确率优于粉末样品,说明国内外不同品牌维生素C片主要成分基本一致,可能是辅剂和工艺上存在细微差异。通过结合近红外光谱分析技术与化学计量学方法,可实现对国产以及进口不同品牌维生素C片的鉴别分析。     

烟草近红外光谱分析网络化及其应用进展

该文回顾了近红外光谱(NIRS)分析技术的应用历程以及"近红外光谱分析+互联网"模式在烟草领域中的应用研究与实践,探讨了在近红外光谱分析网络化环境中,近红外光谱仪器设备存在的硬件差异以及常规化学计量学方法(算法)在建模、数据处理存在的不足对近红外光谱的深度应用产生的影响,并提出了近红外光谱分析云计算应用的解决思路。最后,对大数据时代近红外光谱分析网络化模式的应用前景进行了展望。     

一种基于近红外光谱技术的茶叶非法添加物鉴别方法

本发明公开了一种基于近红外光谱技术的茶叶非法添加物鉴别方法。本方法应用傅里叶变换近红外漫反射光谱分析技术结合因子法建立定性分析模型;应用傅里叶变换近红外漫反射光谱分析技术结合偏最小二乘法(PLS)建立定量分析模型,以两个模型结合应用实现茶叶中非法添加物(蔗糖、糖浆和米糊)的快速准确鉴别。本方法具有分析速度快、效率高、成本低、测试重现性好、样品无需预处理且便于在线无损检测的特点。     

基于近红外光谱技术的内燃机油鉴别研究

针对常规近红外光谱技术测试内燃机油时光谱信号响应低,对大分子基团分辨率不高,以及光谱信息与其结构组成之间存在非线性关系等难点,提出了一种基于电压为外扰方式的内燃机油二维近红外光谱测试技术,介绍了近红外光谱具有分形的特征.运用小波变换将近红外光谱分解至不同分辨尺度,然后计算各尺度分量的分形维数(盒维数),用近红外光谱的小波基分形参量替代近红外光谱的采样值.计算结果表明,在不同小波基和不同分解尺度下,内燃机油近红外光谱具有不同的盒维数,得到了近红外光谱在分形意义下的特征信息.以美孚、埃索和壳牌3种内燃机油品种鉴别分类问题为实例,比较研究了近红外光谱采样值与小波基分形参量,K近邻法的交互验证计算结果表明,小波基分形参量的分类效果优于近红外光谱采样值.采用近红外光谱技术测试内燃机油的结构组成信息是可行的.     

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-可见-近红外光谱技术用于鉴别品牌料酒的研究

模式识别技术广泛应用于食品种类、品牌和原产地的分类鉴别.本文测定了三个品牌114个料酒样品的可见-近红外光谱,利用小波变换技术对光谱信号进行了去噪和压缩处理,并采用Fisher权重法计算了16个小波细节系数的Fisher权重.以16个小波细节系数为特征变量采用向量相似度法对三种不同品牌料酒进行了相似度分析,主成分分析法...     

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。     

近红外光谱分析中的化学计量学算法研究新进展

近红外光谱是一种绿色、快捷的分析技术,在科学研究、工业生产以及日常检测中得到广泛应用。化学计量学算法的应用在近红外光谱技术的发展过程中发挥了重要作用。化学计量学方法通过寻找测量变量之间的相关性,构建数学模型,量化样本间的差异性,并发现事物变化的内在规律,实现较合理准确的未知预测。这也是"大数据"战略的重要环节和主旨所在。该文针对近红外光谱吸收信号较弱、谱峰重叠严重,以及光谱测量过程中易受背景、噪声、无信息变量和外界环境因素干扰等,导致借助化学计量学方法建立的光谱与研究目标的定性定量分析模型变差问题,总结了近年来在近红外光谱领域所提出的一些化学计量学新方法,包括光谱预处理、变量选择、多元校正和模型转移,从不同角度阐述了这些方法在消除近红外光谱模型的干扰因素,提高模型的可靠性、预测准确性和适用性等方面的作用。     

有机/无机复合微胶囊相变材料的近红外光谱分析

通过傅里叶变换近红外(FT-NIR)光谱分析技术, 探索聚甲基丙烯酸甲酯-二氧化硅@相变材料(PMMA-SiO₂@PCM)微胶囊相变过程的光谱学特性和相变机理, 分析相变过程微胶囊的微结构变化特性. 结果显示: 微胶囊中, 石蜡的融化过程就是－CH₂对称伸缩振动逐渐增强和非对称伸缩无规则振动共存的振动变化过程. 石蜡相变过程中, 其近红外吸收峰强度的变化仅是壳层材料吸收峰强度变化幅度的一半. 同时, 近红外光谱可以用来辅助分析微胶囊的核壳结构, 实现微胶囊相变过程监测. 近红外光谱在微胶囊相变材料相变过程的应用对相变机理的研究及高效相变材料的选择具有重要的科学意义和应用价值.     

一种基于近红外光谱技术的不同品种及掺假三七的无损鉴别分析研究

基于近红外光谱技术与化学计量学方法,提出了一种不同品种及掺假三七的快速无损鉴别方法。分别采集景天三七、菊三七、血三七、田三七完整、粉末及掺假样品的近红外光谱,采用单一和组合预处理方法消除光谱中的干扰,筛选出最佳的预处理方法;结合主成分分析法建立不同品种以及掺假三七样品的鉴别模型。结果表明：结合主成分分析,采用原始光谱即可实现粉末及掺假样品的100%鉴别分析,而完整样品由于受到物理性状的干扰,其原始光谱数据的品种鉴别率仅为9.38%;而经连续小波变换预处理后可达93.75%。采用组合预处理方法可以进一步消除光谱存在的多种干扰,显著提高完整样品的鉴别准确性,采用去偏移 + 一阶导数、去偏移+连续小波变换以及二阶导数+标准正态变量变换预处理方法预处理后,完整样品的鉴别准确率达到了93.75%。以上结果表明,采用近红外光谱技术与化学计量学方法可有效实现对不同品种以及掺假三七的快速无损鉴别分析。     

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.     

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.     

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 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.     

小波变换-分段直接校正法用于近红外光谱模型传递研究

提出了一种新的传递算法(WT-PDS)———小波变换-分段直接校正法,并详细讨论了模型传递参数和传递结果。首先利用小波变换对光谱进行压缩处理,采用PDS算法消除不同仪器之间压缩数据的差异,最后利用经校正的压缩数据进行分析,实现模型传递。本方法能够扣除不同仪器之间的大部分差异,大幅度改善分析精度。传递后模型分析精度与源机模型稳健性紧密相关。如果源机模型稳健性强,则能够实现不同仪器之间的共享。本方法能够实现源机的0#轻柴十六烷值、凝点、馏出温度;-10#轻柴十六烷值、凝点以及-10#军柴凝点和馏出温度共10个模型在5台仪器之间共享,简化了建模的成本。与传统的PDS相比,WT-PDS方法具有传递和建模变量少、速度快、光谱校正性能高等优点,而其模型分析精度与传统PDS基本一致。     

校正集样品特性对鼠药中毒鼠强近红外光谱分析的影响

根据市售鼠药样品成分各异且相对复杂,建立6种不同成分体系和9个不同样本容量的校正集,运用小波变换压缩鼠药的近红外透射光谱数据,结合BP反向神经网络算法对压缩的数据进行建模,考察校正集样品特性对模型预测能力的影响。试验结果表明:采用BP神经网络算法建立定量模型时,只要校正集样品中包含了与预测样品性质相似的样本,就能准确地对复杂样品进行近红外定量分析。当校正集容量分别为72和84时,模型预测结果趋于平稳。当校正集数量为96时,模型的最大相关系数为0.959 8,预测最小标准差和平均相对误差分别为1.893%和1.92%。     

A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.     

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优