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。     

主成分-人工神经网络在近红外光谱定量分析中的应用

近红外光谱的主成分由非线性迭代偏最小二乘法（ＮＩＰＡＬＳ）求出。主成分作标准化处理后,作为Ｂ－Ｐ神经网络的输入结点进行非线性迭代。该法的优点是,充分利用了全光谱的数据,得到消除噪声后的最佳主成分,能建立非线性模型,Ｂ－Ｐ神经网络迭代时间显著缩短。用该法对大麦中的淀粉含量进行了定量分析研究。结果为：校准和预测的相关系数分别为０．９８１和０．９５３,校准和预测的相对标准偏差分别为１．７０％和２．４８％。     

段式正交信号校正方法及在小麦近红外光谱数据分析中的应用

针对光谱数据峰宽、局部效应显著、含有噪音、变量个数多及彼此间常存在严重的复共线性等问题,改进和设计一种光谱数据局部校正方法:基于窗口平滑的段式正交信号校正方法,并将之结合偏最小二乘回归,以实现光谱数据的预处理及定量分析。通过NIPALS算法初始化将滤去的正交成分,以近邻分段方式进行逐个波长点的正交信号校正。而后将去噪后的光谱矩阵作为新的自变量阵,通过偏最小二乘回归构建其与性质参变量间的校正模型。通过小麦近红外漫反射光谱数据的应用实验结果表明,本方法正交成分估计稳定,去噪明显,模型的预报性能优于其它方法,PLS成分数减少,模型更加简洁。     

多模型共识偏最小二乘法用于近红外光谱定量分析

将多模型共识偏最小二乘法用于近红外光谱定量分析。利用随机抽取的训练子集建立一系列偏最小二乘模型,选取其中性能较好的部分模型作为成员模型,用这些成员模型来预测未知样品。将该方法用于一组生物样本的近红外光谱与样品中人血清白蛋白、γ-球蛋白以及葡萄糖含量之间的建模研究,并与单模型偏最小二乘法了进行比较。结果 PLS对独立测试集中三种组分进行50次重复预测的平均RMSEP分别为0.1066,0.0853和0.1338,RMSEP的标准偏差分别为0.0174,0.0144和0.0416;而本方法重复预测的平均RMSEP分别为0.0715,0.0750和0.0781,RMSEP的标准偏差分别为0.0033,0.2729×10-4和0.0025。     

近红外光谱-径向基神经网络在异烟肼片无损定量分析中的应用

应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。     

近红外光谱技术用于花生油中棕榈油含量的测定

本文采用近红外光谱技术采集样品的近红外光谱数据,光谱经一阶求导后,采用偏最小二乘法(PLS)建立花生油中棕榈油含量的定标模型,并用交互验证法对模型进行了验证。模型相关系数为0.9963,校正均方根(RMSEC)为0.937。该模型应用于实际样品的检测,结果令人满意。     

重整汽油近红外光谱的稳健偏最小二乘解析

近红外光谱(ＮＩＲ)光谱复杂,组分间光谱重叠严重,目前,多元线性回归(ＭｕｌｔｉｐｌｅＬｉｎｅａｒＲｅ ｇｒｅｓｓｉｏｎ,ＭＬＲ)和偏最小二乘法(ＰａｒｔｉａｌＬｅａｓｔ ｓｑｕａｒｅｓ,ＰＬＳ)是近红外光谱分析中使用最多和效果较好的方法[1]。稳健偏最小二乘(ＲｏｂｕｓｔＰａｒｔｉａｌＬｅａｓｔ Ｓｑｕａｒｅｓ,ＲＰＬＳ)是由稳健统计学构造的具有稳健性能的多元校正方法。当化学测量中引入随机异常点或误差的内在分布偏离正态分布时,它仍能给予接近最优性能的校正,确保分析结果的准确性,是消除奇异点的非常有效的方法[2-4],…     

应用近红外光谱和偏最小二乘回归法预测玉米中淀粉含量

以普通玉米籽粒为试验材料,应用偏最小二乘回归法建立了基于近红外光谱数据的测定玉米籽粒中淀粉含量的校正模型。校正模型的校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别#30．31％、0．42％和0．29％,校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0．9255和0．9310,表明所建立的校正模型具有较高的预测精度和较好的推广性,为玉米籽粒中淀粉含量的快速、无损测定提供了新的途径：     

近红外光谱法快速测定涂料固化剂中游离甲苯二异氰酸酯含量

用气相色谱分析值为参照,采用近红外透射光谱（NIR）技术采集相应样品的NIR光谱,研究了涂料固化剂中游离甲苯二异氰酸酯（TDI）含量的快速测定分析方法。 并从120个固化剂样品中挑选出109个代表性的样品建模,选择7320~7250 cm-1和8485~8370 cm-1波段区间,用偏最小二乘法(PLS)和完全交互验证方式建立TDI含量的预测模型。 结果表明,固化剂中游离甲苯二异氰酸酯含量和近红外光谱之间存在较好的相关性,其预测模型的校正集均方差（RMSEC）为0.0815,验证集均方差（RMSEP）为0.0715,模型性能良好。 近红外光谱法可快速准确测定游离甲苯二异氰酸酯（TDI）含量,用于固化剂样品快速分析。     

A Robust Near-Infrared Calibration Model for the Determination of Chlorophyll Concentration in Tree Leaves with a Calibration Transfer Method

A method based on piecewise direct standardization was developed to directly predict leaf chlorophyll concentrations by correction of near-infrared spectra to construct a robust calibration model. Chinar, camphor, and gingko leaves collected from two growth intervals were evaluated. Spectral pretreatment methods and wavelength selection were investigated. The first derivative combined with stability competitive adaptive reweighted sampling before piecewise direct standardization provided the best performance. Under the optimized parameters, the root mean square error of prediction was significantly reduced by using piecewise direct standardization. This study demonstrates that the calibration model may be used to rapidly characterize chlorophyll concentrations across species and growth intervals.     

Characterization of a Pesticide Formulation by Medium Wave Near-Infrared Spectroscopy with Uninformative Variable Elimination and Successive Projections Algorithm

Near-infrared (NIR) spectroscopy, a rapid and nondestructive analytical method, has been widely used in many fields. In this paper, medium wave near-infrared (MWNIR) was used to determine the active ingredient of a deltamethrin formulation. An uninformative variable elimination-successive projections algorithm (UVE-SPA) was employed to investigate effective variables and was compared with UVE, SPA, and full-spectrum partial least squares (PLS) regression. The results indicate that MWNIR was able to determine the pesticide active ingredient and that UVE-SPA was an efficient variable selection approach by eliminating spectral redundancy and colinearity. The developed method is a meaningful exploration in the application of near-infrared spectroscopy and provides a valuable reference on pesticide quality control.     

模型诊断用于近红外光谱建模校正集中奇异样本的识别

由于校正集样本的质量决定校正模型的质量,校正集中奇异样本的检测在多元校正建模中具有非常重要的意义.本研究建立了一种用于近红外光谱多元校正建模时校正集中奇异样本的检测方法.本方法基于奇异样本的定义和偏最小二乘方法的原理,通过考察每个校正集样本在模型的每个因子(或主成分)中对模型的贡献,将与多数样本表现不同的样本识别为奇异样本.采用218个橘汁样本构成的近红外光谱数据进行了分析,结果表明,校正集中存在6个奇异样本,扣除奇异样本后,校正集的交叉验证均方根误差由16.870减小为4.809,预测集的均方根误差从3.688减小为3.332.     

PDS用于不同温度下的近红外光谱模型传递研究

采用合适的计算方法可降低测定环境对近红外光谱校正模型稳健性的影响。该文以喷气燃料为研究对象,考察了分段直接校正算法对所建模型预测结果的影响,通过选择转移样品数及窗口宽度,建立了最佳的校正模型和光谱转移参数。结果表明,在20℃下建立近红外光谱校正模型,直接预测30℃下喷气燃料的密度,预测集样品均方根误差（RMSEP）为0.2031,而30℃近红外光谱采用分段直接校正算法模型转移后,预测集样品均方根误差（RMSEP）降低为0.1354,预测结果得到明显改善,有效地解决了样品温度对近红外光谱分析结果的影响。     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

近红外与中红外光谱技术在土壤分析中的应用

传统的土壤分析方法周期长、成本高,不能在短时间内获取所需的土壤信息,如何快速、准确地获取土壤信息成为农业现代化的必然要求。漫反射光谱技术以其快速、廉价、非破坏与无污染等特点,已成为环境研究中获取相关土壤信息的重要技术手段,其中的近红外(NIR,780~2 500 nm)和中红外(MIR,2 500~25 000 nm)光谱技术预测土壤理化特性逐渐成为国内外学者研究的重点领域。文章介绍了近红外和中红外光谱技术的基本原理和分析方法,综述了该技术在土壤分析方面的具体应用,并对此提出了一些观点和展望。     

基于正交信号校正的秸秆青贮饲料粗蛋白近红外分析模型传递方法

探讨了基于不同数据预处理方法的正交信号校正在秸杆饲料近红外光谱模型传递中的应用.以141个秸杆青贮饲料样品为研究对象,以其粗蛋白含量为目标参数,研究了基于无处理、局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正,在源仪器(SPECTRUM ONE NTS)和目标仪器1(ANTA-RIS)与目标仪器2(FOSS 6500)之间的模型传递效果.实验表明:对于两台傅里叶变换型近红外光谱仪,采用局部中心化、全局中心化和Z-score标准化预处理方法的正交信号校正均可成功实现模型传递,其中局部中心化和全局中心化法的作用效果基本一致,且优于Z-score标准化法.对于傅立叶变换和光栅型近红外光谱仪,全局中心化的作用效果明显优于其它3组处理效果,且只有全局中心化预处理的正交信号校正传递后的模型可用于实际预测.     

近红外光谱技术应用于摇头丸中MDMA、MA无损定量分析的研究

提出了用近红外漫反射光谱快速无损测定摇头丸中亚甲二氧基甲基苯丙胺(MDMA)、甲基苯丙胺(MA)含量的新方法。收集含MDMA摇头丸56份和含MA摇头丸58份,采用GC-MS确定其中MDMA和MA质量分数分别为0.64%~53.83%,0.37%~5.81%。在12000~4000 cm-1扫描样品,以交叉验证误差均方根(RMSECV)为指标,通过筛选,对各组分确定了用于建模的最优近红外波段和光谱预处理方法,采用偏最小二乘算法建立了近红外光谱与这2组分GC-MS分析值之间的校正模型,并以此分别预测21个样本。δ代表预测样本NIR值/GC-MS值,MDMA和MA在裸片和塑料包装中δ值的均值为99%、101%、100%、101%,RSD分别为7.33%、20.3%、4.52%、12.3%。该方法可对摇头丸裸片中MDMA和MA进行快速无损分析,结果可靠,为刑事案件中毒品成分的测定提供了一种新的分析手段。     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.