近红外漫反射光谱法非破坏分析颠茄粉末药品质量

本文应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对颠茄粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。     

近红外光谱法非破坏分析生理盐水

本文将近红外光谱法与偏最小二乘法(PLS)结合,对生理盐水中的NaCL浓度进行快速分析,建立了最佳数学校正模型.讨论了光谱的预处理方法和主成分数对PLS定量预测生理盐水中NaCL浓度的影响,并对预测集样品含量进行预测,结果令人满意.     

美国一项最新调查研究发现,如果儿童在3岁以前由于手术使用麻醉药的次数达到两次或者更多那么他们日后出现学习障碍的风险可能会比正常儿童高。

本文应用偏最小二乘法（PLS）同近红外漫反射光谱法结合,对颠茄粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。     

PLS-近红外漫反射光谱法对盐酸氟桂利嗪粉末药品非破坏定量分析

本文应用偏最小二乘法（PLS）与近红外漫反射光谱法相结合,对盐酸氟桂利嗪粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。     

短波近红外光谱法非破坏分析青霉素V钾粉末药品

本文应用偏最小二乘法(PLS)同近红外漫反射光谱法结合,对青霉素V钾粉末药品进行无损非破坏定量分析,建立了最佳的数学校正模型.讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果.     

偏最小二乘法-近红外漫反射光谱法快速分析西米替丁粉末药品

研究了用偏最小二乘法(PLS)同近红外漫反射光谱结合,对西米替丁粉末药品进行快速定量分析的方法,建立了最佳的数学校正模型.讨论了波长间隔和主成分数对PLS定量预测能力的影响,预测了未知样品,结果令人满意.     

PLS-近红外漫反射光谱法对氢氧化铝粉末药品非破坏定量分析

本文采用近红外漫反射光谱法对氢氧化铝粉末药品中主要成分氢氧化铝进行快速、无损定量分析。采用偏最小二乘法（PLS）建立近红外光谱信息与待测组分含量间的最佳数学校正模型。讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果。     

短波近红外漫反射光谱法对氨苄西林粉末药品的非破坏定量分析

本文采用短波近红外漫反射光谱法对氨苄西林粉末药品中主要成分氨苄西林进行快速、无损非破坏快速定量分析.采用偏最小二乘法(PLS)建立近红外光谱信息与待测组分含量间的最佳数学校正模型.讨论了光谱的预处理方法和主成分数对PLS定量预测能力的影响,并对预测集样品含量进行预测,得到了较为满意的结果.     

利用聚类分析和偏最小二乘法提高NIR多组分分析精度

提出了一种聚类分析和偏最小二乘法(PLS)结合的新的近红外(NIR)多组分分析法.此方法可以"由粗及精"地预测组分浓度.首先利用聚类分析判别测试样本大致的浓度范围,然后利用此浓度范围附近的训练样本建立PLS校正模型,预测样本的组分浓度.和传统的PLS比较,改善了模型的适应性,显著地提高了预测精度.实验及数据处理结果证明了此方法的有效性.     

应用近红外漫反射光谱快速测定土壤锌含量

采用近红外漫反射光谱和偏最小二乘法(PLS)建立了土壤锌快速分析的定量模型,并进行了波段优选。首先,基于单波长模型预测效果将全体样品划分为定标集和预测集;然后,采用多元散射校正(MSC)和Savitzky-Golay(SG)平滑方法对光谱进行预处理。选取全谱400~2500nm,400~1100nm,1100~1900nm,1900~2500nm,580~900nm等5个波段,每个波段分别采用原谱、一阶导数谱、二阶导数谱,共建立了15个定标模型。同时调整SG平滑点数和PLS因子数,每个模型分别进行PLS数值实验,按照预测效果进行优选。结果显示,采用1900~2500nm波段一阶导数谱的模型效果最好,预测相关系数(RP)、RMSEP、RRMSEP分别为0.806,31.0mg/kg和19.96%。这些结果表明,1900~2500nm波段可以代替全谱波段得到更好的预测效果,可为设计专用土壤近红外光谱仪提供依据。     

特征谱区筛选在近红外光谱检测茶叶游离氨基酸含量中的应用研究

在近红外光谱快速检测茶叶游离氨基酸含量过程中,为了提高检测的精度和稳定性,研究利用特征谱区结合偏最小二乘法建立预测模型。研究分别尝试联合区间偏最小二乘法和遗传偏最小二乘法等特征谱区筛选方法,通过交互验证法确定偏最小二乘模型的主成分因子数和筛选区间,以预测均方根误差RMSEP和相关系数R作为模型的评价指标。试验结果表明：两种方法建立模型的预测能力都好于传统PLS模型;利用联合区间偏最小二乘法建立的预测模型最佳,预测时的相关系数(R)和预测均方根误差(RMSEP)分别为0.9542和0.2560。研究结果表明,近红外光谱结合特征谱区筛选方法可以快速准确地测定茶叶中游离氨基酸含量。     

近红外光谱结合偏最小二乘法对甲氧苄啶粉末药品的定量分析

利用漫反射法获得甲氧苄啶粉末药品的近红外光谱（波长范围1100-2500nm）,采用化学计量学中的偏最小二乘法（PLS）及不同的光谱预处理方法（标准归一化（SNV）、一阶导数和二阶导数）对光谱进行信息提取和分析,对甲氧苄啶粉末药品进行了无损定量分析,以样品中甲氧苄啶为活性成分建立了最佳的数学校正模型。同时讨论了主成分数对PLS模型定量预测能力的影响,并对所得结果做出了比较。     

多元散射校正对近红外光谱分析定标模型的影响

采用近红外漫反射光谱分析技术,用傅里叶变换型光谱仪对50个烟叶样品采集吸收光谱,采用常用的多元散射校正(MSC)对光谱预处理,通过主成分分析、相关谱等方法比较分析了预处理对光谱分析的影响,用偏最小二乘(PLS)回归法建立近红外光谱与总糖含量的定标模型,用Leave-One-Out的交叉检验(Cross-Validation)检验定标模型,结果PLS因子数由MSC校正前的5降为校正后的3,RMSECV值仅由0.884 1%降为0.85%。实验证明:对光谱进行MSC预处理能有效减少模型的最佳因子数,简化数学模型,使模型更稳定,更便于传递,但并不能显著减小最优定标模型的预测标准差,即不能显著提高模型的预测能力。     

小型近红外玉米蛋白质成分分析 仪器设计的波段选择

采用傅里叶变换近红外漫反射光谱技术和偏最小二乘法(PLS)建立了玉米蛋白质含量的定标模型。按照预测效果优选光谱波段,为设计小型近红外玉米蛋白质成分分析仪器提供依据。采用多元散射校正方法对光谱进行预处理,然后利用Savitzky-Golay平滑法对原谱、一阶导数谱和二阶导数谱进行平滑处理。选取全谱、合频、一倍频、二倍频和蛋白质基团等5个波段,每个波段分别采用原光谱、一阶导数谱、二阶导数谱,共建立15个定标模型。同时调整Savitzky-Golay平滑点数和PLS因子数,通过多次PLS数值实验比较,按照预测效果确定每个模型的最优平滑点数、因子数,再从中选优。结果表明,采用一阶导数谱的一倍频波段(7 000~5 500 cm^－1)的定标效果最好,模型的预测相关系数、预测均方根偏差、相对预测均方根偏差分别为0.945,0.357,3.340%。一倍频波段可以代替全谱波段并得到更好的定标效果。     

基于拉曼光谱的三组分食用调和油快速定量检测

应用激光拉曼光谱技术结合化学计量学方法实现了三组分食用调和油中菜籽油、花生油和芝麻油的快速定量检测。分别采用标准正态变量变换(SNV)+去趋势(de-trending)算法和正交信号校正(OSC)算法对600~3 000cm-1波段的原始拉曼光谱进行预处理。建立了基于非线性支持向量机(SVM)和线性偏最小二乘(PLS)回归算法的定量分析模型,并采用19个预测集通过外部交叉验证法对模型进行验证。实验结果显示:对含有菜籽油、花生油和芝麻油的三组分食用调和油,以OSC预处理后建立的线性PLS模型预测效果最好,其验证集决定系数R2p分别为0.990 4,0.965 8,0.977 1,均方根误差(RMSEP)分别为0.018 8,0.037 9,0.026 2。研究结果表明,利用激光拉曼光谱结合化学计量学方法快速定量检测三组分食用调和油中菜籽油、花生油和芝麻油的含量具有可行性,并获得了较高的预测精度。     

Developing a Vis/NIR spectroscopic system for fast and non-destructive pesticide residue monitoring in agricultural product

In this research, an optical system based on fibre optic Vis/NIR spectroscopy combined with chemometrics methods and software as a graphical user interface (GUI) was developed and presented for fast and non-destructive detection and determination of pesticide residues in agricultural products (a case study on diazinon in intact cucumbers). Vis/NIR spectra of cucumber samples without and with different concentrations of diazinon residue were analyzed at the range of 450–1000 nm. Partial least squares (PLS) regression models were developed based on chemical reference measurements and the spectral information of the samples after performing different pre-processing methods. Moreover, partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the maximum residue limits (MRL) as safe and unsafe samples, respectively. Finally, user-friendly software as a GUI was created based on the best PLS and PLS-DA models developed for prediction of diazinon contents in the samples and for classification of intact cucumbers by the absence/presence of diazinon residues, respectively. Evaluation of the system and software designed based on the best developed PLS and PLS-DA models indicated good performance for measuring and detection of diazinon residue in cucumbers. It was concluded that the designed system and software based on Vis/NIR spectroscopy combined with chemometrics methods can be utilized for fast and non-destructive safety control of intact cucumbers by the absence/presence of diazinon residues. It can also be generalized for detection of other pesticide residues in agricultural products if developing their appropriate models is feasible.     

Three-way PLS regression and dual energy gamma densitometry for prediction of total volume fractions and enhanced flow regime identification in multiphase flow

Dual energy gamma densitometry and 3-way partial least squares regression were applied to quantify the total volume fractions and improve flow regime identification in multiphase flow. Multiphase flow experiments were carried out with formation water, crude oil and gas from different North Sea gas fields in Statoil׳s High Pressure Multiphase Flow Loop in Porsgrunn, Norway. Four different flow regimes were investigated (stratified wavy, slug, dispersed and annular). A traversable dual energy gamma densitometer was used to measure the fluid densities in the pipe. Partial least squares regression was previously applied to identify multiphase flow regimes and quantify volume fractions of gas, oil and water. That study showed promising results for flow regime identification but the predictions of the total volume fractions were not acceptable. In this study a new method combining gamma densitometry and 3-way partial least squares regression was applied in order to improve the quantitative estimation of the total volume fractions gained in the previous study. The proposed 3-way regression approach allows prediction of the total volume fractions directly using one model instead of multiple models which was reported earlier. The improved quantification of the volume fractions of gas, oil and water was used to improve the flow regime identification plots and increase the interpretability.The new 3-way prediction results for the volume fractions were significantly better than what was found earlier based on individual PLS models. The root mean square error of prediction for gas, oil and water from the 3-way PLS models were 4.1 %, 4.3 % and 4.6% respectively. All models reported were validated based on independent data (test set validation).