近红外光谱技术检测烟叶中的氨基酸

研究应用傅里叶变换近红外光谱法快速测定烟叶中氨基酸含量的可行性,使用偏最小二乘法(PLS)为建模方法,选择3800~8000 cm-1谱段,采用二阶导数和Norris Derivative滤波法进行光谱预处理,建立了烟叶中氨基酸含量的近红外预测模型。采用留一(leave-one-out)交叉验证法进行建模,并以校正集样品的交叉验证相关系数(R)和均方差(RMSECV)为指标优化光谱预处理方法和模型参数,确定最佳预测模型。将近红外光谱技术与常规标准检测方法相比较,结果表明,近红外光谱技术可以较为准确的测定烟叶中氨基酸的含量。     

光谱预处理对近红外光谱快速检测黄酒酒精度的影响

本文以黄酒近红外透射光谱为研究对象,探讨光谱预处理方法对黄酒酒精度快速定量检测模型的影响,对比分析了采用平滑、一阶导数、二阶导数、多元散射校正、标准正态变换结合去势等五种传统方法和小波变换、傅里叶变换、正交信号校正等三种新方法预处理后光谱的偏最小二乘建模效果.结果表明,正交信号校正处理后,模型交叉验证R和RMSECV分...     

近红外光谱法测定茶多酚中总儿茶素含量

以高效液相色谱(HPLC)分析结果为参考值,建立了快速测量茶多酚中总儿茶素含量的近红外光谱定标模型.将48份茶多酚样品组成定标样品集,在1000～2500nm(4000～10000cm-1)的近红外漫反射光谱为定标波长范围内,光谱经一阶导数(Firstderivative)、二阶导数(Secondderivative)、标准归一化(Stan-dardnormalvariate,SNV)和多元散射校正(multiplicativesignalcorrection,MSC)处理后结合偏最小二乘回归(PLS)定标.经内部交叉验证表明,光谱经SNV处理后建模结果最佳.模型的相关系数Corr.Coeff=0.997,校正均方根RMSEC=1.71%.比较了经典最小二乘法(CLS)、偏最小二乘法(PLS)和主成分回归(PCR)等方法建模结果,以偏最小二乘回归建模效果最好.     

近红外透射光谱聚类分析快速鉴别食用油种类

以8种食用油纯油的43个样品为对象,研究了近红外透射光谱结合聚类分析法快速鉴别食用油种类的可行性.采集样品在12 500~4 000 cm-1范围内的傅立叶变换近红外透射光谱,利用光谱模式识别法中的聚类分析法对图谱进行定性分类鉴别.实验证明,光谱经二阶导数预处理后,最短距离法、最长距离法和方差平方和法均可准确无误地将食用油样品分为8类,判别模型对预测集样品的准确率达到100%.研究表明,近红外透射光谱结合聚类分析法可以为快速无损鉴别食用油种类提供一种准确可靠的方法.     

基于近红外光谱建立大黄鱼新鲜度预测模型

目的 通过近红外光谱技术对不同贮藏时间下冰鲜大黄鱼的鲜度进行评价。方法 以菌落总数为鲜度评价指标,基于均值中心化、标准正态变量变换、趋近归一化法（Normalization by Closure, Ncl）、多元散射校正、一阶导数和二阶导数等预处理方法,运用偏最小二乘法（Partial Least Squares, PLS）建模,比较所建模型的定标集与验证集间的相关系数和标准偏差,构建大黄鱼冰藏期间菌落总数的定量模型,以期快速预测其新鲜度。结果 Ncl比其它预处理方法可以更好地消除光谱噪音,提高模型的预测能力。经Ncl光谱预处理,利用PLS建模,可达到最佳的建模效果,其定标集相关系数为0.9095,校正标准偏差相关系数为0.5872,验证集相关系数为0.8858,预测标准偏差为0.6615。模型相关系数>0.9;结论 表明该模型预测精度较好,在大黄鱼新鲜度检测和品质评价方面应用前景良好。     

近红外漫反射光谱法快速检测火药吸收药混合液中黑索今组分含量

研究了采用近红外( NIR)漫反射光谱技术快速检测火药吸收药混合液中黑索今( RDX)组分含量的方法。将装有混合液样品的烧杯置于光谱仪主机光源窗口上方,直接采集样品光谱图。通过分析纯RDX和样本的近红外光谱,确定908~945 nm、1094~1253 nm和1577~1678 nm作为建模谱区。通过比较不同的光谱预处理办法的效果,选择标准正态变换( SNV )＋一阶导数＋谱线平滑对原始光谱进行预处理。采用偏最小二乘方法(PLS)对RDX组分建立了定量线性校正模型。结果表明：模型的交叉验证决定系数(R2cv)为0.9879,交叉验证均方根误差(RMSECV)为0.2420,预测均方根误差(RMSEP)为0.2127,预测结果的平均相对误差为0.5661％,25 s内完成样品分析。该方法可给改性双基火药的连续自动化生产提供技术支持。     

近红外光谱结合神经网络判别茶鲜叶产地

为快速、无损的判别鲜叶产地,维护恩施玉露的地理标志产品属性,采集恩施市芭蕉乡、白果乡和咸丰县茶鲜叶近红外光谱,经光谱预处理后,对校正集66个样品光谱数据进行主成分分析,然后建立BP神经网络预测模型,对验证集鲜叶样品的产地进行了预测,建立了8(输入节点)-4(隐含层节点)-1(输出节点)三层网络模型,验证集样品判别准确率为100%.近红外光谱技术结合神经网络能够快速、准确地判别茶鲜叶产地.     

近红外漫反射光谱结合神经网络检测小麦蛋白质含量

为了实现对小麦蛋白质含量的快速检测,提出了基于近红外光谱结合神经网络的小麦蛋白质检测方法.以160个小麦样品为对象,采集其近红外漫反射光谱,并以国标法分析小麦样品蛋白质含量,作为参考值.样品随机分成预测样品集和定标样品集,其光谱经标准归一化、去趋势等预处理后,采用BP神经网络和偏最小二乘法分别建立蛋白质含量定标模型.BP神经网络模型的预测相关系数和预测均方根误差分别为0.98和0.270 4%.而偏最小二乘法模型的预测相关性系数和预测均方根误差分别为0.98和0.303 8%.结果表明,两种方法建立的模型都具有较好的预测相关性和预测效果,其中BP神经网络模型优于偏最小二乘法模型.用非线性BP神经网络结合相应算法建立模型检测小麦蛋白质含量的定标模型可以提高检测准确性.     

绿茶汤中茶多酚近红外定量分析的光程选择

研究了光程对近红外定量分析绿茶汤中茶多酚的影响.以不同光程(1 mm,2 mm,5 mm)的样品池采集50个绿茶汤样品的近红外透射光谱.采用偏最小二乘法(PLS)建立茶汤中茶多酚的定量分析模型,并验证模型的准确度和精密度.结果表明,选择1 mm光程光谱建立的茶多酚定量分析模型最优,模型的校正集相关系数R2和内部交叉验证均方根RMSECV分别为91.08%和0.009 3%;检验集相关系数R2和预测标准差SEP为91.53%和0.008 5%;实际值与预测值配对t检验值为0.224 9,差异不显著.10次重复测量相对标准偏差RSD为0.008 7%,表明方法检测重复性好.     

人血清中血糖的近红外光谱快速检测

应用傅利叶变换近红外光谱透射技术结合偏最小二乘法 ( PLS) ,快速定量分析了人血清中血糖含量 .利用内部交叉验证和自动优化功能对预测模型进行了优化 ,确定了最优建模参数 .模型对人血清中葡萄糖定标样品集的实测含量与预测含量的相关系数 r=0 .91 48,内部校正均方差 RMSECV=0 .487mmol/L.     

单基发射药生产过程中驱水棉水分含量的快速检测方法?

由于驱水棉的水分含量对单基发射药成型工艺有着较大的影响,采用近红外光谱分析技术对驱水棉水分含量进行快速检测。通过对比分析纯水、硝化棉(NC)、乙醇以及驱水棉的光谱图,确定了水分含量检测建模区域为5 015.6~5 224.8 cm~(-1)和6 525.9~7 008.7 cm~(-1)。比较不同光谱预处理方法,发现标准正态变量校正(SNV)、一阶导数和平滑的组合方法对驱水棉的光谱进行预处理效果最好。采用偏最小二乘法对水分含量建立定量校正模型,并对预测集样本进行预测和对模型进行重复性验证。试验结果表明:校正集和交互验证相关系数R2分别为0.995 4和0.994 4,预测均方根误差RMSEP值为0.039 0,对预测集的药料样本预测的平均相对误差为0.997%,模型的重复性良好,检测时间小于20 s,能满足单基发射药连续自动化生产工艺的要求。     

Raman spectra of biological samples: A study of preprocessing methods

In this study preprocessing of Raman spectra of different biological samples has been studied, and their effect on the ability to extract robust and quantitative information has been evaluated. Four data sets of Raman spectra were chosen in order to cover different aspects of biological Raman spectra, and the samples constituted salmon oils, juice samples, salmon meat, and mixtures of fat, protein, and water. A range of frequently used preprocessing methods, as well as combinations of different methods, was evaluated. Different aspects of regression results obtained from partial least squares regression (PLSR) were used as indicators for comparing the effect of different preprocessing methods. The results, as expected, suggest that baseline correction methods should be performed in advance of normalization methods. By performing total intensity normalization after adequate baseline correction, robust calibration models were obtained for all data sets. Combination methods like standard normal variate (SNV), multiplicative signal correction (MSC), and extended multiplicative signal correction (EMSC) in their basic form were not able to handle the baseline features present in several of the data sets, and these methods thus provide no additional benefits compared to the approach of baseline correction in advance of total intensity normalization. EMSC provides additional possibilities that require further investigation.     

Noninvasive in vivo measurement of venous blood pH during exercise using near-infrared reflectance spectroscopy

Blood pH is an important indicator of anaerobic metabolism in exercising muscle. This paper demonstrates multivariate calibration techniques that can be used to produce a general pH model that can be applied to spectra from any new subject without significant prediction error. Tissue spectra (725 approximately 880 nm) were acquired through the skin overlying the flexor digitorum profundus muscle on the forearms of eight healthy subjects during repetitive hand-grip exercise and referenced to the pH of venous blood drawn from a catheter placed in a vein close to the muscle. Calibration models were developed using multi-subject partial least squares (PLS) and validated using subject-out cross-validation after the subject-to-subject spectral variations were corrected by mathematical preprocessing methods. A combination of standard normal variate (SNV) scaling and principal component analysis loading correction (PCALC) successfully removed most of the subject-to-subject variations and provided the most accurate prediction results.     

预浸料生产中智能控制系统的应用

用近红外光谱技术对无碱布/酚醛预浸料的树脂含量、可溶树脂含量和挥发分含量进行在线检测,通过偏最小二乘方法分别建立标准模型,选择光谱预处理方法和PLS的因子数.用近红外方法和标准方法对未知样品进行分析,通过t检验结果显示两种方法没有显著性的差别,利用该方法可以同时预测三项指标,1分钟之内就可以分析一个样品,没有破坏性.如果质量指标不合格,通过自动控制系统发出指令,及时调节工艺参数.研究表明近红外光谱方法能够十分有效和准确对分析预浸料质量.     

Evaluation of physical properties of human hair by diffuse reflectance near-infrared spectroscopy

The objective of the present study is to develop a novel nondestructive, simple, and quick method to evaluate the friction, twist, and gloss of human hair based on near-infrared diffuse reflectance (NIR-DR) spectroscopy and chemometrics. NIR-DR spectra were measured for human hair, which was collected from eleven Japanese women (age 5-44 years), by use of an optical fiber probe. Partial least squares (PLS) regression has been applied to the NIR-DR spectra of human hair after mean centering (MC), standard normal variate (SNV), and first derivative (1d) or second derivative (2d) analysis to develop calibration models that predict the friction, twist, and gloss of human hair. We identified the most suitable wavenumber region for the evaluation of each physical property. Correlation coefficients and standard errors of calibration of the PLS calibration models for the friction, twist, and gloss of hair were calculated to be 0.96 and 0.023, 0.81 and 3.27, and 0.90 and 0.36, respectively. Thus, the calibration models have high accuracy.     

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.