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。     

近红外光谱技术定量分析连作滁菊土壤中的阿魏酸含量

应用近红外光谱(NIRS)技术定量分析连作滁菊土壤样品中阿魏酸的含量.通过标准杠杆值、学生残差和马氏距离判断异常光谱,经二阶导数和Norris平滑滤噪预处理后,在6000~4000 cm-1范围,最佳因子数为7,采用偏最小二乘法(PLS)构建数学模型.结果表明,模型校正集和验证集与高效液相色谱仪(HPLC)测定的参考值之间均呈现良好相关关系,校正相关系数Rc为0.9914,交叉验证相关系数Rcv为0.9935,校正集误差均方根(RMSEC)为0.484,预测误差均方根(RMSEP)为0.539,交叉验证误差均方根(RMSECV)为0.615.研究结果表明,NIRS分析技术能够实现连作土壤中阿魏酸的快速检测,结果准确可靠.     

近红外光谱与组合的间隔偏最小二乘法测定清开灵四混液中总氮和栀子苷的含量

应用近红外光谱分析技术结合化学计量学方法, 建立了中药清开灵注射液中间体总氮和栀子苷含量测定的新方法. 首先采用Kernard-Stone法对训练集样本和预测集样品进行分类, 然后应用组合的间隔偏最小二乘法(Synergy interval partial least squares, siPLS)对所得近红外透射光谱进行有效谱段范围的选择以及二者定量校正模型的建立, 并对光谱预处理方法进行了详细的讨论. 所建立的总氮和栀子苷校正模型的预测相关系数(R)分别为0.999和0.708; 交叉验证误差均方根(RMSECV)均为0.023; 预测误差均方根(RMSEP)分别为0.074和0.159; 预测结果表明, 本实验所建方法快速、无损且可靠, 可推广并应用于中药注射液中间体的在线质量控制.     

近红外光谱结合膜富集技术测定饮料中微量邻苯二甲酸二异辛酯的含量

提出了一种基于在线膜富集的近红外漫反射光谱技术,对饮料中的微量塑化剂邻苯二甲酸二异辛酯(DEHP)进行快速检测。采用聚醚砜膜对饮料中的DEHP进行富集,将富集DEHP的膜直接进行近红外漫反射检测。参考DEHP的透射近红外光谱,对波数进行选择,以4 420～4 060、4 700～4 540、6 040～5 600cm-1作为建模的波数区间。通过比较原始光谱、多元散射校正、一阶求导、二阶求导及其组合,考察了光谱预处理方法对模型的影响,用去一交互验证法建立了偏最小二乘(PLS)模型,并用所建立的校正模型对校正集样品进行了预测。结果表明,在选定的波数区间,当用一阶求导对校正集光谱进行预处理时,所建立的模型对校正集的预测效果最佳,在隐变量数为7时,对校正集所有样品的校正均方根误差(RMSEC)为0.188 7mg/L。用此模型对预测集样品进行预测时,DEHP的质量浓度在0.5～5.0 mg/L范围内,预测均方根误差(RMSEP)为0.232 4 mg/L,平均相对预测误差为6.29%。     

基于粒子群算法的波长选择方法用于苹果酸度的近红外光谱分析

采用便携式近红外光谱分析仪,对苹果样品进行扫描获得光谱数据,运用偏最小二乘法结合基于粒子群算法的波长选择方法对苹果试验数据进行多元统计分析,建立数学模型,利用该模型对苹果酸度进行了预测。对于基于粒子群算法和全谱偏最小二乘方法,校正集样品的酸度预测值和实测值之间的相关系数分别为0.9880和0.9553,校正均方根误差分别为0.0197和0.0388;预测集样品的酸度预测值和实测值之间的相关系数分别为0.9833和0.9596,预测均方根误差分别为0.0193和0.0304。与全谱偏最小二乘法相比,基于粒子群算法的偏最小二乘法,不仅较大地减少波长变量而降低计算量,而且也较大地提高了模型性能而增强了模型预测的准确性。该方法可建立较好的定量分析模型,能广泛应用于现场或野外苹果酸度的快速分析。     

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。     

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。     

白酒基酒中酮类物质的近红外光谱检测方法

应用近红外光谱技术建立了白酒基酒中2,3-丁二酮和3-羟基-2-丁酮的快速检测模型。从洛阳杜康酒厂选取182个白酒基酒样品为材料,运用气相色谱法测得两种物质的化学值,同时采集其在12 000~4 000 cm-1范围内的光谱数据,采用偏最小二乘法(PLS)结合内部交叉验证建立校正模型。通过对比不同光谱预处理下PLS模型效果对其进行优化,确定2,3-丁二酮和3-羟基-2 丁酮的最佳预处理方法分别为一阶导数+多元散射校正和二阶导数,最佳光谱区间分别为9 403.2~7 497.9 cm-1和9 403.2~7 497.9 cm-1+6 101.7~5 449.8 cm-1。优化后2,3-丁二酮和3 羟基-2-丁酮校正集样品的化学值和近红外预测值的决定系数(R2)分别为0.960 2和0.963 2,交叉验证均方根误差(RMSECV)分别为0.39、0.22 mg/100 mL;通过外部检验,验证集样品的R2分别为0.957 6和0.957 8,预测均方根误差(RMSEP)分别为0.40、0.24 mg/100 mL。结果表明,应用近红外光谱技术结合化学计量学方法所建立的模型有较高的准确度,能够满足白酒生产中酮类物质的快速检测需要。     

基于近红外光谱法的定量分析模型快速预测蕨菜中总黄酮的含量

采用近红外光谱法结合偏最小二乘法构建蕨菜中总黄酮含量的快速无损测定方法。取蕨菜样品140份,采用傅里叶变换近红外光谱仪采集4 000～11 500 cm^-1波段内近红外光谱,以一阶导数预处理原始光谱,设置主因子数为10,在6 100～7 500 cm^-1和5 400～6 000 cm^-1波段内建模。结果表明：校正集定量分析模型的校正均方根误差(RMSEC)为0.078,交叉验证决定系数(R²)为0.991 9;验证集定量分析模型的预测均方根误差(RMSEP)为0.125,R²为0.984 1,说明所建模型性能较优。分别以定量分析模型和紫外-可见(UV-Vis)分光光度法分析完全外部验证集样品,预测回收率(预测值和测定值比值的百分数)接近100%,说明所建模型的预测准确度较高,可用于蕨菜中总黄酮的快速、准确测定。     

应用近红外漫反射光谱法的猕猴桃品质无损检测北大核心CSCD

应用近红外漫反射光谱技术和化学计量学,研究成熟期猕猴桃内部品质与其近红外漫反射光谱之间的关系。在室温(24±2)℃下,采集猕猴桃赤道区域不同测试部位在4 000~10 000 cm^(-1)范围内的光谱数据,用基于平滑处理、归一化及基线校正的组合式处理方法对原始光谱进行预处理;另应用偏最小二乘(PLS)法、主成分回归法和多元线性回归法等方法分别建立猕猴桃硬度、可溶性固形物含量(SSC)的校正模型。结果表明:采用组合预处理方法和PLS法建立的校正模型精度最高;硬度校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.976 5,0.548 3,0.943 2,0.612 7;SSC校正集相关系数R_c、均方根误差RMSEC和预测集相关系数R_p、均方根误差RMSEP达到了0.916 6,0.539 6,0.901 2,0.619 0;试验结果验证了本法的可行性。     

应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

以普通玉米籽粒为试验材料,在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上,应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型．试验结果表明,基于11个特征波长所建立的校正模型,其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％,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0.9279和0.9390,与全光谱数据所建立的预测模型相比,在预测精度上均有所改善,表明应用遗传算法和PLS进行光谱特征选择,能获得更简单和更好的模型,为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径．     

Horizontal attenuated total reflection applied to simultaneous determination of ash and protein contents in commercial wheat flour

Partial least-squares (PLS) regression was used to generate various models for the determination of both the protein and the ash contents of wheat flours by using spectroscopic data in the mid-infrared region obtained with a horizontal attenuated total reflectance (HATR) accessory. One hundred samples of wheat flour were used as purchased in the market: 55 for constructing the calibration model and 45 as external samples. The protein content varied between 8.85 and 13.23% and the ash content, between 0.330 and 1.287%, as determined by reference methods. Raw spectra and those corrected by multiplicative signal correction (MSC), first and second derivative spectra, were used as data for building the models. Different pre-treatments, such as mean centered and/or variance scaled (VS) methods, were tested and compared. Very good models were built as judged by the correlation coefficients (R²), root mean square error of calibration (RMSEC), root mean square error of validation (RMSEV) and root mean square error of prediction (RMSEP) that were obtained. Best results were achieved with MSC treated spectra.     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

Infrared Spectroscopy as Alternative to Wet Chemical Analysis to Characterize Eucalyptus globulus Pulps and Predict Their Ethanol Yield for a Simultaneous Saccharification and Fermentation Process

Bioethanol can be obtained from wood by simultaneous enzymatic saccharification and fermentation step (SSF). However, for enzymatic process to be effective, a pretreatment is needed to break the wood structure and to remove lignin to expose the carbohydrates components. Evaluation of these processes requires characterization of the materials generated in the different stages. The traditional analytical methods of wood, pretreated materials (pulps), monosaccharides in the hydrolyzated pulps, and ethanol involve laborious and destructive methodologies. This, together with the high cost of enzymes and the possibility to obtain low ethanol yields from some pulps, makes it suitable to have rapid, nondestructive, less expensive, and quantitative methods to monitoring the processes to obtain ethanol from wood. In this work, infrared spectroscopy (IR) accompanied with multivariate analysis is used to characterize chemically organosolv pretreated Eucalyptus globulus pulps (glucans, lignin, and hemicellulosic sugars), as well as to predict the ethanol yield after a SSF process. Mid (4,000–400 cm^?1) and near-infrared (12,500–4,000 cm^?1) spectra of pulps were used in order to obtain calibration models through of partial least squares regression (PLS). The obtained multivariate models were validated by cross validation and by external validation. Mid-infrared (mid-IR)/NIR PLS models to quantify ethanol concentration were also compared with a mathematical approach to predict ethanol yield estimated from the chemical composition of the pulps determined by wet chemical methods (discrete chemical data). Results show the high ability of the infrared spectra in both regions, mid-IR and NIR, to calibrate and predict the ethanol yield and the chemical components of pulps, with low values of standard calibration and validation errors (root mean square error of calibration, root mean square error of validation (RMSEV), and root mean square error of prediction), high correlation between predicted and measured by the reference methods values (R ² between 0.789 and 0.997), and adequate values of the ratio between the standard deviation of the reference methods and the standard errors of infrared PLS models relative performance determinant (RPD) (greater than 3 for majority of the models). Use of IR for ethanol quantification showed similar and even better results to the obtained with the discrete chemical data, especially in the case of mid-IR models, where ethanol concentration can be estimated with a RMSEV equal to 1.9 g?L^?1. These results could facilitate the analysis of high number of samples required in the evaluation and optimization of the processes.     

土壤总氮近红外光谱分析的波段优选

利用移动窗口偏最小二乘( MWPLS)和Savitzky-Golay(SG)平滑方法优选土壤总氮的近红外(NIR)光谱分析模型.从全部97个土壤样品中随机选出35个样品作为检验集;基于偏最小二乘交叉检验预测偏差(PLSPB),将余下62个样品划分为具有相似性的建模定标集(37个样品)、建模预测集(25个样品).最优波段为1692～2138 nm,SG平滑的导数阶数(OD)、多项式次数(DP)、平滑点数(NSP)分别为0,6,69,PLS因子数为11,建模预测均方根偏差(M-RMSEP)、建模预测相关系数(M-Rp)分别为0.015％,0.931,检验预测均方根偏差(V-RM-SEP)、检验预测相关系数(V-RP)分别为0.018％,0.882.其结果可为设计专用NIR仪器提供有价值的参考.     

径向基函数网络在近红外人体无创伤血糖浓度检测基础研究中的应用

在近红外无创伤血糖浓度检测的基础研究中，对于多组分的混合物的分析，常因光谱与样品浓度之间呈现非线性响应，使得基于线性模型的校正方法失效。本文讨论了非线性校正方法径向基函数神经网络( RBFN )的有效性，并与线性校正方法中的主成分分析和偏最小二乘法作了对比研究。验证实验所用样品为①葡萄糖水溶液②包含牛血红蛋白和白蛋白的葡萄糖水溶液，结果表明：在①实验中PLS模型和RBFN预测标准偏差分别为8．2、8．9；在②实验中分别为15．6、8．8。可见在样品组分增多时，RBFN算法较线性PLS方法建立的模型预测能力强。     

Evolving factor analysis-based method for correcting monitoring delay in different batch runs for use with PLS: On-line monitoring of a transesterification reaction by ATR-FTIR

In this work, the base-catalyzed transesterification of soybean oil with ethanol was monitored on-line using mid-infrared spectroscopy (MIRS) and the yield of fatty acid ethyl esters (biodiesel) was obtained by (1)H NMR spectroscopy. The MIRS monitoring carried out for 12min, was performed using a cylindrical internal reflectance cell of PbSe in the range of 3707-814cm(-1) with eight co-added scans. Two different data treatment strategies were used: in the first, the models were built using the raw data and in the other, evolving factor analysis (EFA) was used to overcome the sensor time delay due to the on-line analysis, producing significantly better results. In addition, models based on partial least squares (PLS) using three batches for calibration and another for validation were compared with models with just one batch for calibration and three for validation. The models were compared between each other using root mean square error of prediction (RMSEP) and root mean square prediction difference (RMSPD), obtaining relative errors under 3%.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.