基于光谱比值分析的无标样近红外模型传递方法

测量环境及光谱仪台间差异导致近红外光谱(NIRS)模型传递到从机后,常产生较大误差。该文使用标准正态变量变换(SNV)+微分处理光谱消除光谱散射和基线漂移的影响,提出通过仪器间光谱信号比值分析筛选波长的方法(Screening wavelengths based on spectrum ratio analysis,SWSRA),选出仪器间一致性较好且样本间差异大的光谱特征波长,采用筛选出的波长信号建立待测性质的偏最小二乘近红外光谱定标模型。以80个玉米样品中水分、油、蛋白质含量及72个黄芩样品中黄芩苷含量的NIRS预测对该方法进行了检验。结果表明,SWSRA主机模型预测从机样品的各成分含量的平均相对误差均小于4.3%,明显优于全波长模型直接传递的结果,且其预测均方根残差RMSEP与文献报道的其他模型传递方法的结果相当或更优。SWSRA方法具有模型参数少、稳健、简便易行等优点,可以在同类型近红外光谱仪器之间实现模型的无标样传递。     

黄芩素和黄芩苷与人血清白蛋白相互作用的光谱研究

采用荧光光谱、紫外-可见光谱和表面增强拉曼光谱研究了黄芩素和黄芩苷与人血清白蛋白的相互作用, 得到了分子间的结合状态和吸附形态方面的信息. 通过比较黄芩素和黄芩苷的普通拉曼和表面增强拉曼光谱, 确立了黄芩素和黄芩苷的正常构象, 同时考察了结合上人血清白蛋白后不同浓度黄芩素和黄芩苷的吸附方式差别.     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

近红外光谱法同时测定通天口服液中天麻素和芍药苷的含量

本文应用近红外光谱结合偏最小二乘法建立了同时测定通天口服液中天麻素与芍药苷含量的方法。以高效液相色谱(HPLC)法测定通天口服液样品中天麻素和芍药苷的化学参考值,随机抽取60个样本作校正集,20个样本作预测集。用偏最小二乘法(PLS)将校正集样本的近红外光谱与相应样本的天麻素和芍药苷含量分别相关联建立模型。结果表明,天麻素和芍药苷校正模型的决定系数分别为96.28%、94.55%,模型的交叉验证均方差分别为0.0336、0.00908,预测集的决定系数分别为94.23%、92.86%,预测集均方差分别为0.0453、0.00839。同时还做了模型的精密度实验,该方法能用于大批量样品的快速分析。     

鱼粉近红外光谱模型传递应用研究

为探讨光栅型与傅里叶变换型近红外分析仪之间模型传递的应用效果,选取国产鱼粉为近红外光谱样本,DS2500F型近红外分析仪为源仪器,MPA型近红外分析仪为目标仪器,采用分段直接校正(PDS)方法实现近红外光谱传递。分别建立水分、粗蛋白质、粗脂肪、蛋氨酸和赖氨酸等组分的预测模型,通过交互验证决定系数(R2cv)、交互验证标准误差(RMSECV)、马氏距离(MD)、系统偏差(Bias)、预测均方根误差(RMSEP)和相对分析误差(RPD)等参数,多维度评估光谱传递后所建预测模型的效果。结果表明,DS2500F仪器的近红外光谱传递到MPA型仪器时,所建国产鱼粉的水分、粗蛋白质、粗脂肪、蛋氨酸、赖氨酸的预测模型与MPA型仪器原始预测模型各参数对比无显著差异,预测效果基本一致,说明国产鱼粉在DS2500F仪器上的近红外光谱通过传递可以替代MPA型仪器的原始光谱,间接实现了模型传递,且具有良好的适用性和共享性,可提高近红外预测模型的应用效率。     

基于高光谱图像的生菜叶片氮素含量预测模型研究

为了便于更经济合理地为作物施肥,建立一种无损检测作物氮营养元素的高光谱图像模型。本实验以生菜为研究对象,无土栽培各氮素水平的生菜叶样本,在莲座期,采集生菜叶片样本的高光谱图像(390~1050 nm),同时采用凯氏定氮法测定对应生菜叶片样本的全氮含量。通过ENVI软件提取出生菜叶片中感兴趣区域的平均光谱作为该样本原始光谱信息,分别使用平滑处理(Smoothing)、多元散射矫正(MSC)、标准正态变量变换结合去趋势(SNV detrending)、一阶导数法(First derivative)、二阶导数法(Second derivative)、正交信号矫正(OSC)等预处理方法对样本原始光谱进行处理,然后利用偏最小二乘回归法(Partial least squares regression,PLSR)分别建立样本全波段光谱信息与氮含量的关系模型,研究各预处理方法对氮含量模型的影响,结果表明,使用OSC预处理的模型效果最好。为了简化模型,根据OSC预处理光谱后的模型的PLSR回归系数优选出敏感波长,利用训练集中样本的敏感波长光谱信息与氮含量数据重新构建PLSR回归模型,并利用测试集样本进行测试试验。结果表明,该模型得到校正集和预测集的决定系数(R2p)分别为0.89,0.81;均方根误差RMSEC,RMSEP分别为0.33,0.45。该回归模型大大降低了自变量个数,简化了模型,并且取得了较优的效果,这为生菜氮素含量预测提供了一种新的快速有效方法。     

应用光谱技术无损检测油菜叶片中乙酰乳酸合成酶

应用可见/近红外光谱技术实现了油菜叶片中乙酰乳酸合成酶(ALS)的快速无损检测.对99个油菜样本进行光谱扫描,经过平滑、变量标准化、一阶求导等预处理后,应用偏最小二乘法(PLS)建立了ALS的预测模型.同时提取有效特征变量,作为反向传输人工神经网络(BPNN)和最小二乘-支持向量机(LS-SVM)的输入值,并建立相应的模型.用66个样本建模,33个样本验证.结果表明,LS-SVM模型能够获得最优的预测结果,预测集样本的相关系数(r)、预测标准差(RMSEP)和偏差(Bias)分别为0.998、 0.715和0.079,获得了满意的预测精度.结果表明,应用可见/近红外光谱技术结合LS-SVM检测油菜中乙酰乳酸合成酶是可行的,并能获得满意的预测精度,为进一步应用光谱技术进行油菜生长状况的大田监测奠定了基础.     

紫外-可见-短波近红外漫反射光谱技术测定白芷中二氧化硫残留量的研究

建立紫外-可见-短波近红外漫反射光谱结合化学计量学测定白芷中二氧化硫残留量的方法。利用紫外-可见-短波近红外漫反射光谱技术并结合化学计量学建模预测二氧化硫残留量。偏最小二乘回归法(PLSR)建模优于支持向量回归法(SVR); Random Frog波段选择结合Auto-scaling预处理后PLS建模后预测效果最佳,校正集R~2为0. 99,交叉验证集R~2为0. 94,预测集R~2为0. 96。紫外-可见-短波近红外漫反射光谱结合化学计量学可以实现二氧化硫残留量的快速检测,为中药饮片的质量评价及监管提供一种技术手段。     

近红外光谱技术对烟草pH值的快速测定

提出了一种基于近红外漫反射光谱技术快速测定烟草pH值的方法.采集不同烟草粉末样品的近红外漫反射光谱,并对其原始光谱数据进行一阶微分、二阶微分及平滑等预处理,用偏最小二乘法(PLS)方法建立pH值预测模型(建模样品572个).从光谱主成分分布和pH值分布方面考察了81个验证集样品,结果表明验证集样品分布范围较大,分布较合理.利用主仪器模型对验证集样品进行预测,结果表明主仪器一阶微分模型和二阶微分模型对验证集样品的pH值预测与实际测量值的平均绝对偏差分别为0.057、0.065,t检验表明预测值和实测值之间没有显著性差异,达到了较好的结果.考察了主仪器pH值一阶微分、二阶微分模型在同一型号不同仪器间的直接转移效果,一阶微分模型转移给了子仪器A ～F,二阶微分模型转移给了子仪器G,7台子仪器pH值预测的平均绝对偏差为0.049 ～0.070,且都通过了F检验.实验表明,该主仪器模型能够快速预测烟叶中的pH值,并成功转移到同类仪器上进行检测.     

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.     

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg1, Rb2, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-for-ward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg1, Rb1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.     

Laser ablation molecular isotopic spectrometry of water for 1D2/1H1 ratio analysis

Laser Ablation Molecular Isotopic Spectrometry (LAMIS) has been investigated for optical isotopic analysis of the deuterium to protium ratio in enriched water samples in ambient air at atmospheric pressure. Multivariate PLSR (Partial Least Squares Regression) based calibrations were carried out and validated using multiple statistical parameters. Comparisons of results are reported using two spectrometers having two orders of magnitude difference in spectral resolution. The accuracy and precision of isotopic analysis depends on the spectral resolution and the inherent isotope shift of the elements. The requirements for spectral resolution of the measurement system can be significantly relaxed when the isotopic abundance ratio is determined using chemometric processing of the spectra. Large isotopic shifts in the individual rotational branches of OH/OD molecular emission spectra were measured. Optimized temporal conditions for LAMIS measurements were established. Several sub-regions of spectra were used for PLSR calibration and the results demonstrate that both the emission intensity and degree of spectral differentiation affect the quality of the PLSR calibration. LAMIS results also were compared with traditional LIBS results obtained using PLSR and a spectral deconvolution method, demonstrating the advantages of LAMIS over LIBS with respect to isotopic composition determination.     

Components Analysis of Biochar Based on Near Infrared Spectroscopy Technology

This study aims to establish a rapid quantitative analysis method for biochar based on near infrared spectroscopy (NIRS) technology. Near infrared spectra of 163 samples in the 10000–3800 cm^–1 (1000–2632 nm) range were collected, and the contents of fixed carbon (FC), volatile matter (VM) and ash of samples were also analyzed. A partial least square (PLS) model for FC, VM and Ash was established after the model spectral ranges were optimized, the optimal factors were determined, and the raw spectra were pretreated by multiple scatter correction and second derivative (MSC + SD) method. Finally, the prediction performance of predictive model was evaluated. The results showed that the PLS model had a good prediction ability, and the predicted coefficient R²_p of actual values vs prediction values for FC, VM and ash were 0.9423, 0.9517 and 0.9265, respectively. Root mean square error of prediction (RMSEP) was 0.1074, 0.1201 and 0.1243, and ratios of prediction to deviation (RPD) were 3.51, 4.28 and 2.03, respectively. The PLS model had good accuracy and precision for both of FC and VM, and could be used as a quantitative method for FC and VM contents analysis. Nevertheless, PLS model need to improve the precision for Ash analysis according to RPD value. This method provides a fast and effective technical means for the quantitative analysis of biochar components.     

光谱滤波法提高激光诱导击穿光谱对蔬菜中元素Pb的检测精度

激光诱导击穿光谱(Laser induced breakdown spectroscopy, LIBS)原始光谱中包含较多噪声信号, 为探究不同滤波方法对LIBS光谱预处理的影响, 本研究以实验室Pb污染处理的蔬菜为研究对象, 采集波长范围在400.45~410.98 nm的LIBS谱线信息, 分别利用相邻平均(Adjacent averaging)、Savitzky-Golay(S-G)滤波器、快速傅里叶变换(Fast Fourier transformation, FFT)对采集的LIBS光谱进行平滑、去噪, 并结合偏最小二乘法(PLS)定量分析模型对光谱处理效果进行评价.结果表明, S-G平滑效果最优, 当S-G滤波器窗口宽度为15, 拟合阶次为3时, PLS定量模型效果最佳, 其验证集均方根误差(RMSEP)为0.26、平均相对误差(ARE)为3.7%.结果表明, 选择适合的滤波方法有助于提高LIBS光谱质量以及检测模型的精度.     

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.     

A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements

A bottleneck of the wide commercial application of laser-induced breakdown spectroscopy (LIBS) technology is its relatively high measurement uncertainty. A partial least squares (PLS) based normalization method was proposed to improve pulse-to-pulse measurement precision for LIBS based on our previous spectrum standardization method. The proposed model utilized multi-line spectral information of the measured element and characterized the signal fluctuations due to the variation of plasma characteristic parameters (plasma temperature, electron number density, and total number density) for signal uncertainty reduction. The model was validated by the application of copper concentration prediction in 29 brass alloy samples. The results demonstrated an improvement on both measurement precision and accuracy over the generally applied normalization as well as our previously proposed simplified spectrum standardization method. The average relative standard deviation (RSD), average of the standard error (error bar), the coefficient of determination (R²), the root-mean-square error of prediction (RMSEP), and average value of the maximum relative error (MRE) were 1.80%, 0.23%, 0.992, 1.30%, and 5.23%, respectively, while those for the generally applied spectral area normalization were 3.72%, 0.71%, 0.973, 1.98%, and 14.92%, respectively.     

Comparison of subcellular responses for the evaluation and prediction of the chemotherapeutic response to cisplatin in lung adenocarcinoma using Raman spectroscopy

Confocal Raman Micro-spectroscopy (CRM) is employed to examine the chemical and physiological effects of anticancer agents, using cisplatin and A549 adenocarcinoma cells as a model compound and test system respectively. Spectral responses of the membrane and cytoplasm of the cell are analysed independently and the results are compared to previously reported spectroscopic studies of the nucleus. Moreover, Raman spectra from the proteins extracted from the control and exposed samples are acquired and analysed to confirm the origin of the molecular changes of the cell membrane and cytoplasm of the A549 cells. Multivariate data analysis techniques including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) along with PLS-Jackknifing are used to analyse the data measured from the cell membrane and cytoplasm of the A549 cells and results are correlated with parallel measurements from the cytotoxicity assay MTT. A PLSR model is used to differentiate between the chemical effect of the chemotherapeutic agent and the physiological response of the A549 cells and to identify regions of the spectrum that are associated with these processes respectively. The PLSR model is also employed to predict, on the basis of the Raman spectra, the effective dose as well as the level of physiological response, using spectra data from the cytoplasmic and cell membrane regions. The effectiveness of the models based on spectral datasets from the cell membrane and cytoplasm is compared to similar models constructed using spectral data from the nuclear region as well as one combining spectral data from all regions. In all cases, higher prediction accuracy is found for regression against the cisplatin dose, and for both regression against the dose and the physiological response, nuclear data yield higher precision.     

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.