Characterization of Gentiana rigescens by Ultraviolet–Visible and Infrared Spectroscopies with Chemometrics

Gentiana rigescens is a medicinal plant for treatment of rheumatism, convulsions, and jaundice. The present paper reports the potential for chemometric characterization of G. rigescens using ultraviolet–visible and infrared spectroscopies, individually and together. Low- and mid-level data fusions were considered in this study. Partial least square discriminant analysis and support vector machine were used to analyze single and fused spectral data, respectively. The data fusion strategy improved the classification capacity. In particular, the mid-level data fusion provided the best results by selecting important variance from the data matrixes, with 100% correct classification of test samples by partial least square discriminant analysis and support vector machine characterization. The results demonstrated that the combined use of ultraviolet–visible and infrared spectroscopies is more suitable for the discrimination of G. rigescens than the individual methods.     

Characterization of Paris polyphylla var. yunnanensis by Infrared and Ultraviolet Spectroscopies with Chemometric Data Fusion

Paris polyphylla var. yunnanensis has been used for its anti-tumor, anthelmintic, and hemostatic properties. In this investigation, Fourier transform infrared and ultraviolet spectroscopy combined with chemometrics were used for qualitative analysis of P. polyphylla var. yunnanensis from different geographical origins in Yunnan Province. A total of 82 samples for each region were divided into 57 in the calibration set and 25 in the validation set by Kennard–Stone algorithm. Support vector machine and partial least square discrimination on the basis of Fourier transform infrared, ultraviolet, and low- and mid-level data fusion were investigated. Different pretreatments were compared for the appropriate model. The results indicated that the combination of Savitzky–Golay (11 points), second derivative, and standard normal variation has the best performance for support vector machine and partial least square discrimination with the lowest root mean square error of estimation and root mean square error of cross validation and the highest cross validation accuracy rate. The accuracies of calibration and validation for mid-level data fusion in the model of support vector machine were 84.21 and 96% for the partial least square discrimination values of 96.49 and 84%, which was better performance than a single technique or low-level data fusion for the classification. Moreover, the chemical information of sample collected from Kunming and Xishuangbanna was distinguishable from the others. These results provide a rapid and robust strategy for quality control of P. polyphylla var. yunnanensis for further analysis.     

Authentication of Dendrobium Species Using Near-Infrared and Ultraviolet–Visible Spectroscopy with Chemometrics and Data Fusion

Herbal products produced from multiple plants have special characteristics in the clinical practice of traditional Chinese medicine. These traits provide the opportunity for fraudulent merchants to mix other herbal products similar in appearance into authentic herbal medicine. Shihu is a tonic herbal medicine from the Dendrobium plants with complex botanical origins. In this context, 11 Dendrobium plants including 109 individuals from China were collected for authentication work. Nine species have been described as herbal medicines in the literature while D. hookerianum and D. xichouense are not reported to have medicinal benefits. A key feature of this study was that multiple recognition approaches, based on near-infrared and ultraviolet–visible spectra as well as their combination, were compared to investigate their classification performance. Intuitively, score plots using principal component analysis and hierarchical cluster diagrams were used to evaluate the genetic relationships among these species. Compared with support vector machine discrimination analysis and k-nearest neighbor models, the partial least square discrimination analysis model combined with low-level data fusion provided excellent performance for authentication and was the most robust model with 100% accuracy rates for the training and prediction sets. The results indicated that near-infrared and ultraviolet–visible spectra and their fusion dataset combined with supervised recognition analysis are effective and therefore recommended for the authentication of genuine and sham of herbal Shihu species.     

Characterization of Tobacco Leaves by Near-Infrared Reflectance Spectroscopy and Electronic Nose with Support Vector Machine

The determination of different regions of tobacco leaves is vital in the tobacco industry. Different parts of tobacco leaves produce varying flavors due to the different chemical compositions. Here, near infrared spectroscopy and electronic nose were combined with support vector machine to predict the parts of tobacco leaves. Comparing to the single data model as near infrared spectroscopy with support vector machine or electronic nose with support vector machine, near infrared spectroscopy and electronic nose with support vector machine model show higher accuracy. The accuracy of near infrared spectroscopy and electronic nose with support vector machine model is 95.31%, while the accuracy of leave-one-out cross-validation is 79.69%. The optimal model was then applied to 60 unknown tobacco samples from different parts of tobacco leaves to test its accuracy, which is 81.67%.     

FT-MIR and UV–vis data fusion strategy for origins discrimination of wild Paris Polyphylla Smith var. yunnanensis

Paris Polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz has multiple therapeutic properties and the origins may affect clinical efficacy. Tracing the geographical origin is important to the authentication and quality assessment of this species. 177 wild samples collected from central, southeast and northwest Yunnan Province, China, were analyzed by single analytical method and data fusion strategies (low- and mid-levels) using Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV–vis) spectroscopies combined with chemometrics (partial least squares discrimination analysis (PLS-DA) and support vector machines grid search (SVM-GS)), for categorizing samples from different geographic origins. According to the results, mid-level data fusion strategy presented a better generalization performance and accuracy rates based on latent variables selected by PLS-DA than single analytical method and low-level data fusion strategy. Accuracy rates were almost 100% when both of the PLS-DA and SVM-GS were employed for classifying samples picked from southeast and northwest districts based on mid-level dataset. For samples collected from central of Yunnan where was divided into seven categories in this paper, the accuracy rates of training set and test set of PLS-DA and SVM-GS were preferable (>87%). Based on the mid-level data set, both of the classification results of PLS-DA and SVM-GS presented satisfying accuracy for 177 samples. Additionally, as small as possible parameters showed in mid-level data set, it suggested that this method was robust and generalized. Therefore, the comprehensive method was established for the origin traceability of wild P. Polyphylla Smith var. yunnanensis, which is meaningful for the quality control of herbal medicines.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Geographical Authentication of Gentiana Rigescens by High-Performance Liquid Chromatography and Infrared Spectroscopy

The chemical characteristics of Gentiana rigescens are extremely variable due to their geographical origins which should be determined to evaluate the quality of this species. Different with other herbs with official tissue for classification materials, the geographical characterization of raw herbal materials on the basis of nonmedicinal parts is rarely discussed. Chromatographic active components were used as references to characterize the chemical profiles of samples from various geographical origins. Based on spectra data matrix of different botanical parts, the chemometric methods of partial least square discrimination analysis and support vector machine discrimination analysis were used to develop mathematical models to classify samples from different geographical origins. In terms of six active components, we found that significant differences were present in the tissue of G. rigescens based on geographical origins. In addition, the region with higher content of gentiopicroside was selected to be the optimal cultivated location. Chemometric results indicated that leaves were the optimal material for geographical characterization of G. rigescens with 100% accuracy by support vector machine while the accuracies of roots, stems, and flowers were 90.91, 96.10, and 97.01%, respectively. Partial least square discrimination analysis showed that accuracy values for roots, stems, leaves, and flowers were 35.65, 67.53, 76.62, and 50.75%, respectively, which also indicated that leaves are the optimal material. In conclusion, northwest Yunnan Province with higher content of gentiopicroside was selected to be the optimal cultivation location. Furthermore, leaves should be used for the most accurate geographical authentication.     

Sparse Representation Classification of Tobacco Leaves Using Near-Infrared Spectroscopy and a Deep Learning Algorithm

A spare representation classification method for tobacco leaves based on near-infrared spectroscopy and deep learning algorithm is reported in this paper. All training samples were used to make up a data dictionary of the sparse representation and the test samples were represented by the sparsest linear combinations of the dictionary by sparse coding. The regression residual of the test sample to each class was computed and finally assigned to the class with the minimum residual. The effectiveness of spare representation classification method was compared with K-nearest neighbor and particle swarm optimization–support vector machine algorithms. The results show that the classification accuracy of the proposed method is higher and it is more efficient. The results suggest that near-infrared spectroscopy with spare representation classification algorithm may be an alternative method to traditional methods for discriminating classes of tobacco leaves.     

Classifying formulations of crosslinked polyethylene pipe by applying machine‐learning concepts to infrared spectra

Crosslinked polyethylene (PEX‐a) pipes are emerging as promising replacements for traditional metal or concrete pipes used for water, gas, and sewage transport. Understanding the relationship between pipe formulation and performance is critical to their proper design and implementation. We have developed a methodology using principal component analysis (PCA) and the machine learning techniques of k‐means clustering and support vector machines (SVM) to compare and classify different PEX‐a pipe formulations based on characteristic infrared (IR) spectroscopy absorbance peaks. The application of PCA revealed that a large percentage (89%) of the total variance could be explained by the first three principal components (PC1‐PC3), with distinct clustering of the data for each formulation. By examining the contribution of the individual IR bands to the PCs, we determined that PC1 could be attributed to different peroxide crosslinkers, whereas PC2 and PC3 could be attributed to differences in the additives. Using the PCA results as input to k‐means clustering and SVM resulted in very high accuracy of classifying the different pipe formulations. Our approach highlights the advantages of using PCA and machine learning techniques to characterize different formulations of PEX‐a pipes, which is important to achieve a detailed understanding of the pipe formulation and manufacturing process. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1255–1262     

多源光谱信息融合在水质分析中的应用

为解决现有单一光谱法用于水质有机污染综合指标分析精度较低的问题,提出一种基于多源光谱信息融合的水质分析新方法。本方法采用改进的参考独立成分分析方法分别提取紫外吸收光谱和三维荧光光谱对检测水样的多种有效信息特征,去除与水质分析无关的干扰信号;然后采用最小二乘支持向量机进行特征信息融合建模。采用总有机碳指标覆盖范围在3.4~125.3 mg/L内的32个城市地表水和生活污水样本为研究对象,对其紫外光谱、荧光光谱数据进行了信息融合分析实验。结果表明:采用融合分析方法后,对总有机碳指标的分析误差均方根比单一紫外光谱分析法和单一荧光光谱分析法分别下降36.1%和34.7%。     

最小角回归结合核极限学习机的近红外光谱对柑橘黄龙病的鉴别

传统的柑橘黄龙病检测方法存在准确度低、稳定性差等问题,该文提出了一种基于最小角回归结合核极限学习机(Least angle regression combined with kernel extreme learning machine,LAR-KELM_((RBF)))的近红外柑橘黄龙病鉴别方法。该方法将光谱数据通过小波变换进行预处理,然后用最小角回归(LAR)算法进行光谱波长的筛选,最后通过核极限学习机(KELM_((RBF)))实现样本的分类。实验采用柑橘叶片的近红外光谱数据,验证了LAR-KELM_((RBF))算法的性能,其分类准确度最高为99.91%,标准偏差为0.11。不同规模训练集的实验结果表明,LAR-KELM_((RBF))模型较极限学习机(ELM)、波形叠加极限学习机(SWELM)、反向传播神经网络(BP_((2层)))、KELM_((RBF))和支持向量机(SVM)模型分类准确度高、稳定性强,能够广泛应用于柑橘黄龙病的检测鉴别。     

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

Chinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.     

Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data

Classification of suspect powders, by using laser‐induced breakdown spectroscopy (LIBS) spectra, to determine if they could contain Bacillus anthracis spores is difficult because of the variability in their composition and the variability typically associated with LIBS analysis. A method that builds a support vector machine classification model for such spectra relying on the known elemental composition of the Bacillus spores was developed. A wavelet transformation was incorporated in this method to allow for possible thresholding or standardization, then a linear model technique using the known elemental structure of the spores was incorporated for dimension reduction, and a support vector machine approach was employed for the final classification of the substance. The method was applied to real data produced from an LIBS device. Several methods used to test the predictive performance of the classification model revealed promising results. Published 2012. This article is a US Government work and is in the public domain in the USA.     

Application of near infrared spectroscopy (NIR), X-ray fluorescence (XRF) and chemometrics to the differentiation of marmora samples from the Mediterranean basin

Abstract

Near-infrared (NIR) and X-ray fluorescence spectra were recorded for 15 different samples of marmora, from the Mediterranean Basin and of different colours. After appropriate pretreatment (SNV transform + second derivative), the results were subjected to principal component analysis (PCA) treatment with a view to differentiating them. The observed differences among the samples were chemically interpreted by highlighting the NIR wavelengths and minerals, respectively, contributing the most to the PCA models. Moreover, a mid-level data fusion protocol allowed integrating the information from the different techniques and, in particular, to correctly identify (based on the distance in the score space) three test samples of known type. Moreover, it should be stressed that positive results on the differentiation and identification of marmora were obtained using two completely non-invasive, non-destructive and relatively inexpensive techniques, which can also be used in situ.     

Quantification of <Emphasis Type="Italic">Lactobacillus</Emphasis> in fermented milk by multivariate image analysis with least-squares support-vector machines

This paper reports an approach for quantification of Lactobacillus in fermented milk, grown in a selective medium (MRS agar), by use of digital colour images of Petri plates easily obtained by use of a flatbed scanner. A one-dimensional data vector was formed to characterize each digital image on the basis of the frequency-distribution curves of the red (R), green (G), and blue (B) colour values, and quantities derived from them, for example lightness (L), relative red (RR), relative green (RG), and relative blue (RB). The frequency distributions of hue, saturation, and intensity (HSI) were also calculated and included in the data vector used to describe each image. Multivariate non-linear modelling using the least-squares support vector machine (LS-SVM) and a linear model based on PLS regression were developed to relate the microbiological count and the frequency vector. Feasibly models were developed using the LS-SVM and errors were below than 10% for Lactobacillus quantification, indicating the proposed approach can be used for automatic counting of colonies.     

Green Multi-Platform Solution for the Quantification of Levodopa Enantiomeric Excess in Solid-State Mixtures for Pharmacological Formulations

The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.     

水平衰减全反射傅里叶变换红外光谱法结合化学计量学鉴别问荆与节节草

采用水平衰减全反射傅里叶变换红外光谱法(HATR-FT-IR)测定中药问荆和同科植物节节草的FT-IR谱,运用连续小波变换(CWT)多分辨率分析法对吸收较为相似的问荆及节节草的FT-IR进行特征提取.选择第8、9、10分解层数下的特征值作为分析的基础,采用FT-IR-CWT-SVM法建立了问荆和节节草识别的模型.通过学习训练,对120个预测样品的识别准确率为90%以上.当采用径向基函数作为核函数时,识别准确率达100%.样品的FT-IR经小波特征提取后的特征值有所差异,采用SVM进行识别可以很好地把两者分类.通过对样品的FT-IR小波变换后所得特征值进行SVM的分类,能够有效地进行区别鉴定形态较为相似的同科植物问荆及节节草.     

Spectroscopy of Dibenzorubicene: Experimental Data for a Search in Interstellar Spectra

We report on the characterization of dibenzo[cde,opq]rubicene (C₃₀H₁₄). The molecule was studied in solution at room temperature with absorption spectroscopy in the visible (vis) and ultraviolet (UV) wavelength ranges, and with emission spectroscopy. The infrared (IR), visible, ultraviolet, and vacuum ultraviolet (VUV) absorption spectra of a thin film were measured also at room temperature. In addition, the UV/vis absorption spectrum was measured at cryogenic temperatures using the matrix isolation spectroscopy technique. The interpretation of spectra was supported by theoretical calculations based on semiempirical and ab initio models, as well as on density functional theory. Finally, the results of the laboratory study were compared with interstellar spectra.     

基于XRF和Vis-NIR光谱数据融合的土壤镉含量定量分析法

根据鄱阳湖南矶山区域土壤的X荧光光谱和可见近红外光谱特征,建立了3种数据融合(等权融合、累加融合、外积融合)的最小二乘向量机定量分析模型。结果表明,等权融合和外积融合模型精度和稳定性均优于单一光谱定量分析模型。其中外积融合模型性能最佳,其决定系数(R2)为0.85,校正均方根误差(RMSEC)为009,预测均方根误差(RMSEP)为006,相对分析误差(RPD)为2.41,满足实际土壤中Cd的检测需求。该方法准确可靠,可为我国土壤重金属分类分级方法研究提供参考。     

一种基于最小二乘支持向量机算法的近红外光谱判别分析方法

将最小二乘支持向量机(LSSVM)用于近红外(NIR)光谱分析,建立一种新型的NIR光谱快速鉴别方法。以丹参药材道地性鉴别为例,对其NIR漫反射光谱进行主成分分析后,运用LSSVM法建立NIR光谱非线性分类模型,对丹参药材道地性进行快速鉴别。将本方法与经典SVM和BP神经网络法相比较,结果表明,本法判别准确率高,计算时间少,可推广应用于中药等天然产物质量快速鉴别。