Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

Development and Application of Chemometric Methods in Near Infrared Spectral Analysis

In recent 10 years, like other disciplines influenced by the fast development of PC technique, chemometrics has been used in many analytical methods, especially in instrumental analysis. This article describes applications and comparison of multivariate linear regression (MLR), principal component analysis (PCA), principal component regression (PCR), partial least square (PLS), neural network (ANN), fuzzy and model recognition. A better calibration method can be a great help to improve the efficiency of the routine analytical work.     

Unsupervised random forest: a tutorial with case studies

Unsupervised methods, such as principal component analysis, have gained popularity and wide‐spread acceptance in the chemometrics and applied statistics communities. Unsupervised random forest is an additional method capable of discovering underlying patterns in the data. However, the number of applications of unsupervised random forest in chemometrics has been limited. One possible cause for this is the belief that random forest can only be used in a supervised analysis setting. This tutorial introduces the basic concepts of unsupervised random forest and illustrates several applications in chemometrics through worked examples. Copyright © 2016 John Wiley & Sons, Ltd.     

化学计量学―光谱法在工农业生产中的应用

化学计量学是近几年发展迅速的化学量测方法。常用的化学计量学方法有小波变换、多元线性回归、偏最小二乘法、主成分分析、人工神经网络、遗传算法等。化学计量学方法与光谱法相结合广泛运用于工业生产、农业生产和环境监测等各个领域,体现出化学计量学在数据处理、信号解析等方面的重要作用。化学计量学―光谱法的建立为工、农业生产中多组分混合物的快速、准确测定及满足质量监控等要求提供了一条有效的途径。     

Multiple analytical methods for identification and quality evaluation of Fritillariae Thunbergii Bulbus based on biological single molecules by high‐performance liquid chromatography

Quantitative analysis for biological single molecules in Fritillariae Thunbergii Bulbus and identification method by chemometrics was established by using high‐performance liquid chromatography. Amino acids, nucleosides, and nucleobases were quantitatively analyzed, and 11 peaks were selected for species identification. A Common pattern was established for chemometrics, and then similarity analysis, principal component analysis, and hierarchical cluster analysis heatmap were applied, and the results indicated that species were ideally identified from the adulterants as Fritillariae Cirrhosae Bulbus. This evaluation method was valuable for further quality control to select the characteristic components.     

Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools

A 400‐MHz ¹H nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis were used in the context of food surveillance to discriminate 46 authentic rice samples according to type. It was found that the optimal sample preparation consists of preparing aqueous rice extracts at pH 1.9. For the first time, the chemometric method independent component analysis (ICA) was applied to differentiate clusters of rice from the same type (Basmati, non‐Basmati long‐grain rice, and round‐grain rice) and, to a certain extent, their geographical origin. ICA was found to be superior to classical principal component analysis (PCA) regarding the verification of rice authenticity. The chemical shifts of the principal saccharides and acetic acid were found to be mostly responsible for the observed clustering. Among classification methods (linear discriminant analysis, factorial discriminant analysis, partial least squares discriminant analysis (PLS‐DA), soft independent modeling of class analogy, and ICA), PLS‐DA and ICA gave the best values of specificity (0.96 for both methods) and sensitivity (0.94 for PLS‐DA and 1.0 for ICA). Hence, NMR spectroscopy combined with chemometrics could be used as a screening method in the official control of rice samples. Copyright © 2013 John Wiley & Sons, Ltd.     

基于近红外光谱技术与化学计量学的绿茶无损鉴别方法研究

该文利用近红外光谱技术结合化学计量学方法开发了不同品种绿茶的无损鉴别方法。通过近红外光谱技术得到了8个品种绿茶样品的近红外光谱,比较了单一以及优化组合光谱预处理方法对光谱的影响,利用无监督的主成分分析(PCA)与有监督的线性判别分析方法(LDA)分别构建了茶叶品种鉴别模型。结果表明：对比单一预处理方法,优化组合预处理具有更优的鉴别准确性。标准正态变量变换预处理消除了茶叶样品大小不均造成的光谱散射影响,一阶导数预处理实现了变动背景的消除,减少了基线漂移的影响,突出了图谱中的有效信息,采用二者相结合的预处理方式并结合无监督的主成分分析法可实现较为准确的绿茶样品种类鉴别分析,准确率达75.0%。此外,采用有监督的线性判别分析方法处理原始光谱数据,可达到100%的鉴别准确率,但该方法需提供类别的先验知识。因此,采用近红外光谱技术和化学计量学相结合的手段可实现不同品种绿茶的快速无损鉴别。     

Application of the ANOVA-PCA method to stability studies of reference materials

Near infrared spectroscopy (NIRS) is an analytical technique that can be very useful for stability studies in particular because of its non destructive analytical capability. However, the spectral interpretation and treatment of this kind of multivariate data remains difficult without the use of chemometrics. In this article, a recent chemometrics method, analysis of variance - principal component analysis (ANOVA-PCA), was used for NIRS stability studies of sunflower and bread wheat external reference materials (ERM). It provided a practical tool for the study of the significance of various storage conditions according to an experimental design. Thus, the effect of the temperature, the nature of the atmosphere in the packaging and the storage duration were tested. ANOVA-PCA highlighted the influence of temperature and storage duration on the stability of the sunflower materials. For the bread wheat materials, the storage conditions did not have a significant effect on stability. Consequently, by applying ANOVA-PCA to near infrared spectral data, the sunflower materials were found to be considered stable for the time length of the study, i.e. 18 months stored in a cold room, while the bread wheat materials were found to be considered stable for the time length of the study, i.e. 12 months under the same conditions.     

蓝色圆珠笔字迹的高效液相色谱分析与模式识别研究

该文收集市售蓝色圆珠笔样品16份,结合模式识别技术对其高效液相色谱数据进行研究。利用相关系数法计算色谱图相对峰面积的相似度,定量地评价样品间的差异性。选取16个样品3个批次间的最小相似度(λ=0.92)作为判定是否源于同种样品的阈值,相似度小于0.92的数据为89份,占全部比对样品的74.2%;根据液相色谱图的保留时间、峰数及峰形差异将所有样品分为3类,进一步利用系统聚类法依据相似度的大小将第一类样品分为3小类。该文提出的采用相对峰面积相似度评价样品差异的方法,能够显著提高分辨能力,促进圆珠笔字迹的自动化识别。高效液相色谱法获取数据,模式识别技术分析数据的方法为圆珠笔字迹的分类与鉴别提供了新思路。     

Analytical problem-solving, reference materials, and multivariate quality control: A chemometrics approach

Summary Breakthroughs in sensor technology have augmented the chemist's measurement repertoire by introducing new kinds of detectors with improved selectivity and the capacity to perform simultaneous multi-species measurements. Thus, the electronic revolution has qualitatively and quantitatively changed the data matrices to which the analyst/problem-solver has access. The new chemical subdiscipline of chemometrics is developing powerful mathematical and statistical data analysis tools to exploit the electronic windfall and enhance data interpretation. Principal component analysis and graphical procedures have been used to examine the multivariate suitability of current reference materials in matching the concentration ranges and matrices for various food analyses. Principal component analysis has been useful in developing and exploring quality control information for the routine analysis laboratory.     

Thermogravimetric and principal component analyses in quality assessment of lubricating oils

The suitability of the derivative thermogravimetric and principal component analyses for the assessment of service performance of lubricating oils has been studied. A total sum of 179 samples has been examined, including M-20 Bp, MS-20 p, Marinol CB SAE-30 and DS-11 oils. The results indicate that principal component analysis greatly assisted in the analysis of the quality of lubricating oils by derivative thermogravimetric technique. Considering that, this multivariate statistical method can be applied to the differentiation of oil samples taking into account degree of their degradation in the oil system of an engine.     

A simple and effective method for identification of Fraxini Cortex from different sources by multi-mode fingerprint combined with chemometrics

Fraxini Cortex has a long history of being used as a medicinal plant in traditional Chinese medicine. However, it is challenging to differentiate and make quality evaluations for Fraxini Cortex from different origins due to their similarities in morphological features, as well as general chemical composition using traditional chemical analytical methods. In this study, a simple and effective method was developed to identify Fraxini Cortex from different origins by multi-mode fingerprint combined with chemometrics. Digital images of the high-performance thin-layer chromatography profiles were converted to grayscale intensity, and the common patterns of high-performance thin-layer chromatography fingerprints were generated with ChemPattern software. Authentication and quality assessment were analyzed by similarity analysis, hierarchical cluster analysis, principal component analysis, and multivariate analysis of variance. The ultra-high-performance liquid chromatography fingerprints were analyzed by similarity analysis, principal component analysis, and orthogonal partial least square-discriminant analysis. When combined with chemometrics, high-performance thin-layer chromatography and ultra-high-performance liquid chromatography fingerprint provided a simple and effective method to evaluate the comprehensive quality of Fraxini Cortex, and to distinguish its two original medicinal materials (Fraxinus chinensis Roxb. and Fraxinus rhynchophylla Hance.) recorded in the Chinese Pharmacopeia and its three adulterants (Fraxinus mandschurica Rupr., Fraxinus pennsylvanica Marsh., and Juglans mandshurica Maxim.). A similar workflow may be applied to establish a differentiation method for other medicinal and economic plants.     

Solomon,a classification program based on a statistical multivariate disjoint model

An algorithm called SOLOMON is presented for classification of patterns in multi-dimensional space. This is achieved by constructing a statistical model based on multivariate analysis of the classes under study. The disjoint multivariate analysis is done by using multi-inductive component analysis which has many advantages compared to techniques such as principal components analysis. A weghting algorithm is described for optimum classification results.     

化学计量学在电分析化学中的新进展

评述了化学计量学的各种方法,如主成分分析、偏最小二乘、小波分析、人工神经网络等在电分析化学中的进展,主要介绍了这些方法在电分析化学中的应用,并展望了化学计量学在电分析化学中的应用前景。     

Chemometrics: an important tool for the modern chemist, an example from wood-processing chemistry

This study briefly outlines the idea of principal component analysis and cross-correlation calculations (applied chemometrics) and presents an illustrative example from wood-processing chemistry. The applicability of chemometric data analysis was demonstrated by investigating the various structural changes that take place in dissolved and degraded lignin ("kraft lignin") during laboratory-scale kraft pulping of Scots pine (Pinus sylvestris) and silver birch (Betula pendula). The structural data (31P NMR and size exclusion chromatographic data) on kraft lignin were further processed by chemometric multivariate techniques (PCA and 2DCC), confirming, for example, that the cleavage of beta-aryl ether structures, the most prominent linkages between monomeric units, is directly related to the decrease in the average molecular mass of lignin.     

Simultaneous Voltammetric Determination of Three Herbicides in Food and Water Samples with the Aid of Chemometrics

Differential pulse stripping voltammetry method(DPSV) was applied to the determination of three herbicides,ametryn,cyanatryn,and dimethametryn.It was found that their voltammograms overlapped strongly,and it is difficult to determine these compounds individually from their mixtures.With the aid of chemometrics,classical least squares(CLS),principal component regression(PCR) and partial least squares(PLS),voltammogram resolution and quantitative analysis of the synthetic mixtures of the three compounds were successfully performed.The proposed method was also applied to the analysis of some real samples with satisfactory results.     

Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon-hydrogen bonds with molybdenum-oxo bonds

Principal component analysis (PCA) is a favorite tool in chemometrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%.     

On-line attenuated total reflection infrared (ATR-IR) spectroscopy combined with advanced chemometrics for investigating the synthesis process of 3,5-diamino-1,2,4-triazole

The synthesis process of 3,5-diamino-1,2,4-triazole (DAT) was investigated by on-line attenuated total reflection infrared (ATR-IR) spectroscopy combined with advanced chemometrics method. The principal component analysis (PCA) was used to analyze the IR spectra matrix, which was in order to obtain orthonormal column and the number of principal components. Then the pure IR spectrum of every substance was obtained by mutual information least dependent component analysis (MILCA). The possible synthesis mechanism of DAT was deducted based on the changes of functional groups in the IR spectra. The geometric configurations of intermediates were optimized with the density functional theory (DFT) at B3LYP/6-311G*(d, p) level, and the vibrational frequencies were calculated simultaneously. The results by MILCA method agree well with quantum chemical calculation method, thus which demonstrated the reliability of MILCA. The present study proves that on-line ATR-IR spectroscopy combined with advanced chemometrics method can be applied to study the chemical synthesis mechanism and provide a strong technical support for the research and development of process analytical technology (PAT).     

Characterization of Aromatic Liquor by Gas Chromatography and Principal Component Analysis

The differentiation of aromas of Chinese liquor is important for their unique flavors. In this work, aromas of Chinese liquor were characterized by gas chromatography and chemometrics. Ten representative aroma compounds, including three alcohols, four esters, two organic acids, and acetal in 16 Chinese liquor were determined by gas chromatography with flame ionization detection. The relationship between these compounds and six classic aromas was investigated using principal component analysis and k-means clustering. The cumulative contribution of the first three principal components reached up to 84.607%, which effectively differentiated the liquors. The variables with the highest loading absolute value were acetal and ethyl acetate for principal component 1, ethyl butanoate and ethyl hexanoate for principal component 2, and the hexanoic acid and ethyl butanoate for principal component 3. The aromas of the liquors were characterized by k-means clustering with the first three principal component scores, indicating that the acetal, ethyl acetate, ethyl butanoate, ethyl hexanoate, and hexanoic acid are important for the aroma of Chinese liquors. This work demonstrated that the gas chromatography with chemometrics is effective for the characterization of aromatic liquor.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.