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.     

PLS‐DA for compositional data with application to metabolomics

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.     

Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples

Continuous wavelet transform (CWT) has been shown to be a high-performance signal processing technique in multivariate calibration. However, the signal processed by CWT with a specific wavelet may account for only a part of the information. To effectively utilize more abundant information contained in analytical signals, a method, named as wavelet unfolded partial least squares (WUPLS), was proposed. In the approach, the measured dataset is firstly extended by CWT with different wavelets, and then partial least squares (PLS) is employed to develop the quantitative model between the extended dataset and the target values. In order to select the representative wavelets, principal component analysis (PCA) is used to investigate the distribution of the signals obtained by CWT with different wavelets. The performance of the method was tested with blood and tobacco powder samples. Compared with the results obtained by PLS methods, the WUPLS method combined with signal processing techniques is proven to be a promising tool for improving the near-infrared (NIR) spectral analysis of complex samples.     

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.     

近红外分析中光谱预处理及波长选择方法进展与应用

光谱预处理和波长选取方法在近红外光谱分析技术中相当重要。本文综述了常用的NIR预处理和波长选取方法及这一领域的最新进展,详细介绍正交信号校正(OSC)、净分析信号(NAS)和小波变换(WT)等新光谱预处理方法以及无信息变量消除(UVE)和遗传算法(GA)等波长选取方法,并给出了这些方法的具体算法和一些应用实例。     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Multicomponent Determination of Organophosphorus Pesticides in Grain Samples by Linear Sweep Stripping Voltammetry and Multivariate Calibration

Abstract

Organophosphorus pesticides, such as parathion methyl (PTM), fenitrothion (FT), parathion (PT), and isocarbophos (ICP), have sensitive but overlapped voltammetric peaks with peak potentials ?309, ?364, ?317, and ?480 mV, respectively, in Britton‐Robinson buffer of pH 4.8 by application of linear sweep stripping voltammetry (LSSV). In this work, two multivariate calibration methods, partial least squares (both PLS‐1 and PLS‐2), and principal component regression (PCR), were applied to quantitatively resolve the overlapping voltammogram of the mixtures of these four pesticides. The prediction results obtained from a set of independent test samples showed that PLS‐1 method performed better prediction ability than PLS‐2 and PCR methods. The proposed method was successfully applied to the determination of these four pesticides in grain samples after a pre‐extraction step with a solvent of acetone.     

Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm

An enzymatic flow-batch system with spectrophotometric detection was developed for simultaneous determination of levodopa [(S)-2 amino-3-(3,4-dihydroxyphenyl)propionic acid] and carbidopa [(S)-3-(3,4-dihydroxyphenyl)-2-hydrazino-2-methylpropionic acid] in pharmaceutical preparations. The data were analysed by univariate method, partial least squares (PLS) and a novel variable selection for multiple lineal regression (MLR), the successive projections algorithm (SPA). The enzyme polyphenol oxidase (PPO; EC 1.14.18.1) obtained from Ipomoea batatas (L.) Lam. was used to oxidize both analytes to their respective dopaquinones, which presented a strong absorption between 295 and 540 nm. The statistical parameters (RMSE and correlation coefficient) calculated after the PLS in the spectral region between 295 and 540 nm and MLR-SPA application were appropriate for levodopa and carbidopa. A comparative study of univariate, PLS, in different ranges, and MLR-SPA chemometrics models, was carried out by applying the elliptical joint confidence region (EJCR) test. The results were satisfactory for PLS in the spectral region between 295 and 540 nm and for MLR-SPA. Tablets of commercial samples were analysed and the results obtained are in close agreement with both, spectrophotometric and HPLC pharmacopeia methods. The sample throughput was 18 h(-1).     

Comparison of multivariate calibration models for glucose,urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L⁻¹ for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L⁻¹ for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.     

Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks

Metal ions such as Co(II), Ni(II), Cu(II), Fe(III) and Cr(III), which are commonly present in electroplating baths at high concentrations, were analysed simultaneously by a spectrophotometric method modified by the inclusion of the ethylenediaminetetraacetate (EDTA) solution as a chromogenic reagent. The prediction of the metal ion concentrations was facilitated by the use of an orthogonal array design to build a calibration data set consisting of absorption spectra collected in the 370-760 nm range from solution mixtures containing the five metal ions earlier. With the aid of this data set, calibration models were built based on 10 different chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), artificial neural networks (ANN) and others. These were tested with the use of a validation data set constructed from synthetic solutions of the five metal ions. The analytical performance of these chemometrics methods were characterized by relative prediction errors and recoveries (%). On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). PLS and PCR models applied to the spectral data matrix that used the first derivative pre-treatment were the preferred methods. They together with ANN-radial basis function (RBF) and PLS were applied for analysis of results from some typical industrial samples analysed by the EDTA-spectrophotometric method described. DPLS, DPCR and the ANN-RBF chemometrics methods performed particularly well especially when compared with some target values provided by industry.     

Evaluation of the potential of Raman microspectroscopy for prediction of chemotherapeutic response to cisplatin in lung adenocarcinoma

The study of the interaction of anticancer drugs with mammalian cells in vitro is important to elucidate the mechanisms of action of the drug on its biological targets. In this context, Raman spectroscopy is a potential candidate for high throughput, non-invasive analysis. To explore this potential, the interaction of cis-diamminedichloroplatinum(II) (cisplatin) with a human lung adenocarcinoma cell line (A549) was investigated using Raman microspectroscopy. The results were correlated with parallel measurements from the MTT cytotoxicity assay, which yielded an IC(50) value of 1.2 ± 0.2 μM. To further confirm the spectral results, Raman spectra were also acquired from DNA extracted from A549 cells exposed to cisplatin and from unexposed controls. Partial least squares (PLS) multivariate regression and PLS Jackknifing were employed to highlight spectral regions which varied in a statistically significant manner with exposure to cisplatin and with the resultant changes in cellular physiology measured by the MTT assay. The results demonstrate the potential of the cellular Raman spectrum to non-invasively elucidate spectral changes that have their origin either in the biochemical interaction of external agents with the cell or its physiological response, allowing the prediction of the cellular response and the identification of the origin of the chemotherapeutic response at a molecular level in the cell.     

Characterization of surfactant complex mixtures using Raman spectroscopy and signal extraction methods: Application to laundry detergent deformulation

This paper presents the analysis of surfactants in complex mixtures using Raman spectroscopy combined with signal extraction (SE) methods. Surfactants are the most important component in laundry detergents. Both their identification and quantification are required for quality control and regulation purposes. Several synthetic mixtures of four surfactants contained in an Ecolabel laundry detergent were prepared and analyzed by Raman spectroscopy. SE methods, Independent Component Analysis and Multivariate Curve Resolution, were then applied to spectral data for surfactant identification and quantification. The influence of several pre-processing treatments (normalization, baseline correction, scatter correction and smoothing) on SE performances were evaluated by experimental design. By using optimal pre-processing strategy, SE methods allowed satisfactorily both identifying and quantifying the four surfactants. When applied to the pre-processed Raman spectrum of the Ecolabel laundry detergent sample, SE models remained robust enough to predict the surfactant concentrations with sufficient precision for deformulation purpose. Comparatively, a supervised modeling technique (PLS regression) was very efficient to quantify the four surfactants in synthetic mixtures but appeared less effective than SE methods when applied to the Raman spectrum of the detergent sample. PLS seemed too sensitive to the other components contained in the laundry detergent while SE methods were more robust. The results obtained demonstrated the interest of SE methods in the context of deformulation.     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

Simultaneous spectrophotometric determination of phenanthridine, phenanthridinone and phenanthridine N-oxide using multivariate calibration methods

The multivariate calibration methods, partial least squares (PLS) and principle component regression (PCR) have been used to determine phenanthridine, phenanthridinone and phenanthridine N-oxide in spiked human plasma samples. Resolution of binary and ternary mixtures of analytes with minimum sample pre-treatment and without analyte separation has been successfully achieved analyzing the UV spectral data. The net analyte signal (NAS) concept was also used to calculate multivariate analytical figures of merit such as limit of detection, selectivity and sensitivity. The simultaneous determination of three analytes was possible by PLS and PCR processing of sample absorbance in the 210–355 nm region. Good recoveries were obtained for both synthetic mixtures and spiked human plasma samples.     

Photochemistry and chemometrics—An overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics.To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.     

Determination of Maneb Residues,in Tomatoes,by Spectrophotometric Method of Acid Hydrolysis,Through Multivariate Calibration

ABSTRACT

In this work, a chemometric method was applied through multivariate calibration, PLS (Partial Least Squares), to establish the analysis of ethylenebisdithiocarbamates residues in tomatoes samples by the hydrolysis method. The algorithm used to implement the PLS in the MatLab environment on IBM-compatible personal computer, was obtained from chemometrics package PLS_ToolBox. In samples with elevated levels of Maneb the univariate calibration showed similar results to the multivariate calibration. However, in samples with lower levels of residues increases occurred in the order of 15 to 47% in the levels detected by the multivariate calibration. In addition, there was a significant decrease in the standard deviations, in relation to those obtained, when the method of univariate calibration was used. The levels of contamination by Maneb found in tomatoes samples were below the maximum established by the Brazilian legislation.     

Fast determination of milk fat content using Raman spectroscopy

In our work, we have demonstrated the capability of VIS Raman spectroscopy in combination with partial least square regression (PLS) as a rapid technique for direct milk fat determination. Raman spectra of milk samples revealed contributions from proteins, but mainly from their fat content with different spectral characteristics. Three different methods of sample preparations were applied: (i) liquid milk contained in an open dish, (ii) dried milk droplets on glass plates covered with Al foil, and (iii) liquid milk contained in quartz cuvettes. Methods (i) and (ii) showed a good PLS model for milk fat prediction with low root mean square errors and high correlation coefficients. The main advantage of milk sample contained in the dish lies in its simplicity as well as the fact that the open container maximizes the signal of interest avoiding background contributions. Our results show that Raman spectroscopy is suited for in-line monitoring purposes.     

Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry

Discrete wavelet transform (DWT) provides a well-established means for spectral denoising and baseline elimination to enhance resolution and improve the performance of calibration and classification models. However, the limitation of a fixed filter bank can prevent the optimal application of conventional DWT for the multiresolution analysis of spectra of arbitrarily varying noise and background. This paper presents a novel methodology based on an improved, second-generation adaptive wavelet transform (AWT) algorithm. This AWT methodology uses a spectrally adapted lifting scheme to generate an infinite basis of wavelet filters from a single conventional wavelet, and then finds the optimal one. Such pretreatment combined with a multivariate calibration approach such as partial least squares can greatly enhance the utility of Raman spectroscopy for quantitative analysis. The present work demonstrates this methodology using two dispersive Raman spectral data sets, incorporating lactic acid and melamine in pure water and in milk solutions. The results indicate that AWT can separate spectral background and noise from signals of interest more efficiently than conventional DWT, thus improving the effectiveness of Raman spectroscopy for quantitative analysis and classification.