Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.

Rapid Quantification of Melamine in Different Brands/Types of Milk Powders Using Standard Addition Net Analyte Signal and Near-Infrared Spectroscopy.

Multivariate calibration (MVC) and near-infrared (NIR) spectroscopy have demonstrated potential for rapid analysis of melamine in various dairy products. However, the practical application of ordinary MVC can be largely restricted because the prediction of a new sample from an uncalibrated batch would be subject to a significant bias due to matrix effect. In this study, the feasibility of using NIR spectroscopy and the standard addition (SA) net analyte signal (NAS) method (SANAS) for rapid quantification of melamine in different brands/types of milk powders was investigated. In SANAS, the NAS vector of melamine in an unknown sample as well as in a series of samples added with melamine standards was calculated and then the Euclidean norms of series standards were used to build a straightforward univariate regression model. The analysis results of 10 different brands/types of milk powders with melamine levels 0~0.12% (w/w) indicate that SANAS obtained accurate results with the root mean squared error of prediction (RMSEP) values ranging from 0.0012 to 0.0029. An additional advantage of NAS is to visualize and control the possible unwanted variations during standard addition. The proposed method will provide a practically useful tool for rapid and nondestructive quantification of melamine in different brands/types of milk powders.

Quality Degradation of Chinese White Lotus Seeds Caused by Dampening during Processing and Storage: Rapid and Nondestructive Discrimination Using Near-Infrared Spectroscopy.

Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n = 167) and dampened (n = 118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000-12000 cm(-1)) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively.

Untargeted detection and quantitative analysis of poplar balata (PB) in Chinese propolis by FT-NIR spectroscopy and chemometrics.

This paper investigates the feasibility of using FT-NIR spectroscopy and chemometrics for rapid analysis of poplar balata (PB) in Chinese propolis. Because practical adulterations usually involve addition of certain known active components, together with commercial PB, the commonly targeted analysis methods are insufficient to identify PB-adulterated propolis. Untargeted analysis of PB was performed by developing class models of pure propolis using one-class partial least squares (OCPLS). Quantitative analysis of PB was performed using partial least squares regression (PLSR). For untargeted analysis, the most accurate OCPLS model was obtained with SNV spectra with sensitivity 0.960 and specificity 0.941. OCPLS could detect adulterations with 2% (w/w) or more PB. For quantitative analysis, the root mean squared error of prediction (RMSEP) value of PB was 0.902 (w/w, %) with SNV-PLS. FT-NIR spectrometry and chemometrics demonstrate potential for rapid analysis of PB adulterations in Chinese propolis.

The feasibility of using near-infrared spectroscopy and chemometrics for untargeted detection of protein adulteration in yogurt: removing unwanted variations in pure yogurt.

Untargeted detection of protein adulteration in Chinese yogurt was performed using near-infrared (NIR) spectroscopy and chemometrics class modelling techniques. sixty yogurt samples were prepared with pure and fresh milk from local market, and 197 adulterated yogurt samples were prepared by blending the pure yogurt objects with different levels of edible gelatin, industrial gelatin, and soy protein powder, which have been frequently used for yogurt adulteration. A recently proposed one-class partial least squares (OCPLS) model was used to model the NIR spectra of pure yogurt objects and analyze those of future objects. To improve the raw spectra, orthogonal projection (OP) of raw spectra onto the spectrum of pure water and standard normal variate (SNV) transformation were used to remove unwanted spectral variations. The best model was obtained with OP preprocessing with sensitivity of 0.900 and specificity of 0.949. Moreover, adulterations of yogurt with 1% (w/w) edible gelatin, 2% (w/w) industrial gelatin, and 2% (w/w) soy protein powder can be safely detected by the proposed method. This study demonstrates the potential of combining NIR spectroscopy and OCPLS as an untargeted detection tool for protein adulteration in yogurt.

Automatic discrimination of the geographical origins of milks by excitation-emission fluorescence spectrometry and chemometrics.

This paper presents the automatic discrimination of geographical origins of milks from Western Yunnan Plateau areas and eastern China by excitation-emission fluorescence spectrometry and chemometrics. Genuine plateau milks (n = 60) and milks from eastern China (n = 89) are scanned in the regions of 180-300 nm for excitation and 200-800 nm for emission. Different options of data analysis are investigated and compared in terms of their performance in discriminating milks of different geographical origins: (1) two-way partial least squares discriminant analysis (PLSDA) based on excitation and emission spectra, respectively; (2) two-way PLSDA based on fusion of excitation and emission spectra; (3) three-way PLSDA based on excitation-emission matrix spectra. The two-way PLSDA methods with excitation spectra, emission spectra, and fusion of excitation and emission spectra correctly classify 91.3%, 88.6%, and 95.3% of the milk samples, respectively; while the total accuracy of three-way PLSDA is 96.0%. The results demonstrate the two-way data combining excitation and emission spectra are sufficient to characterize and identify the plateau milks. Considering both model accuracy and the analytical time required, two-way PLS-DA with fusion of excitation and emission spectra is recommended as a reliable and quick method to discriminate plateau milks from ordinary milks.

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis.

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.

Combining the least correlation design, wavelet packet transform and correlation coefficient test to reduce the size of calibration set for NIR quantitative analysis in multi-component systems.

The paper focuses on solving a common and important problem of NIR quantitative analysis in multi-component systems: how to significantly reduce the size of the calibration set while not impairing the predictive precision. To cope with the problem orthogonal discrete wavelet packet transform (WPT), the least correlation design and correlation coefficient test (r-test) have been combined together. As three examples, a two-component carbon tetrachloride system with 21 calibration samples, a two-component aqueous system with 21 calibration samples, and a two-component aqueous system with 41 calibration samples have been treated with the proposed strategy, respectively. In comparison with some previous methods based on much more calibration samples, the results out of the strategy showed that the predictive ability was not obviously decreased for the first system while being clearly strengthened for the second one, and the predictive precision out of the third one was even satisfactory enough for most cases of quantitative analysis. In addition, all important factors and parameters related to our strategy are discussed in detail.

The spatial effect of near-infrared spectroscopy and its application to the study of supramolecular chemistry.

An unlooked-for experimental observation that in near-infrared spectroscopy (NIR) the absorption peak of the second overtone of aniline adsorbed by 13X molecular sieve nearly disappeared led us investigate a fundamental question: the behavior of NIR when the outside space surrounding a molecule is too small to allow the molecule to vibrate freely. Through NIR of various organic compounds adsorbed by different porous inorganic materials like 13X molecular sieve, silica gel and active aluminium oxide, and NIR of supramolecular cyanuric acid-melamine, we can reasonably confirm a theoretical inference that in the micro-environment above, all intensities of NIR absorbance decrease, and the second overtone decreases more than the first overtone does. Furthermore, one distinct feature of NIR, higher sensitivity to the size of micro-environmental space as compared with mid-infrared (MIR), and its potential application to the study of supramolecular structure are outlined by our experiments.

Excitation-emission-kinetic fluorescence coupled with third-order calibration for quantifying carbaryl and investigating the hydrolysis in effluent water.

A novel method for determination of carbaryl in effluent was proposed in this study. The kinetic evolution of excitation-emission matrix fluorescence (EEM) for the pesticide were recorded and come into being a four-way data array. The four-way fluorescence data were analyzed using the parallel factor analysis (PARAFAC). The methodology exploits the second-order advantage of three-order calibration based on quadrilinear parallel factor analysis, allowing analyte concentrations to be estimated even in the presence of an uncalibrated fluorescent background. It gave the satisfactory results for determination of the carbaryl in effluent samples. In addition, the kinetic study of degradation of carbaryl was performed according to the kinetic profile provided by the calibration.

Using near-infrared spectroscopy and differential adsorption bed method to study adsorption kinetics of orthoxylene on silica gel.

This paper has demonstrated the study on the adsorption kinetics of orthoxylene on silica gel with a novel experimental methodology. In the method, there was a differential adsorption bed (DAB) where the solid adsorbent always contacted with the same bulk concentration of the adsorbate vapor, and the DAB was monitored with near-infrared diffuse reflectance spectroscopy (NIRDRS) continuously as well as non-invasively. Local partial least squares (PLS) algorithm was suggested to replace normal global PLS method in multivariate calibration models for processing NIRDRS data, because the concentration of the adsorbate on the adsorbent varied greatly as the adsorption process was going on. In this way, we, conveniently as well as promptly, obtained instantaneous adsorption rates of several orthoxylene/silica gel adsorption processes under different conditions like partial pressure of orthoxylene vapor and velocity of gas, and discovered that the adsorption process was physical adsorption, and mainly controlled by external diffusion.

Chemometric methods for evaluation of chromatographic separation quality from two-way data--a review.

Most traditional chromatographic separation criteria or response functions are defined on chromatograms recorded by single-channel detectors, e.g. a spectrometer measuring the absorbance at a single wavelength or a thermal conductivity detector. When the peaks are seriously overlapped, usually there is a lack of the information concerning the total number of chemical components, overlap degree of the peaks and peak purity. Such information characterizes some crucial aspects of separation process and lack of it will lead to an inaccurate and misleading evaluation of separation quality as well as some computational ambiguity for many traditional response functions. In contrast, hyphenated chromatography-(multi-channel) spectroscopy instruments together with chemometric methods will largely increase the information content available in chromatographic detection. Such information, if properly used, can cast a new light on evaluation of chromatographic separation quality. The main objective of this article is to review chemometric methods devoted to estimation of the number of chemical components, determination of elution sequence and assessment of peak purity. Some newly defined response functions or separation criteria based on extracted information by chemometric methods are also introduced. The methods reviewed are limited to those for treating two-way data obtained by hyphenation of high-performance liquid chromatography with multi-channel detectors. We prefer to provide a comprehensive view of such methods rather than present a full list of all the methods developed. Further details of some important methods are touched upon in favor of employment and understanding of them by researchers not very familiar with chemometrics.

An ensemble of Monte Carlo uninformative variable elimination for wavelength selection.

An improved method based on an ensemble of Monte Carlo uninformative variable elimination (EMCUVE) is presented for wavelength selection in multivariate calibration of spectral data. The proposed algorithm introduces Monte Carlo (MC) strategy to uninformative variable elimination-PLS (UVE-PLS) instead of leave-one-out strategy for estimating the contributions of each wavelength variable in the PLS model. In EMCUVE wavelength variables are evaluated by different Monte Carlo uninformative variable elimination (MCUVE) models. Moreover, a fusion of MCUVE and the vote rule can obtain an improvement over the original uninformative variable elimination method. Results obtained from simulated data and real data sets demonstrate that EMCUVE can properly carry out wavelength selection in the course of data analysis and improve predictive ability for multivariate calibration model.

QSAR study of angiotensin II antagonists using robust boosting partial least squares regression.

In the current study, robust boosting partial least squares (RBPLS) regression has been proposed to model the activities of a series of 4H-1,2,4-triazoles as angiotensin II antagonists. RBPLS works by sequentially employing PLS method to the robustly reweighted versions of the training compounds, and then combing these resulting predictors through weighted median. In PLS modeling, an F-statistic has been introduced to automatically determine the number of PLS components. The results obtained by RBPLS have been compared to those by boosting partial least squares (BPLS) repression and partial least squares (PLS) regression, showing the good performance of RBPLS in improving the QSAR modeling. In addition, the interaction of angiotensin II antagonists is a complex one, including topological, spatial, thermodynamic and electronic effects.