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Juice counterfeiting not only affects product quality but also deceives consumers into buying low-quality goods at high prices. Here is explored the feasibility of combining attenuated total reflection–mid-infrared (ATR-MIR) spectroscopy and interval-based partial least squares (PLS) and full-spectrum PLS models for identifying the adulteration of apple juice. A sample set composed of 41 concentrations of diluted juice was prepared. The adulteration concentration was obtained based on the juice spectrum and the corresponding model. The changeable moving window PLS model was shown to provide the best performance and may be an alternative to traditional approaches for quality control of juice. [ABSTRACT FROM AUTHOR]

The quantitative determination of the components of textiles takes an important position in quality control. A rapid and green method for simultaneously determining four components in blended fabrics was explored by near-infrared (NIR) spectroscopy combined with chemometrics. Two sample sets were designed and used as the training and test sets, respectively. The four components included wool, polyester, polyacrylonitrile, and nylon. A variance sorting-based algorithm was used for variable filtering. Both classic partial least squares (PLS) and extreme learning machine (ELM) were used for multivariate calibration and a systematic comparison was made. This results reveal that ELM is superior to the conventional PLS, especially when there are fewer variables, indicating that NIR spectroscopy combined with ELM and a pertinent variable selection is feasible for NIR-based textile analysis. The developed procedure may have commercial and regulatory potential to avoid laborious, time-consuming, and expensive wet chemical analysis. [ABSTRACT FROM AUTHOR]

Metronidazole is a widely used antibacterial and amoebicide drug. The feasibility of the classification of metronidazole samples with respect to their brands was investigated by near-infrared (NIR) spectroscopy along with chemometrics. A total of 92 samples of different lots and four brands were collected for measurements. First, principal component analysis was conducted to visualize the difference between metronidazole samples of different brands. Then, based on an effective classifier-independent method, i.e., joint mutual information, only the 30 most important variables were selected for modeling. From the independent test set, the partial least-squares discriminant analysis model based on the reduced variable set was compared with the corresponding full-spectrum model using all variables, which indicates the model based on the reduced variable set outperforms the full-spectrum model. It appears that the combination of NIR spectroscopy, joint mutual information, and partial least-squares discriminant analysis is a potential method for the classification of metronidazole from different brands and can, therefore, be used in the screening of counterfeit pharmaceutical products. [ABSTRACT FROM AUTHOR]

The determination of aromatic compounds is of importance in many fields. Even if these compounds may be determined by molecular fluorescence and second-order calibration, the methods are relatively complex and require higher mathematics. Here is explored whether the reduction of the dimensions, i.e., transforming two-dimensional data to one-dimensional data followed by a special variable selection and calibration process, is feasible for this purpose. The present work investigated the feasibility of combining competitive adaptive reweighted sampling and elastic component regression for the direct evaluation of the spectra to quantify three components simultaneously. Model population analysis has also been performed. The results confirmed the feasibility of the procedure, which may serve as an alternative method to simultaneously determine aromatics. [ABSTRACT FROM AUTHOR]

The authenticity of Chinese liquor concerns consumer health and economic issues. The traditional characterization methods are time-consuming and require experienced analysts. The use of near-infrared (NIR) spectroscopy and chemometrics to classify Chinese liquor samples was investigated using 128 liquors. The spectral region between 5340 cm−1and 7400 cm−1was found to be most informative. Principal component analysis was employed to characterize liquor and principal components were extracted as inputs of training classifiers. Several supervised pattern recognition methods including K-nearest neighbor, perceptron, and multiclass support vector machine were used as algorithms of constructing classifiers. The initial principal components and all spectral variables were used as the input of training models. In terms of the misclassification ratio, the support vector machine approach was the most accurate. The results indicated that near-infrared spectroscopy and chemometrics are an alternative to conventional methods for the characterization of liquor. [ABSTRACT FROM AUTHOR]