Wavenumber selection based on Singular Value Decomposition for sample classification

A novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

Rapid identification of the geographical origin of Eucommia ulmoides by using excitation-emission matrix fluorescence combined with chemometric methods

Eucommia ulmoides is an important and valuable traditional Chinese medicine with various medical functions, and has been widely used as health food in China, Japan, South Korea and other Asian countries for many years. The efficacy and quality of E. ulmoides are closely associated with the geographical origin. In this work, the potential of excitation-emission matrix (EEMs) fluorescence coupled with chemometric methods was investigated for simple, rapid and accurate for identification E. ulmoides from different geographical origins. Parallel factor analysis (PARAFAC) was applied for characterizing the fluorescence fingerprints of E. ulmoides samples. Moreover, k-nearest neighbor (kNN), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA) models were used for the classification of E. ulmoides samples according to their geographical origins. The results showed that kNN model was more suitable for identification of E. ulmoides samples from different provinces. The kNN model could identify E. ulmoides samples from eight different geographical origins with 100% accuracy on the training and test sets. Therefore, the proposed method was available for conveniently and accurately determining the geographical origin of E. ulmoides, which can expect to be an attractive alternative method for identifying the geographic origin of other traditional Chinese medicines.

Selectivity of identification of compounds from the group of phosphodiesterase-5 inhibitors (PDE-5i) in falsified products from the Polish market using attenuated total reflectance Fourier transform infrared spectroscopy and X-ray powder diffraction

Falsified drugs are a growing problem and a great threat all over the world. Searching for easy, fast, not expensive and reproducible methods for identification of active pharmaceutical ingredients (APIs) is an important issue. The application of ATR-IR technique for identification of phosphodiesterase-5 inhibitors (PDE-5i) in falsified products from the Polish market has been demonstrated. Furthermore, the strategy of confirmation of the product's originality inaquick and easy way has been developed. The advantages of this application are: exceptional simplicity, very short measurement time, high selectivity inrelation to structurally similar substances and almost no sample preparation. After validation, developed strategy has been successfully introduced into routine analysis.