[Discrimination of Honey Varieties Based on Amino Acid Derivative Fluorescence Method Combining with Multilinear Pattern Recognition].

Assessing the authenticity about the botanical and geographical origins of food is an important content for food safety research. Amino acids are the most important nutrients of honey. The types and contents of amino acids are different in various honey samples. Thus they can be used as one of the important parameters to discriminate the honey variety and quality. In this article, amino acids in honey were first derived with formaldehyde and acetyl acetone solution. In the following step, three-dimensional fluorescence spectrums combing with multidimensional pattern recognition methods were used to distinguish the kinds of honey. Five kinds of honey (total 150 honey samples) from different botanicals were studied in this research. Before fluorescence detection, the effect of the amount of derivation reagent, the time of reaction, temperature and pH to the derivation progress of honey samples were first studied. Research showed that the fluorescence intensity of derivatives of honey was the strongest when the amount of derivation reagent was 4.0 mL, the time of reaction being 2 h, pH being 4 and the temperature being 100 ℃. The derivatives of honey were then scanned with three-dimensional fluorescence spectrometry. The collection of fluorescence intensity values occurred within excitation-emission ranges of 300~500 and 380~580 nm. A 150×41×101 cube matrix data sets can be acquired. The three-dimensional fluorescence data sets were decomposed with multilinear pattern recognition methods, such as multilinear principal components analysis (M-PCA), self-weight alternative trilinear decomposition (SWATLD) and multilinear partial least squares discriminant analysis (N-PLS-DA) methods. All of these multilinear pattern recognition methods showed the clustering tendency for five different kinds of honey. Compared with the other two methods, N-PLS-DA got more accurate and reliable classification results because it made full use of all the fluorescence information of the derivative honey samples. Its total recognition rate reached 88%. The result is acceptable for the complexity of the honey samples. It showed this method could be applied to identify the varieties of honey. Compared with the chromatographic analysis method, this method is relatively simpler and more sensitivity. It avoided the chromatographic separation and reduced the consumption of organic solvent. Thus it can be regarded as a kind of relatively green honey classification method. This research will provide a new idea to directly fluorescence analyze for no or weak fluorescence natural substances.

Detection of adulteration in acetonitrile using near infrared spectroscopy coupled with pattern recognition techniques.

In this paper, near infrared spectroscopy (NIR) in cooperation with the pattern recognition techniques were used to determine the type of neat acetonitrile and the adulteration in acetonitrile. NIR spectra were collected between 400 nm and 2498 nm. The experimental data were first subjected to analysis of principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Then support vector machine (SVM) were applied to develop classification models and the best parameter combination was selected by grid search. Under the best parameter combination, the classification accuracy rates of three types of neat acetonitrile reached 87.5%, and 100% for the adulteration with different concentration levels. The results showed that NIR spectroscopy combined with SVM could be utilized for determining the potential adulterants including water, ethanol, isopropyl alcohol, acrylonitrile, methanol, and by-products associated with the production of acetonitrile.

Simultaneous and Direct Determination of Vancomycin and Cephalexin in Human Plasma by Using HPLC-DAD Coupled with Second-Order Calibration Algorithms.

A simple, rapid, and sensitive method for the simultaneous determination of vancomycin and cephalexin in human plasma was developed by using HPLC-DAD with second-order calibration algorithms. Instead of a completely chromatographic separation, mathematical separation was performed by using two trilinear decomposition algorithms, that is, PARAFAC-alternative least squares (PARAFAC-ALSs) and self-weight-alternative-trilinear-decomposition- (SWATLD-) coupled high-performance liquid chromatography with DAD detection. The average recoveries attained from PARAFAC-ALS and SWATLD with the factor number of 4 (N = 4) were 101 ± 5% and 102 ± 4% for vancomycin, and 96 ± 3% and 97 ± 3% for cephalexininde in real human samples, respectively. The statistical comparison between PARAFAC-ALS and SWATLD is demonstrated to be similar. The results indicated that the combination of HPLC-DAD detection with second-order calibration algorithms is a powerful tool to quantify the analytes of interest from overlapped chromatographic profiles for complex analysis of drugs in plasma.