[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.

Determination of daunomycin in human plasma and urine by using an interference-free analysis of excitation-emission matrix fluorescence data with second-order calibration.

Daunorubicin (DNR) is a significant antineoplastic antibiotic, which is usually applied to a chemotherapy of acute lymphatic and myelogenous leukaemia. Unfortunately, cardiotoxicity research in animals has indicated that DNR is cardiotoxic. Therefore, it is important to quantify DNR in biological fluids. A new algorithm, the alternating fitting residue (AFR) method, and the traditional parallel factor analysis (PARAFAC) have been utilized to directly determine DNR in human plasma and urine. These methodologies fully exploit the second-order advantage of the employed three-way fluorescence data, allowing the analyte concentrations to be quantified even in the presence of unknown fluorescent interferents. Furthermore, in contrast to PARAFAC, more satisfactory results were gained with AFR.

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.

Estimating the chemical rank of three-way data arrays by a simple linear transform incorporating Monte Carlo simulation.

Estimating an appropriate chemical rank of a three-way data array is very important to second-order calibration. In this paper, a simple linear transform incorporating Monte Carlo simulation approach (LTMC) to estimate the chemical rank of a three-way data array was suggested. The new method determines the chemical rank through performing a simple linear transform procedure on the original cube matrix to produce two subspaces by singular value decomposition. One of two subspaces is derived from the original three-way data array itself and the other is derived from a new three-way data array produced by the linear transformation of the original one. Projection technique incorporating the Monte Carlo approach acts as distinguishing criterion to choose the appropriate component number of the system. Simulated three-way trilinear data arrays with different noise types (homoscedastic and heteroscedastic), various noise level as well as high collinearity are used to illustrate the feasibility of the new method. The results have shown that the new method could yield accurate results with different conditions appended. The feasibility of the new method is also confirmed by two real arrays, HPLC-DAD data and excitation-emission fluorescent data. All the results are compared with the other three factor-determining methods: factor indicator function (IND), core consistency diagnostic (CORCONDIA) and two-mode subspace comparison (TMSC) approach. It shows that the newly proposed algorithm can objectively and quickly determine the chemical rank to fit the trilinear model.

Interference-free determination of fluoroquinolone antibiotics in plasma by using excitation-emission matrix fluorescence coupled with second-order calibration algorithms.

Fluoroquinolones or so-called second-generation quinolones, in particular, ofloxacin (OFL), norfloxacin (NOR), and enoxacin (ENO), with therapeutic advantages possess strongly overlapped fluorescence spectra. In this paper, two strategies were proposed for simultaneous direct determination of OFL, NOR and ENO in plasma by combining fluorescence excitation-emission matrix (EEM) with second-order calibration based on the alternating trilinear decomposition algorithm (ATLD) and parallel factor analysis (PARAFAC). The results showed that both algorithms could solve the problem of serious fluorescence spectral overlapping of the sought-for analytes even in the presence of uncalibrated interferents. However, ATLD has advantages of being insensitive to overestimated component number and fast convergence. The results by using ATLD with an estimated component number of five were reasonably acceptable for clinical analysis. The average recoveries of OFL, NOR and ENO in synthetic samples were 99.7+/-2.4, 101.5+/-2.4 and 97.3+/-3.8%, respectively; the average recoveries of OFL, NOR and ENO in complex plasma were 94.3+/-2.6, 85.6+/-3.3 and 103.3+/-3.0%, respectively.