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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.

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.

A novel algorithm, alternating penalty quadrilinear decomposition (APQLD), is developed as an extension of alternating penalty trilinear decomposition (APTLD) for decomposition of quadrilinear data and applied to third-order calibration. The proposed method as well as four-way parallel factor analysis (PARAFAC) not only retains the second-order advantages possessed in second-order calibration but also holds additional advantage, for example with trilinear data from one sample, the intrinsic profiles in each order can be determined uniquely for each species in the sample. From simulations, it is observed that another advantage is that the introduction of fourth mode can relieve the serious problem of collinearity. It can be defined the ‘third-order advantage’. It was shown a much higher convergence rate compared with four-way PARAFAC. Moreover, it is generally insensitive to the overestimates of the component number chosen. This offers the advantage that in third-order calibration one need not pay much attention to determining a proper component number for the model, and it is difficult for four-way PARAFAC to avoid it. By treating simulated and one real excitation-emission-pH data sets, the results indicated that both APQLD and PARAFAC work well, but the performance of APQLD is better than that of PARAFAC in the prediction of concentration even if the component number chosen is the same as the actual number of underlying factors in the real system. Copyright © 2007 John Wiley & Sons, Ltd.

Binding affinities of a series of substituted imidazobenzodiazepines for the a6 Benzodiazepine Receptor (BzR) isoform are investigated by the Optimized Blockwise Variable Combination (OBVC) by Particle Swarm Optimization (PSO) based on Partial Least Squares (PLS) modeling. The QSAR analysis result showed that MolRef, AlogP, MR CM**-3 , Rotatable bonds (Rotlbonds), Hydrogen Bond Acceptors (Hbond acceptor), five Jurs descriptors, two Shadow indices descriptors and principal moment of inertia are the most important descriptors among all the investigated descriptors. One can change the molar refractivity, the polar interactions between molecules, the shape of the molecules, the principal moments of inertia about the principal axes of a molecule, the hydrophobic character of the molecule, the number of Rotlbonds and Hbond acceptors of the compounds to adjust the binding affinities of imidazobenzodiazepine for the a6 BzR isoform. The Quantitative Structure-Activity Relationship (QSAR) analysis result was also compared with MLR, PLS, and hierarchical PLS algorithms. It has been demonstrated that OBVC by PSO for PLS modeling shows satisfactory performance in the QSAR analysis.

An alternating asymmetric trilinear decomposition for three-way data arrays analysis (AATLD) method was introduced. The new proposed algorithm combines the merit of Three-way Alternating Least Squares (Tri-ALS) and Alternating Trilinear Decomposition (ATLD). It retains the second-order advantage of quantification for analyte(s) of interest even in the presence of potentially unknown interferents. As an asymmetric trilinear decomposition, AATLD can perform well when three-way data arrays possess serious collinearity problem. Simulated and real high-performance liquid chromatography data arrays were used to demonstrate these advantages of the algorithm. In contrast with traditional PARAFAC, ATLD and Tri-ALS, the new proposed algorithm performs better when the data are high collinear, e.g., the large condition number of the loading matrices A, B and C. Even with heavily collinear simulated data set, it was also found that the AATLD algorithm is faster than others on obtaining solutions with chemical meaning.

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.

Determining the rank of a trilinear data array is a first step in subsequent trilinear component decomposition. Different from estimating the rank of bilinear data, it is more difficult to decide the significant number of component to fit the trilinear decompositions exactly. General methods of rank estimation utilize the information contained in the singular values but ignore information from eigenvectors. In this paper, a rank estimating method specifically for trilinear data arrays is proposed. It uses the idea of direct trilinear decomposition (DTLD) to compress the cube matrix into two pseudo sample matrices which are then decomposed by singular value decomposition. Two eigenvectors combined with the projection technique are used to estimate the rank of trilinear data arrays. Simulated trilinear data arrays with homoscedastic and heteroscedastic noise, different noise levels, high collinearity, and real three-way data arrays have been used to illustrate the feasibility of the proposed method. Compared with other factor-determining methods, for example use of the factor indication function (IND), residual percentage variance (RPV), and the two-mode subspace comparison approach (TMSC), the results showed that the new method can give more reliable answers under the different conditions applied.

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.