Spectrophotometric Determination of Ternary Mixtures of Thiamin, Riboflavin and Pyridoxal in Pharmaceutical and Human Plasma by Least-Squares Support Vector Machines

Abstract

Vitamins are essential compounds in living systems; they differ in their chemical structure and physiological action. Analytical methods have been developed for vitamin identification and/or quantification, using a wide variety of strategies. Thus, the food and pharmaceutical industries have taken advantages of these reliable methods and used them for the estimation of vitamins from simple to complex matrices. Mixtures of vitamins such as vitamin B complex 1,2 and multivitamins in tables and other pharmaceutical formulations are used in the treatment of several diseases.Therefore, the simultaneous determination of mixtures of vitamins is very useful in the pharmaceutical industry. Several methodologies have been developed for the determination of vitamins in different samples, such as liquid chromatography, 3-5 high performance liquid chromatography, 6 micellar electrokinetic capillary chromatography 7 and spectrophotometry and derivative methods. 8,9 The greatest difficulties with simultaneous spectrophotometric determination methods arise when the analytes to be determined give partly or fully overlapped spectra, as is the case with the ingredients of most pharmaceutical preparations. Nowadays, the combination of chemometrics methods with the computer controlled instruments to monitor the molecular absorption spectra creates powerful methods in multicomponent analysis. 10 Partial least squares regression (PLSR) is the most commonly used multivariate calibration method. It is based on linear models and is used as a satisfactory solution in most cases where a linear relationship is present between the spectra and the property to be determined (concentration, for example). 10 However, PLSR is not always the best option, especially in situations where a nonlinear model is clearly required. Theory and application of PLS have been discussed by several workers. [10][11][12][13][14][15][16][17][18] A support vector machine (SVM) is an algorithm from the machine learning community, developed by Cortes and coworker.19 Due to its remarkable generalization performance, SVM has attracted attention and gained extensive application in pattern recognition and regression problems. 20 SVM maps input data into a high dimensional feature space where it may become linearly separable by a hyperplane. One reason that SVM often performs betters than other methods is that SVM was designed to minimize structural risk; such a design has been shown to be superior to the traditional empirical risk minimization principle employed by conventional neural networks.Especially, Suykens and coworker 21,22 proposed a modified version of SVM called least-squares SVM (LS-SVM), which resulted in a set of linear equations instead of a quadratic programming problem, which can extend the applications of the SVM. There exist a number of excellent introductions of SVM. [23][24][25][26][27][28][29][30][31][32][33][34] The theory of LS-SVM has also been described clearly by Suykens et al. 21,22 Applications of LS-SVM in quantification and classification ha...

Water-Soluble Photoluminescent Adenosine-Functionalized Gold Nanoclusters as Highly Sensitive and Selective Receptors for Riboflavin Detection in Rat Brain

“…The linear ranges and the limit of detection are 0.005–0.1 and 0.002 nM, respectively. To the best of our knowledge, the LOD value for RF is far superior to those values given from other methods. − ,− The selectivity of the photoluminescence sensor is challenged by some potentially interfering substances that possibly exist in the rat brain, such as metal ions, anions, amino acids, glucose, nicotinamide, uric acid, ascorbic acid, folic acid, dopamine, glutathione (GSH), bovine serum albumin (BSA), or hydrogen peroxide (H 2 O 2 ). The photoluminescence spectra of Ade-AuNCs without and with RF (0.05 nM) or each interferent (0.05 nM) are displayed in Figure A, and the selectivity plot is shown in Figure B–E.…”

“…Up to now, several methods have been developed for in vitro riboflavin detection, including high-performance liquid chromatography, 5 voltammetry, 6 spectrophotometry, 7 and fluorimetry. 8 However, it is difficult to apply these methods for fast, selective, and sensitive detection of cerebral riboflavin due to the intrinsic high chemical complexity of the cerebral system.…”

Chemometrics and Related Fields in Python

“…Classification, 98 especially supervised classification, identifies category membership assuming two or more classes, i.e., binary classification or multiclass classification. Many algorithms have been applied to the problem, e.g., linear discriminant analysis (LDA), 99,100 kernel approximation, 101 k-nearest neighbors, 102 naive Bayes, 103 support vector machine (SVM), [104][105][106][107] random forest (RF), 108,109 neural network (NN), [110][111][112][113] deep learning (DL), 12,114,115 etc. These algorithms are mainly enclosed in the scikit-learn.…”

APPLICATION OF MULTIVARIATE IMAGE ANALYSIS IN QSPR STUDY OF pKa OF VARIOUS ACIDS BY PRINCIPAL COMPONENTS-LEAST SQUARES SUPPORT VECTOR MACHINE

“…LS-SVM encompasses similar advantages as SVM, but its additional advantage is that it requires solving a set of only linear equations (linear programming), which is much easier and computationally more simple. Some application of LS-SVM in quantification and QSAR reported by different researchers in literature [8][9][10][11] .…”