Non-destructive determination of metronidazole powder by using artificial neural networks on short-wavelength NIR spectroscopy

Development and Optimization of HPLC Analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in Pharmaceutical Dosage Forms Using Experimental Design

A new simple, sensitive, rapid and accurate gradient reversed-phase high-performance liquid chromatography with photodiode array detector (RP-HPLC-DAD) was developed and validated for simultaneous analysis of Metronidazole (MNZ), Spiramycin (SPY), Diloxanidefuroate (DIX) and Cliquinol (CLQ) using statistical experimental design. Initially, a resolution V fractional factorial design was used in order to screen five independent factors: the column temperature (°C), pH, phosphate buffer concentration (mM), flow rate (ml/min) and the initial fraction of mobile phase B (%). pH, flow rate and initial fraction of mobile phase B were identified as significant, using analysis of variance. The optimum conditions of separation determined with the aid of central composite design were: (1) initial mobile phase concentration: phosphate buffer/methanol (50/50, v/v), (2) phosphate buffer concentration (50 mM), (3) pH (4.72), (4) column temperature 30°C and (5) mobile phase flow rate (0.8 ml min^-1). Excellent linearity was observed for all of the standard calibration curves, and the correlation coefficients were above 0.9999. Limits of detection for all of the analyzed compounds ranged between 0.02 and 0.11 μg ml^-1; limits of quantitation ranged between 0.06 and 0.33 μg ml^-1 The proposed method showed good prediction ability. The optimized method was validated according to ICH guidelines. Three commercially available tablets were analyzed showing good % recovery and %RSD.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer.

Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm

The feasibility of rapid determination of the process variables (i.e. pH and moisture content) in solid-state fermentation (SSF) of wheat straw using Fourier transform near infrared (FT-NIR) spectroscopy was studied. Synergy interval partial least squares (siPLS) algorithm was implemented to calibrate regression model. The number of PLS factors and the number of subintervals were optimized simultaneously by cross-validation. The performance of the prediction model was evaluated according to the root mean square error of cross-validation (RMSECV), the root mean square error of prediction (RMSEP) and the correlation coefficient (R). The measurement results of the optimal model were obtained as follows: RMSECV=0.0776, R(c)=0.9777, RMSEP=0.0963, and R(p)=0.9686 for pH model; RMSECV=1.3544% w/w, R(c)=0.8871, RMSEP=1.4946% w/w, and R(p)=0.8684 for moisture content model. Finally, compared with classic PLS and iPLS models, the siPLS model revealed its superior performance. The overall results demonstrate that FT-NIR spectroscopy combined with siPLS algorithm can be used to measure process variables in solid-state fermentation of wheat straw, and NIR spectroscopy technique has a potential to be utilized in SSF industry.