Using partial least squares and principal component regression in simultaneous spectrophotometric analysis of pyrimidine bases

Rapid simultaneous analysis of anti human immunodeficiency virus drugs in pharmaceutical formulation by smart spectrophotometry based on multivariate calibration and least squares support vector machine methods

In this study, two chemometrics methods, including partial least squares regression (PLS) and least squares support vector machine (LS-SVM) were applied for the simultaneous determination of zidovudine (ZDV) and lamivudine (LMV) in synthetic mixtures and anti-HIV pharmaceutical formulation. These approaches along with the spectrophotometric method were used to solve spectral overlapping problems between mentioned components. The results of PLS showed that the number of components for ZDV and LMV were 10 and 10 with mean square prediction error (MSPE) of 0.4045 and 2.1189, respectively. This method revealed recoveries ranging from 99.48% to 100.40% and 99.55% to 101.25% for ZDV and LMV, respectively. By applying leave-one-out cross-validation (LOO-CV), γ (regularization parameter) and σ² (width of the function) values were found to be 50, 1500 and 210, 20 with root mean square error (RMSE) of 0.6156 and 0.3163 for ZDV and LMV, respectively. The mean recoveries obtained by the LS-SVM were 100.82% and 98.93% for ZDV and LMV, respectively. A comparison between the suggested methods and high-performance liquid chromatography (HPLC) as a reference technique was implemented, which did not show a significant difference. The results obtained in this research revealed that the chemometrics approaches can be efficient, simple, inexpensive, and precise for routine analysis and quality control of the drug.

Non-Destructive Analysis of Chlorpheniramine Maleate Tablets and Granules by Chemometrics-Assisted Attenuated Total Reflectance Infrared Spectroscopy

Non-destructive analysis of chlorpheniramine maleate (CPM), pharmaceutical tablets, and granules was conducted by chemometrics-assisted attenuated total reflectance infrared spectroscopy (ATR-IR). For tablets, an optimum PLSR model with eight latent factors was obtained from area-normalized and standard normal variate (SNV) pretreated ATR-IR spectral data with correlation coefficients (R²) of calibration and cross-validation of 0.9716 and 0.9602, respectively. The model capability for the 42 test set samples was proven with R² between the reference and model prediction values of 0.9632, and a root-mean-square error of prediction (RMSEP) of 1.7786. The successive PLSR model for granules was constructed from SNV and first derivative pretreated ATR-IR spectral data with two latent factors and correlation coefficients (R²) of calibration and cross-validation of 0.9577 and 0.9450, respectively.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug

Spectrophotometric analysis method based on artificial neural network (ANN), partial least squares regression (PLS) and principal component regression (PCR) models was proposed for the simultaneous determination of Emtricitabine (ETB) and Tenofovir alafenamide fumarate (TAF) in human immunodeficiency virus (HIV) drug. An artificial neural network consisting of two, five, and seven layers with 2,3,5,7, and 9 neurons was trained by applying a feed forward back-propagation learning. In this method, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back propagation (GDX) algorithms were used. Statistical parameters indicated that the ability of LM was better than GDX algorithm. Also, root mean square error (RMSE) and recovery (%) of the PLS and PCR methods showed that PLS has worked better than PCR. The proposed models were compared to the high- performance liquid chromatography (HPLC) as a reference method. Furthermore, the obtained results of the one-way analysis of variance (ANOVA) test at the 95% confidence level represented that there was no significant difference between the proposed and reference methods.