Due to the differences in raw materials and production processes, the quality of the same type of drug produced by different manufacturers is different. In drug supervision, determining the manufacturer can help to trace drug quality issues. In this study, a method for the quick identification of drug manufacturers based on laser-induced breakdown spectroscopy (LIBS) was proposed for the first time. We obtained the LIBS spectra from 12 samples of three types of penicillin (phenoxymethylpenicillin potassium tablets, amoxicillin capsules, and amoxicillin and clavulanate potassium tablets) produced by 10 manufacturers. The LIBS characteristic lines of the three types of penicillin were ranked by importance based on the decrease in the Gini index of random forest (RF). Three classifiers—the linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN)—were used to identify the different manufacturers of the three types of penicillin. RF-ANN provided the best classification result and an accuracy of 100% in penicillin manufacturer identification. The results show that LIBS combined with chemometrics could be used in the identification of penicillin manufacturers, and this method has application potential in drug quality supervision.
Early-stage detection of tumors helps to improve patient survival rate. In this work, we demonstrate a novel discrimination method to diagnose the gastrointestinal stromal tumor (GIST) and its healthy formalin fixed paraffin embedded (FFPE) tissues by combining chemometric algorithms with laser-induced breakdown spectroscopy (LIBS). Chemometric methods which include partial least square discrimination analysis (PLS-DA), k-nearest neighbor (k-NN) and support vector machine (SVM) were used to build the discrimination models. The comparison of PLS-DA, k-NN and SVM classifiers shows an increase in accuracy from 94.44% to 100%. The comparison of LIBS signal between the healthy and infected tissues shows an enhancement of calcium lines which is a signature of the presence of GIST in the FFPE tissues. Our results may provide a complementary method for the rapid detection of tumors for the successful treatment of patients.
Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.
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