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.
As a popular food, Chinese yam (CY) powder is widely used for healthy and commercial purposes. Detecting adulteration of CY powder has become essential. In this work, chemometric methods combined with laser-induced breakdown spectroscopy (LIBS) were developed for identification and quantification of CY powder adulteration. Pure powders (CY, rhizome of winged yam (RY) and cassava (CS)) and adulterated powders (CY adulterated with CS) were pressed into pellets to obtain LIBS spectra for identification and quantification experiments, respectively. After variable number optimization by principal component analysis and random forest (RF), the best model random forest-support vector machine (RF-SVM) decreased 48.57% of the input variables and improved the accuracy to 100% in identification. Following the better feature extraction method RF, the Gaussian process regression (GPR) method performed the best in the prediction of the adulteration rate, with a correlation coefficient of prediction (Rp2) of 0.9570 and a root-mean-square error of prediction (RMSEP) of 7.6243%. Besides, the variable importance of metal elements analyzed by RF revealed that Na and K were significant due to the high metabolic activity and maximum metal content of CY powder, respectively. These results demonstrated that chemometric methods combined with LIBS can identify and quantify CY powder adulteration accurately.
The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C2 molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.
Introduction: Nowadays, the widespread use of plastic products has significantly contributed towards environmental pollution caused by waste plastics. Laser-induced breakdown spectroscopy (LIBS), an emerging spectroscopic technology, has shown great potential for rapid sorting and recycling of plastics. However, the poor robustness of the classification model severely limits the large-scale application of LIBS technology in plastic sorting and recycling.Methods: In this research, we used spectral preprocessing combined with feature selection to improve the robustness of the support vector machine (SVM) classification model for four typical plastic samples (ABS, nylon, 3240, and its modified product FR-4). LIBS spectral data were collected under different experimental conditions, then we defined robustness over time (ROT), robustness over time and different focusing lenses (ROT&RFL), and robustness over time and different manufacturers (ROT&RDM) to assess model performance. The feature importance of the preprocessed spectra was evaluated using the Relief-F algorithm, and the maximum accuracy of the validation set was 92.6% when inputting the first 19 most important features. Eventually, the optimal model was used for the prediction of the test set.Results and discussion: The ROT of the original spectrum, spectrum preprocessing, and spectral preprocessing combined with feature selection were 58.4%, 79.1%, and 98.47%, respectively. Similarly, ROT&RFL for the same methods were 65.54%, 75%, and 95.25%, respectively. ROT&RDM were 65.5%, 67%, and 93.92%, respectively. The results demonstrate that spectral preprocessing combined with feature selection can significantly improve the robustness of the classification model, and the proposed method is feasible for plastic sorting and recycling.
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