Application of Laser-Induced Breakdown Spectroscopy Combined with Chemometrics for Identification of Penicillin Manufacturers

Abstract

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.

Discrimination of rocks by laser-induced breakdown spectroscopy combined with Random Forest (RF)

“…There are three approaches to measuring the value of variable importance: Shapley Variable Importance, Permutation Variable Importance and Gini Variable Importance. 38 In this work, Gini Variable Importance was calculated to rank the important variable of spectral data. The weighted sum of the decreases in impurity for all the nodes is the Gini Variable Importance.…”

Artificial neural network for high-throughput spectral data processing in LIBS imaging: application to archaeological mortar

“…As mentioned above, ANNs have been widely used for LIBS data analysis both for qualitative and quantitative purposes in many fields, 25 soil study 26 and pharmaceutical industry for example. 17 ANNs are multivariate models that can process data within a short period of time and whose operation is inspired by biological neurons. The elementary brick of an ANN is an artificial neuron.…”

“…ANN is well known to the LIBS community and it has already been largely explored through various applicative cases either for qualitative or quantitative purposes, but not in the frame of imaging. [15][16][17][18] Advantages of ANN are well known and include fast response time (possible implementation in real time), low sensitivity to noise, high robustness, and accurate prediction capabilities. The use of an ANN to automate micro-LIBS imaging processing can address most of the current issues faced in the processing, which include: a large dataset (>1 million spectra, single-shot spectrum which oen experiences noise, "mixed" spectra (spectral interferences between 2 or more elements)), and spectral variability due to laser shot-toshot uctuations and long period of analysis.…”

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

“…The 3D high-resolution chemical characterisation of sputtered lithium-rich NMC811 (Ni 0.8 Mn 0.1 Co 0.1 O 2 ) thin lms using TOF-SIMS was reported by Priebe et al 211 The surface passivation layer and buried structure were analysed using a primary ion beam of 69 Ga + with an energy of 20 keV and with the detector in positive ion mode. The techniques of XRD (for crystal structure), SEM and FTIR were also used for characterisation.…”