Cavity-constrained LIBS combined with the gray wolf optimization algorithm for optimizing bidirectional long short-term memory (GWO-BiLSTM) networks for classification prediction of different brands of cigarettes

As a kind of plant with complex chemical composition, the different compositions of tobacco determine the quality of tobacco, which in turn determines the quality of its cigarette products, so high-precision and rapid identification of different brands of cigarettes is of great significance for combating the market of counterfeit and shoddy cigarettes and safeguarding people's life and health. Traditional cigarette detection methods are time-consuming and subjective, and the analysis results are not objective and precise enough, whereas this study proposes a combination of cavity-constrained laser-induced breakdown spectroscopy (LIBS) and gray wolf optimization algorithm optimized bidirectional long short-term memory (GWO-BiLSTM) networks for classifying and identifying cigarette samples of 10 different brands. The signal-to-noise ratio and enhancement factor of the spectral intensity signal, LIBS plasma temperature and density are compared for different sizes of cavity constraints, and an optimal spectral enhancement size of 5 mm in both cavity height and diameter is selected. Comparing four different spectral downscaling methods, namely, principal component analysis (PCA), robust principal component analysis (RPCA), linear discriminant analysis (LDA), and t-distribution-stochastic neighborhood embedding (t-SNE), the LDA downscaling model is selected to achieve effective downscaling of the LIBS spectral data. By comparing the classification performance of the three models, the long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network, and GWO-BiLSTM network, the GWO-BiLSTM model can achieve a classification accuracy of up to 98.31% in the test set. The results show that the classification method for different brands of cigarettes proposed in this study can effectively solve the technical pain points of traditional tobacco detection methods and provide a technical means to prevent the circulation of counterfeit cigarettes.