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Aiming to tackle the problems of low classification accuracy, difficult data processing, long analysis time, and the influence of subjective consciousness of traditional Baijiu (Chinese liquor), this paper reports a method based on the combination of laser-induced breakdown spectroscopy with high-frequency ultrasonic atomization system (AS-LIBS) and optimized grid search cross validation (Opt-GSCV) for high-precision and rapid classification of Baijiu of different dimensions. Multi-dimensional high-precision classification of 23 Baijiu samples with different series, degrees, and production dates from Shanxi Xinghuacun Fen Wine Factory Co., Ltd was performed. By studying the growth and evolution mechanism of plasma generated by laser excitation, a new method of Baijiu spectral signal acquisition was realized by combining LIBS with Baijiu detection. Aiming at addressing the shortcomings of high breakdown thresholds and poor signal stability due to the direct breakdown of Baijiu with high ethanol content, a high-frequency atomization system based on ultrasonic technology is proposed to realize the efficient extraction of Baijiu plasma spectral signals. With regard to the selection of the number of nodes, weights and thresholds of the implicit layer of the back-propagation (BP) network, an optimization model based on the combination of the particle swarm optimization (PSO) algorithm combined with the genetic algorithm (GA) is proposed to realize the construction of the BP optimal network architecture. By constructing a secondary optimization seeking network model based on OptGSCV, the technical problem of mismatch between optimal parameter selection and optimal network construction is solved, and a new network integrating an intelligent qualitative analysis algorithm and a traditional classification algorithm is realized. The experimental results show that the proposed OptGSCV quadratic optimization network combined with the AS-LIBS multidimensional Baijiu high-precision classification model not only introduces an innovative and high-efficiency detection method for the Baijiu industry, but also provides important technical support and new ideas for the key areas of food safety production and Baijiu quality control. [ABSTRACT FROM AUTHOR]

Focusing on the problems of complicated sample processing and long detection time of traditional soil composition testing in mining areas, this paper proposes a rapid soil classification method of grid search and cross validation (GSCV) quadratic optimization-seeking network combined with magnetic field enhanced laser-induced breakdown spectroscopy (MF-LIBS), which can quickly and accurately classify standard soil samples from 10 different mining areas. First, the effects of different magnetic field strengths on the enhancement factor, signal-to-noise ratio (SNR) and plasma electron temperature of the characteristic spectral lines were investigated. Next, 5400 12 248-dimensional spectral data under the 0.98 T magnetic field constraint were preprocessed using principal component analysis (PCA). Then the back propagation (BP) network was optimized by combining the sparrow search algorithm (SSA) external optimization network with the genetic algorithm (GA) internal optimization network. Finally, the hyperparameters of the optimal network architecture are refined by GSCV for secondary optimization. By comparing with the unoptimized network, the results show that the classification accuracy of GSCV-SSA-GA-BP is 0.99997, the precision is 0.99977, the recall is 0.99981, and the F1-score is 0.99979. The proposed technique of GSCV quadratic optimization network combined with MF-LIBS soil classification can not only play an important role in soil research, but also provide a new scheme for rapid detection and high-precision qualitative analysis of soil components. [ABSTRACT FROM AUTHOR]

The pollution of the surrounding soil can be caused by the development of heavy metal mining areas, which can do a significant harm to the ecological environment and human health. The classification of soil in different mining areas is of great significance for soil management and environmental pollution control. Due to the complex physical property and composition of soil, it is easily affected by matrix effects, which can directly affect the collection and processing of spectral data. It is thus challenging to accurately classify soil. In this article, laser-induced breakdown spectroscopy (LIBS) combined with a convolutional neural network with a two-dimensional (2D-CNN) algorithm, which was based on principal component analysis (PCA), was used to accurately identify and classify standard soil samples from ten different mining areas. The sample data were first standardized and dimensionlessly pre-processed by PCA to remove redundant information and to improve the signal-to-noise ratio (SNR) of the data. Then, the structure of 2D-CNN was designed, and the features of the input data were deeply extracted twice by convolution and pooling operations, so that more feature information could be extracted from the data, which was conducive to improving the classification accuracy of the model. The experiment results show that the classification accuracy of the 2D-CNN test set could reach 99.60% and the loss could be minimized at 0.012. In order to verify the superior performance of the 2D-CNN-based PCA classification model, the 2D-CNN algorithm was compared with other models, including back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The results show that 2D-CNN has great potential in the field of soil recognition and classification, and provides a new and reliable data processing method for LIBS to classify materials with similar chemical properties. [ABSTRACT FROM AUTHOR]