Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

FPGA-based CCD signal acquisition and transmission system design.

In order to facilitate the analysis and processing of optical signals, an FPGA-based CCD signal acquisition and data transmission system is designed in this work. The system uses an FPGA as the main control device, the TCD1304DG/AP chip as the optical signal detector, and the CYUSB3KIT-003 development board product by Cypress for data transmission. Verilog and Python languages are employed for modular design and on-board verification. Through the coordination of each module, the system successfully achieves CCD signal data acquisition and transmission.