Prediction of black, immature and sour defective beans in coffee blends by using Laser-Induced Breakdown Spectroscopy

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.

Application and Research Progress of Laser-Induced Breakdown Spectroscopy in Agricultural Product Inspection

The quality and safety of agricultural products are of paramount importance in ensuring the health of the food supply chain. Additionally, the composition and trace elements in agricultural products significantly influence their quality and nutritional value. Therefore, the need for rapid and accurate analysis techniques for agricultural product composition is particularly crucial. In the current landscape of evolving compositional analysis technologies, Laser-Induced Breakdown Spectroscopy (LIBS) technology is emerging as a promising analytical tool with broad applications in agricultural product testing. Its characteristics of being rapid, real-time, and capable of simultaneous detection of multiple elements provide an efficient and reliable means for assessing the quality, monitoring safety, and tracing the origin of agricultural products. This technology is expected to play a significant role in controlling and managing the agricultural industry chain and can offer consumers safer and healthier agricultural products. This paper provides an overview of the research status and recent developments of LIBS technology in agricultural product testing applications in recent years. Based on the current research landscape, challenges and opportunities of applying LIBS technology in fields such as agricultural product quality and safety assessment, soil analysis, assessment of crop nutrition, detection of plant diseases, and identification of agricultural product varieties have been evaluated. Moreover, recommendations for further expanding the application of LIBS technology in the agricultural sector are proposed.