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.

A convenient LIBS approach was proposed to reduce the self-absorption effect of alkali and alkaline earth metal elements in soil.

The detection of manganese (Mn) in steel by laser-induced breakdown spectroscopy (LIBS) provides essential information for steelmaking. However, self-absorption greatly disrupts the LIBS spectral lines of Mn with high content. In this study, to minimize self-absorption for Mn spectral lines in LIBS, laser-induced fluorescence (LIF) was applied. Compared with conventional LIBS, the self-absorption factors (α) of Mn I 403.08, 403.31, and 403.45 nm lines were reduced by 90%, 88%, and 88%, respectively; the root mean square errors of cross-validation were decreased by 88%, 85%, and 87%, respectively; the average relative errors were reduced by 93%, 90%, and 91%, respectively; and average relative standard deviations were decreased by 29%, 32%, and 33%, respectively. The LIBS-LIF was shown to successfully minimize the self-absorption effect and spectral intensity fluctuation and improve detection accuracy.

Recently, the mode conversion of elastic waves has attracted much attention, due to its scientific significance and potential applications. The applications based on the high mode conversion efficiency were also explored in many fields. However, because of the complexity of elastic waves, the existing structures for the high efficient conversion of elastic waves are relatively complicated, and there are also some limitations in the practical design. Here, we report the extraordinary mode conversion of elastic waves through asymmetric brass plates partitioned by subwavelength cuts. It is demonstrated that high efficiencies (90%) and one-way conversions between transversal waves and longitudinal waves are achieved by the structured solid plate at the resonant frequency, which leads to the striking unidirectional transmission of elastic waves. Analyzing the resonant fields demonstrates that the intrinsic modes within the individual pieces derived by the cuts are responsible for this abnormal wave conversion. The simple scheme for wave conversion presented here may have potential applications, such as non-invasive flow sensing.

The LASSO–LightGBM–SHAP framework integrates L ₁ -regularized wavelength selection, LightGBM-based gradient boosting, and SHAP interpretation to achieve accurate and interpretable multi-element quantification from complex LIBS spectra.