Phosphorus quantification in soil using LIBS assisted by laser-induced fluorescence

A novel system for precise identification and explainability analysis based on multimodal learning combining laser-induced breakdown spectroscopy and laser-induced plasma acoustic signals

This study presents an innovative approach to identify copper types using Laser-Induced Breakdown Spectroscopy (LIBS) in conjunction with Laser-Induced Plasma Acoustic (LIPA) signals. Traditionally overlooked, plasma acoustic signals can indeed provide valuable insights into plasma characteristics essential for copper identification. This study pioneers a cross-modal learning technique, integrating LIBS and LIPA signals, and employs a Support Vector Machine (SVM) for classification. To enhance feature extraction, Principal Component Analysis (PCA) reduces data dimensionality, while SHapley Additive exPlanations (SHAP) assess feature contributions, aiding feature selection. The combined model demonstrates high identification accuracy, and the interpretability analysis deepens our understanding of feature roles in copper detection. This framework not only boosts LIBS-based identification accuracy but also advances the theoretical foundation for multi-modal data fusion in material analysis.

"Raman plus X" dual-modal spectroscopy technology for food analysis: A review

Raman spectroscopy, a nondestructive optical technique that provides detailed chemical information, has attracted growing interest in the food industry. Complementary spectroscopic methods, such as near-infrared (NIR) spectroscopy, nuclear magnetic resonance (NMR), terahertz (THz) spectroscopy, laser-induced breakdown spectroscopy (LIBS), and fluorescence spectroscopy (Flu), enhance Raman spectroscopy's capabilities in various applications. The integration of Raman with these techniques, termed "Raman plus X," has shown significant potential in agri-food analysis. This review highlights the latest advances and applications of dual-modal spectroscopy methods combining Raman spectroscopy with NIR, NMR, THz, LIBS, and Flu in food analysis. Key applications include detecting harmful contaminants, evaluating food quality, identifying adulteration, and characterizing structure. The synergistic use of Raman-based dual-modal spectroscopy provides more comprehensive information and improves modeling accuracy compared to single techniques. The review also explores the role of data fusion in multisource spectral analysis and discusses challenges and prospects of "Raman plus X," including the development of integrated hardware and advanced data fusion algorithms. These advancements aim to streamline multisource data analysis, offering valuable insights to select appropriate analytical methods for practical applications in the food industry.

Laser-induced voltage of table salt for deep ultraviolet pulsed laser detection

To meet the requirements of fast response and simple process of deep ultraviolet (UV) pulsed laser detector, table salt (TS) was used as laser detection material in combination with a variable resistor to achieve single-pulse laser detection. Under the irradiation of a KrF excimer laser, the laser-induced voltage (LIV) of TS was influenced by the dynamic process of laser-induced plasma, and the whole process was well fitted with the sum of the three exponential functions. As the applied bias voltage (Vb) and incident laser energy (Ein) increased, the LIV amplitude (Vp) increased and the response time decreased. When the variable resistor (R) was reduced to 14.7 Ω, the response time of LIV decreased from ∼300 μs to ∼20 ns, which is the same as the duration of laser pulse. This research provided a simple, low-cost, and fast method for the detection of UV single-pulse laser based on the laser-TS interaction.