Laser-Induced Breakdown Spectroscopy-Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma.

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model's robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique.

BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed. RESULTS: We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples. SIGNIFICANCE AND NOVELTY: In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Investigation of ultra wide bandgap Flouro-perovskite materials RBeF3 (R[bond, double bond]K and Li) for smart window applications: A DFT study.

The human body is affected by ultraviolet radiation because it can penetrate and harm bodily cells. Although skin cancer and early aging are consequences of prolonged exposure to ultraviolet (UV) rays, sun rays signify immediate excessive exposure. In this context, some structural, optical, electrical, and mechanical properties of the beryllium-based cubic fluoro-perovskite RBeF3 (R[bond, double bond]K and Li) compounds are examined through the use of density functional theory (DFT) within generalized gradient approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) approximations (GGA-PBE). The compounds KBeF3 and LiBeF3 have space group 221-pm3m, and their lattice constants and volumes are (3.765, 3.566) Å and (53.380, 45.379) Å3, respectively, based on their structural properties. Computed results indicate that the compounds' bandgaps are 7.35 eV and 7.12 eV, respectively, with an indirect nature for KBeF3 and LiBeF3. The properties of the band structure indicate that both compounds are insulators. The bonding properties of these compounds, RBeF3, are a combination of covalent and ionic. Optical properties of the compounds are examined which reflect the light-matter interaction like reflectivity, conductivity, and absorption. These materials were likely very hard but brittle, based on a higher bulk modulus B from elastic features, the B/G ratio, Pugh's ratio, and Vickers hardness. The compound RBeF3, as determined by the findings, is used as a UV protection and reflection layer for car and room windows.