Machine learning in laser-induced breakdown spectroscopy: A review

To achieve automated rock classification and improve classification accuracy, this work discusses an investigation of the combination of laser-induced breakdown spectroscopy (LIBS) and the use of one-dimensional convolutional neural networks (1DCNNs). As a result, in this paper, an improved Bayesian optimization (BO) algorithm has been proposed where the algorithm has been applied to automatic rock classification, using LIBS and 1DCNN to improve the efficiency of rock structure analysis being carried out. Compared to other algorithms, the improved BO method discussed here allows for a reduction of the modeling time by about 65% and can achieve 99.33% and 99.00% for the validation and test sets of 1DCNN.

Automatic rock classification of LIBS combined with 1DCNN based on an improved Bayesian optimization.

This review covers advances in the analysis of advanced materials, metals, fuels and lubricants, nanostructures, ceramics, refractories, organic and inorganic chemicals, catalysts and nuclear materials by a range of techniques including X-ray, ICP, LIBS, mass spectrometry, synchrotron-based techniques, plus non-destructive and ablation surface techniques. 

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Coal quality analysis is important to promote the rational and sustainable utilization of coal resources. The traditional method of selecting characteristic lines from the coal sample spectrum is time-consuming. This...

Determination of ash content, volatile matter, and calorific value in coal by OLS combined with laser-induced breakdown spectroscopy based on PCs recombination

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.

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

Abstract High arsenic (As) content in medicine may threaten health and its determination is necessary. However, traditional methods cannot achieve rapid analysis because of complex digestion procedures. Exploring rapid and accurate methods for the determination of As is urgent. In this study, the As concentration in niuhuangjiedu tablets (NHJDTs) was accurately and rapidly determined by laser-induced breakdown spectroscopy (LIBS). Furthermore, comprehensive health risks of As were analyzed combined with solubility measurements to evaluate the impact on humans. The results showed that the relative standard deviation and relative error of the LIBS results were less than 10% for detection period of less than 0.3 h, which meets the requirements for rapid, accurate, and convenient As determination. The indices for non-carcinogenic and carcinogenic risk for total As and soluble As in niuhuangjiedu tablets were 966.67, 0.15, 4.37, and 6.72 × 10−4, respectively, indicating the presence of potential health risks. This work provided support for further investigation for quality control of pharmaceutical products.

Rapid Determination of Arsenic in Traditional Chinese Medicine by Laser-Induced Breakdown Spectroscopy (LIBS)

The purpose of photocatalysis is to realize the conversion between solar energy and chemical energy, and it is essential to develop a high-performance photocatalyst under visible-light irradiation. The conventional methods for photocatalyst preparation are mainly wet chemical routes, and abundant yields can be obtained. However, the products are not neat and accompanied by chemical groups and impurities, which are not beneficial for the enhancement of photocatalytic performance. In recent years, as a powerful tool for nanomaterial fabrication, pulsed laser heating in a liquid medium has been utilized to prepare a variety of nanocomposites. Products with synergistic effects and high crystallinity can be rapidly prepared under pulsed laser selective heating, which is beneficial for obtaining more effective photocatalytic performance. In this review, the typical characteristics of pulsed laser heating in liquids and their prepared nanocomposites for photocatalytic applications are summarized. This review not only highlights the innovative works of pulsed-laser-prepared nanocomposites in liquids for photocatalysis but also briefly introduces the specific challenges and prospects of this field.

/pdf/a-review-on-pulsed-laser-preparation-of-nanocomposites-in-2zfc46c7.pdf

A Review on Pulsed Laser Preparation of Nanocomposites in Liquids and Their Applications in Photocatalysis

The combination of a Bessel beam and LIBS is an effective method for rapidly detecting uneven samples in industry, which has higher accuracy and stability under fewer spectra.

Classification of uneven steel samples by laser induced breakdown spectroscopy based on a Bessel beam

Laser-induced breakdown spectroscopy (LIBS) has been applied in coal analysis for advantages such as real-time online analysis. Fine-tuning is a transfer learning method that has been utilized in LIBS to improve accuracy in the target domain with a limited training set by introducing a model trained on a different but related source domain. This research proposed a hybrid transfer learning method (HTr-LIBS) to further enhance the performance of LIBS coal analysis by combining fine-tuning with sample reweighting. A neural network was pre-trained on the source domain and target domain training set. The sample weights of the source domain were iteratively adjusted according to the prediction errors. The pre-trained neural network with optimal sample weights was then fine-tuned using the target domain training set. The proposed method significantly improved the analytical accuracy compared to direct modeling using small training sets. When the training set size increased to 19, the R2P of direct modeling for ash content and volatile matter content were 0.8105 and 0.9440, respectively. HTr-LIBS increased the R2P for ash content and volatile matter content to 0.9029 and 0.9627, respectively. The improvements were more significant and stable than fine-tuning of the source domain model without sample reweighting. The introduction of target domain data during pre-training and the iterative adjustment of sample weights both contributed to the improvements. 

Accuracy improvement of laser-induced breakdown spectroscopy coal analysis by hybrid transfer learning.

Rapid determination of lead (Pb) in the soil–plant system by laser-induced breakdown spectroscopy (LIBS): case study of Pb-pollution from perovskite solar cells

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