A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis

Li, Luning; Liu, Xiangfeng; Yang, Fan; Wang, Xu; Wang, Yunlong; Shu, Rong

doi:10.1016/j.sab.2021.106183

Cited by 85 publications

(40 citation statements)

References 138 publications

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“…Among the variety of ANN approaches, the multilayer perceptron regression (MLP-R) architecture was considered since it has been proven to be a suitable technique for a wide range of LIBS data processing problems (see, for example, [ 43 ] and references therein). The training phase included two steps: the input data were fed forward through the network; in order to measure how far the resulting output was from the desired one, an error function was then calculated, and propagated back to the previous layers while changing the corresponding weights.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

et al. 2022

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Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation. Particular attention was dedicated to the development of LIBS methodology providing a reliable basis for precise material grading. The congruence of the results achieved demonstrates the capability of the present approach to precisely quantify major and minor geochemical components of calcareous rocks, thus disclosing a concrete application perspective within the lime industry production chain.

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Section: Methodsmentioning

confidence: 99%

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

et al. 2022

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“…Common supervised machine learning algorithms that have been used in LIBS studies, are, among others, Support Vector Machines (SVMs) [ 23 , 24 ], Linear Discriminant Analysis (LDA) [ 25 ], Partial Least Squares (PLS) [ 26 ], Partial Least Squares Discriminant Analysis (PLS-DA) [ 27 ], Random Forests (RFs) [ 28 ], and k Nearest Neighbors (k-NN) [ 29 ]. Moreover, deep learning algorithms have been also used in LIBS studies, with the most popular type being the multi-layer perceptron (MLP) neural networks [ 12 , 30 ].…”

Section: Chemometrics and Machine/deep Learning For Libsmentioning

confidence: 99%

“…During the past decade, the scientific interest about LIBS-related applications has been importantly revived, mostly due to the implementation of chemometric and machine learning tools for the analysis of the LIBS spectroscopic data [ 11 , 12 ]. In comparison with other spectroscopic techniques, LIBS is superior in terms of collected data, as it can provide enormous datasets with thousands of variables in very short acquisition times.…”

Section: Introductionmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

et al. 2021

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Laser-Induced Breakdown Spectroscopy (LIBS), having reached a level of maturity during the last few years, is generally considered as a very powerful and efficient analytical tool, and it has been proposed for a broad range of applications, extending from space exploration down to terrestrial applications, from cultural heritage to food science and security. Over the last decade, there has been a rapidly growing sub-field concerning the application of LIBS for food analysis, safety, and security, which along with the implementation of machine learning and chemometric algorithms opens new perspectives and possibilities. The present review intends to provide a short overview of the current state-of-the-art research activities concerning the application of LIBS for the analysis of foodstuffs, with the emphasis given to olive oil, honey, and milk.

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“…Introduction to BPNN. The BP neural network (BPNN) is a typical representative of ANN, and it is also the most widely used ANN [11]. BPNN is produced by simulating the structure of the human brain neuron network, which is a complex network composed of many nodes connected [12].…”

Section: Introduction To Bpnn and Its Optimizationmentioning

confidence: 99%

Piano Music Teaching under the Background of Artificial Intelligence

Huang

Ding

2022

Wireless Communications and Mobile Computing

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The work is aimed at solving the problems of easy trapping into local extremes and slow convergence speed of the traditional music teaching evaluation system on Backpropagation Neural Network (BPNN). The traditional note recognition methods are susceptible to high noise complexity. Firstly, the Levenberg Marquardt (LM) algorithm is used to optimize the BPNN; secondly, an improved endpoint detection algorithm is proposed by short-term energy difference, which can accurately identify the time value of each note in the piano playing audio. By the traditional frequency domain analysis method, a radical frequency extraction algorithm is proposed by the improved standard harmonic method, which can accurately identify the note’s pitch. Finally, a piano performance evaluation model by BPNN is implemented, and the model is implemented by the Musical Instrument Digital Interface (MIDI) system. This evaluation model can be used to correct the errors of students’ performances in the piano music teaching process and to perform overall evaluation, rhythm evaluation, and expressive evaluation. Teachers and students play minuet to collect experimental samples to train BPNN and test the performance of the evaluation model. The practical result shows that (1) after 3000 times of training, the neural network error is less than 0.01, and the network converges; (2) the evaluation results of the piano performance evaluation model designed are basically in line with the actual level of the performer and have specific feasibility; and (3) the optimized BPNN is used to correct errors during performances with an accuracy rate of 94.3%, which is 5.25% higher than the traditional method. The error correction accuracy rate for pitch is 92.9%, which is 5.21% higher than the traditional method. The optimized BPNN has significantly improved the error correction accuracy of the notes and pitches played by the player. The model can effectively help piano beginners correct errors and improve the accuracy and efficiency of the practice. The purpose of this study is to alleviate the scarcity of piano teachers, reduce the work intensity of piano teachers, realize automatic error correction and objective evaluation of playing, and provide necessary technical support for improving the efficiency of piano music teaching.

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A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis

Cited by 85 publications

References 138 publications

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

Piano Music Teaching under the Background of Artificial Intelligence

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