Discrimination of wood species using laser-induced breakdown spectroscopy and near-infrared reflectance spectroscopy

Leandro, João Guilherme Rodrigues; Gonzaga, Fabiano Barbieri; Latorraca, João Vicente de Figueiredo

doi:10.1007/s00226-019-01119-7

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…In recent years, handheld LIBS has been used in a wide variety of areas where elemental signature characterization is important, including mining, 2,3 astrochemistry, 26 wood analysis, 27 and other applications 28 . In one recent study, a homemade portable LIBS system in tandem with a near IR spectrometer was used to discriminate varying wood genera and showed a 90% classification accuracy 29 . In a similar vein, LIBS was coupled with an electronic nose sensor to classify four Brazilian woods of differing genus with perfect classification success 27 .…”

Section: Introductionmentioning

confidence: 99%

“…28 In one recent study, a homemade portable LIBS system in tandem with a near IR spectrometer was used to discriminate varying wood genera and showed a 90% classification accuracy. 29 In a similar vein, LIBS was coupled with an electronic nose sensor to classify four Brazilian woods of differing genus with perfect classification success. 27 Alternatively, a benchtop LIBS system was used to differentiate seven Dalbergia species with algorithm dependent classification rates from 52% to 100%.…”

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confidence: 99%

“…30 It should also be noted here that LIBS frequently requires multivariate analysis and chemometrics regardless of application due to the highly overlapping and information rich signals. 17,26,27,[29][30][31][32] There are multiple classification algorithms that have been applied to problems analogous to the classification of Dalbergia species via LIBS. 17,26,27,[29][30][31][32] One facet of this study considers how six common supervised classification methods perform modeling large amounts low S/N data compared with smaller amounts of high S/N data.…”

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confidence: 99%

“…17,26,27,[29][30][31][32] There are multiple classification algorithms that have been applied to problems analogous to the classification of Dalbergia species via LIBS. 17,26,27,[29][30][31][32] One facet of this study considers how six common supervised classification methods perform modeling large amounts low S/N data compared with smaller amounts of high S/N data.…”

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confidence: 99%

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Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser‐induced breakdown spectroscopy

Celani

McCormick

Speed

et al. 2023

Journal of Chemometrics

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The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like Dalbergia spp. (rosewood), a world‐wide protected genus from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Due to increased threat to Dalbergia spp., and lack of action to reduce threats, port of entry analysis methods are required to identify Dalbergia spp. Handheld laser‐induced breakdown spectroscopy (LIBS) has been shown to be capable of identifying species and establishing provenance of Dalbergia spp. and other tropical hardwoods, but analysis methods for this work have yet to be investigated in detail. The present work investigates five well‐known algorithms—partial least squares discriminant analysis (PLS‐DA), classification and regression trees (CART), k‐nearest neighbor (k‐NN), random forest (RF), and support vector machine (SVM)—two training/test set sampling regimes, and data collection at two signal‐to‐noise (S/N) ratios to assess the potential for handheld LIBS analyses. Additionally, imbalanced classes are addressed. For this application, SVM and RF yield near identical results (though RF takes nearly 100 longer to compute), while the S/N ratio has a significant effect on model success assuming all else is equal. It was found that forming a training set with replicate low S/N analyses can perform as well as higher precision training sets for true prediction, even if the predicted samples have low signal to noise! This work confirms handheld LIBS analyzers can provide a viable method for classification of hardwood species, even within the same genus.

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Section: Introductionmentioning

confidence: 99%

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Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser‐induced breakdown spectroscopy

Celani

McCormick

Speed

et al. 2023

Journal of Chemometrics

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“…Laser-induced breakdown spectroscopy (LIBS) as a powerful elemental analysis technique has some unique advantages such as rapid analysis, little or no sample preparation, operation in situ, and remote measurement [19][20][21]. Thus, the classification of wood species using LIBS combined with chemometric methods has been proposed [22,23]. In the analytical process of LIBS, the sample surface is ablated by a laser pulse with high energy and a laser-induced plasma is generated.…”

Section: Introductionmentioning

confidence: 99%

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

Cui¹,

Wang²,

Wei³

et al. 2021

Plasma Sci. Technol.

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In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor, partial least squares-discrimination analysis, random forest, and support vector machine (SVM), combined with the feature selection methods, distance correlation coefficient (DCC), important weight of linear discriminant analysis (IW-LDA), and Relief-F algorithms, to discriminate eight species of wood (African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk, Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy (LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA, and Relief-F. All models are built by using the different number of feature lines (sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate (CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves (98.55...0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.

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Identification of Graves’ ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Feng

Huang

et al. 2020

Front. Optoelectron.

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Discrimination of wood species using laser-induced breakdown spectroscopy and near-infrared reflectance spectroscopy

Cited by 7 publications

References 27 publications

Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser‐induced breakdown spectroscopy

Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser‐induced breakdown spectroscopy

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

Identification of Graves’ ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

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