Discrimination of “Louros” wood from the Brazilian Amazon by near-infrared spectroscopy and machine learning techniques

Santos, Joielan Xipaia dos; Vieira, Helena Cristina; Souza, Deivison Venicio; Menezes, Marlon Costa de; Muñiz, Graciela Inés Bolzón de; Soffiatti, Patricia; Nisgoski, Silvana

doi:10.1007/s00107-021-01685-3

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…Subsequently, we calculated a multivariate analysis model using the NIR spectra to differentiate between softwood and hardwood species. Specifically, we employed the SIMCA [14][15][16], PLS-DA [13,17,18], and SVM classification model [9,13,[18][19][20].…”

Section: Experimental Designmentioning

confidence: 99%

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The utilization of wood samples from xylarium in historical wooden statues: improving the separation accuracy non-destructive measurement for using several algorithms

Kurata,

Horiuchi,

Kojima

et al. 2024

J Wood Sci

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There are numerous wooden historical artifacts in Kyoto and other parts of Japan, including Buddhist statues or Shinto deities. The identification of wood species in these historical artifact is desirable for both repair and maintenance purposes. The most common method of identifying wood species involves examining samples taken from the artifacts. However, intentional sampling from old cultural artifacts is prohibited in Japan. As a result, we attempted to determine the wood species of old statues non-destructively using near-infrared spectroscopy (NIRS). In this article, we developed the softwood and hardwood separation model using NIRS to compare the prediction accuracy for few algorithms. The model was created based on wood samples stored in the xylarium of the Forestry and Forest Products Research Institute (TWTw). We then applied this model to old Buddhist statues in order to classify them as either softwood or hardwood. These Buddhist statues were housed in Nazenji temple and are believed to have been carved during the Heian period (8th–12th century). For the near-infrared (NIR) measurements, we collected diffuse reflectance spectra from TWTw sample and Buddhist statues using same spectrometer. Initially, we used the soft independent modeling of class analogy method (SIMCA), partial least squares discriminant analysis (PLS_DA), and support vector machine to analyze the NIR spectra obtained from the TWTw wood samples. Subsequently, we applied the NIR spectra obtained from several Buddhist statues in Nazenji temple to the aforementioned separation model and determined whether spectra data were classified as the softwood or hardwood. Finally, wood specimens detached naturally from the Buddhist statues over time were observed under microscopic analysis to identify the wood species. As comparing the prediction accuracy of few algorithms, SIMCA had a poor result, but PLS_DA had a good result. PLS_DA had better discrimination because it performed calculations to improve regression from both explanatory variables and objective variables.

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Section: Experimental Designmentioning

confidence: 99%

“…For PLS-DA and SVM analysis [13,18], response variable of 1 or − 1 was defined for hardwood or softwood. Calculation response variable from xylarium wood sample spectra data, and hardwood and softwood classification model was created.…”

Section: Softwood and Hardwood Classification Model Using Multivariat...mentioning

confidence: 99%

The utilization of wood samples from xylarium in historical wooden statues: improving the separation accuracy non-destructive measurement for using several algorithms

Kurata,

Horiuchi,

Kojima

et al. 2024

J Wood Sci

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show abstract

“…This is repeated K times so that after K iterations all data is used for calibration as well as validation. K values between five and ten are commonly used (Paiva et al, 2021;Santos et al, 2021).…”

Section: Model Developmentmentioning

confidence: 99%

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs

et al. 2021

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The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.

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“…SVM was also used to identify small samples of timber species in combination with infrared spectroscopy (IR), finding that this technology led to better predicting results than Cluster Analysis and Bayes Discriminant [21]. Likewise, Dos Santos et al 2021 [22], using a combination of near-infrared spectroscopy (NIR) and machine learning techniques (SVM, KNN and partial least squares discriminant analysis), were able to recognize wood from the "Louros" group from the Brazilian Amazon.…”

Section: Introductionmentioning

confidence: 99%

Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber

Rello

Spavento

Casado-Sanz

et al. 2022

Engineering Structures

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The efficiency of visual grading standards applied to structural timber is often inappropriate, and timber properties are either under or over-graded. Although not included in the current UNE 56544 visual grading standard, machine learning algorithms represent a promising alternative to grade structural timber. The general aim of this research was to compare the performance of machine learning algorithms based on visual defects, non-destructive techniques and sawing systems ("cut type") with UNE 56544:1997 visual grading in order to predict the qualifying efficiency of Populus x euramericana I-214 structural timber. Visual evaluation, ultrasound and vibrational non-destructive testing, and sawing systems register (radial, tangential and mixed) were applied to characterize 945 beams. In addition, in order to retrieve actual physical-mechanical values, density and static bending destructive testing (EN-408:2011 + A1:2012) was also carried out. Several machine learning algorithms were then used to grade the beams, and their predictive accuracy was compared with that of visual grading. To do so, three scenarios were considered: a first scenario in which only visual variables were used; a second scenario in which "cut type" variables were also included; and a third scenario in which additional nondestructive variables were considered. Results showed a poor level of performance of UNE 56544:1997, with an apparent mismatch between the strength values assigned for each visual grade (established by the EN 338 standard) and the actual values. On the opposite, all algorithms performed better than visual grading and may thus be deemed as promising timber strength grading tools.

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Discrimination of “Louros” wood from the Brazilian Amazon by near-infrared spectroscopy and machine learning techniques

Cited by 9 publications

References 30 publications

The utilization of wood samples from xylarium in historical wooden statues: improving the separation accuracy non-destructive measurement for using several algorithms

The utilization of wood samples from xylarium in historical wooden statues: improving the separation accuracy non-destructive measurement for using several algorithms

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs

Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber

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