Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

Santos, António J.; Anjos, Ofélia; Pereira, Helena

doi:10.5424/fs/2015242-07580

Cited by 10 publications

(5 citation statements)

References 30 publications

(33 reference statements)

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“…First, the infrared data from the calibration samples were regressed against the measured fiber length and width, and by means of full cross-validation with one sample omitted a significant number of PLS components (rank) was obtained using OPUS/Quant 2 software (version 7.5.18 BRUKER, Bruker Corporation, Karlsruhe, Germany). Besides the raw spectra, also pre-processed spectra with 10 methods were used for PLS analysis [ 36 , 37 , 40 ]. In a second step, the validation of the PLS-R models was performed using the independent test set.…”

Section: Methodsmentioning

confidence: 99%

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

2015

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In this paper, the morphological properties of fiber length (weighted in length) and of fiber width of unbleached Kraft pulp of Acacia melanoxylon were determined using TECHPAP Morfi® equipment (Techpap SAS, Grenoble, France), and were used in the calibration development of Near Infrared (NIR) partial least squares regression (PLS-R) models based on the spectral data obtained for the wood. It is the first time that fiber length and width of pulp were predicted with NIR spectral data of the initial woodmeal, with high accuracy and precision, and with ratios of performance to deviation (RPD) fulfilling the requirements for screening in breeding programs. The selected models for fiber length and fiber width used the second derivative and first derivative + multiplicative scatter correction (2ndDer and 1stDer + MSC) pre-processed spectra, respectively, in the wavenumber ranges from 7506 to 5440 cm−1. The statistical parameters of cross-validation (RMSECV (root mean square error of cross-validation) of 0.009 mm and 0.39 μm) and validation (RMSEP (root mean square error of prediction) of 0.007 mm and 0.36 μm) with RPDTS (ratios of performance to deviation of test set) values of 3.9 and 3.3, respectively, confirmed that the models are robust and well qualified for prediction. This modeling approach shows a high potential to be used for tree breeding and improvement programs, providing a rapid screening for desired fiber morphological properties of pulp prediction.

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Section: Methodsmentioning

confidence: 99%

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

2015

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“…These measurements, although simple, must be done carefully in order to avoid non-sampling errors (Soares et al, 2011), which can impact the analysis when one or more values are out of the general trend of the data and are considered as outliers. Because of their marked difference, according to Santos et al (2015), the number of outliers should be as low as possible. There are several ways to identify them, among which stands out the boxplot graphic (Schwertman et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

Júnior

Souza

Assis

et al. 2019

Pesq. agropec. bras.

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The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.

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“…A espectroscopia na região do infravermelho próximo (NIR) tem sido frequentemente aplicada como método analítico que fornece resultados eficientes para a determinação de moléculas orgânicas e variáveis quantitativas. A ferramenta NIR está presente em quase todas as áreas do conhecimento, devido ao nível de desenvolvimento de tecnologia, principalmente nos setores agrícola, alimentício, têxtil, cosméticos de polímeros, petroquímica, farmacêutica, ambiental e florestal (LEBLON et al, 2013;MUÑIZ et al, 2013;SANTOS et al, 2015).…”

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Estimativa da densidade básica da madeira de Eschweilera odora (Poepp.) Miers por espectroscopia no infravermelho próximo

Nascimento

Brasil

Nascimento

et al. 2017

RCM

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RESUMO A densidade básica é uma das propriedades mais estudadas na tecnologia da madeira, visto sua importância na correlação com as propriedades físicas e mecânicas. A metodologia tradicional (destrutiva) demanda altos custos e extenso período para execução dos ensaios. Neste sentido, a pesquisa desenvolvida buscou determinar a densidade da madeira de Eschweilera odora por metodologia não destrutiva, avaliando a eficiência da espectroscopia no infravermelho próximo-NIR. Madeiras provenientes de floresta secundária de terra firme (Amazonas/Brasil) foram selecionadas para estudo, onde oito replicadas da espécie Eschweilera odora foram utilizadas para validação da densidade e 11 espécies (64 amostras) utilizadas na construção do modelo PLS/NIR. Os espectros NIR das madeiras foram coletados na faixa de 4.000-10.000 cm-1 , onde a análise espectral possibilitou associar espectros x densidade. Os modelos utilizados para estimar a densidade de Eschweilera odora nas faces tangenciais e radiais apresentaram altos índices de determinação (R 2 > 0,90) e baixos erros (Calibração/RMSEC e previsão/RMSEP). O teste T mostrou que a diferença entre os valores da densidade de Eschweilera odora preditos pelos PLS Radial e Tangencial não foi significativa para um intervalo de confiança de 95%, apresentando resultados aproximados. Estes resultados confirmam a potencialidade da espectroscopia NIR para estimativa da densidade da madeira de Eschweilera odora. Palavras-chave: madeiras da Amazônia; caracterização tecnológica; NIR; metodologia não destrutiva. Estimation of the basic density of wood Eschweilera odora (Poepp.) Miers by near infrared spectroscopy ABSTRACT The basic density is one of the most studied properties in wood technology area due to its significance in the correlation with the physical and the mechanical properties. The traditional (destructive) methodology demands high costs and an extended time period for the execution of the tests. The research developed here determines the basic density of Eschweilera odora wood by non-destructive methodology, evaluating the efficiency of the near infrared spectroscopy-NIR technique. Timber from "terra firme" secondary forest (Amazonas/Brazil) was selected for our study, where eight replicates of Eschweilera odora species were used for density's validation and 11 species (64 samples) were used to construct the PLS/NIR model. The NIR spectra of the woods were collected in the range of 4,000-10,000 cm-1 , where the spectral analysis made it possible to associate spectra x density. The models used to determine the density of Eschweilera odora wood on the tangential and radial faces presented high determination indices (R 2 > 0.90) and low errors (Calibration/RMSEC and prediction/RMSEP). The T test showed that the difference between values of Eschweilera odora wood density predicted by Radial PLS and Tangential PLS were not significant 95% confidence level, showing approximate results. These results confirm the potential the NIR spectroscopy to estimate the wood density.

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Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

Cited by 10 publications

References 30 publications

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models

Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

Estimativa da densidade básica da madeira de Eschweilera odora (Poepp.) Miers por espectroscopia no infravermelho próximo

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