Raman Spectroscopy and Genetic Algorithms for the Classification of Wood Types

Lavine, Barry K.; Davidson, Charles E.; Moores, Anthony J.; Griffiths, Peter R.

doi:10.1366/0003702011953108

Cited by 82 publications

(41 citation statements)

References 17 publications

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“…FT-Raman band assignments for hardwoods reported in the literature (AGARWAL et al, 1996;KAWAI, 2003;AGARWAL, 1985;BARSBERG et al, 2005;FISCHER et al, 2005;LAVINE et al, 2001;PETROU et al, 2009 …”

Section: Figure 10mentioning

confidence: 99%

Physical, Morphological Properties and Raman Spectroscopy of Chestnut Blight Diseased Castanea Sativa Mill. Wood

Gündüz

Oral

Akyüz

et al. 2016

CERNE

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ABSTRACT:In this study, some of the physical and anatomical properties of Chestnut Blight Diseased (CBD) wood were investigated, and the study also included observations using Raman spectroscopy. The objective of these investigations was to determine the extent of the damage that is done to the wood of the diseased chestnut trees, which must be removed from the forest and used in the manufacture of industrial products. It was indicated that most of the adverse effects of the disease were in the vascular cambium. There was a clear indication of deterioration of the wood in the last growth ring next to vascular cambium. In the diseased secondary xylem region next to vascular cambium; vessel diameter, vessel frequency and vessel element length had a decrease, and vessel and other cells were irregular compared to healthy wood. Spores were detected and identified as Cryphonectria parasitica (Murrill). Annual ring properties (annual growth ring width, latewood percentage, etc.) were similar in diseased wood compared to healthy wood. The Raman spectroscopy results showed no significant changes in the structure of the cell wall or its components. After removing the diseased parts, unlimited usage of formerly wood is possible. Heat treatment of the wood is suggested before use in the interest of sanitation and dimensional stability. PROPRIEDADES FÍSICAS E MORFOLÓGICAS E ESPECTROSCOPIA RAMAN DE MADEIRA DE CASTANEA SATIVA MILL. AFETADA PELO CANCRO DO CASTANHEIRORESUMO: Neste estudo, algumas das propriedades físicas e anatômicas de madeira afetada pelo cancro do castanheiro (CBD) foram investigadas, e o estudo também incluiu observações usando espectroscopia Raman. O objetivo destas investigações foi determinar a dimensão do dano que é causado na madeira dos castanheiros doentes, que devem ser removidos da floresta e utilizados na fabricação de produtos industriais. Foi avaliado que a maioria dos efeitos adversos da doença esta localizado no câmbio vascular. Há uma clara indicação de deterioração da madeira no último anel de crescimento junto ao câmbio vascular. Na região doente do xilema secundário ao lado do câmbio vascular o diâmetro, frequência e comprimento do vaso eram menores, e os vasos e outras células eram irregulares comparados à madeira saudável. Esporos foram detectados e identificados como Cryphonectria parasitica (Murrill). Propriedades dos anéis anuais (largura de anéis de crescimento anual, porcentagem de lenho tardio, etc.) foram similares em madeira doente e madeira saudável. O resultado da espectroscopia Raman não mostrou mudança significativa na estrutura da parede celular ou dos seus componentes. Depois de retirar as partes doentes, o uso ilimitado da madeira é possível. Tratamento térmico da madeira é sugerido antes da utilização para fins de saneamento e estabilidade dimensional.

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Section: Figure 10mentioning

confidence: 99%

Physical, Morphological Properties and Raman Spectroscopy of Chestnut Blight Diseased Castanea Sativa Mill. Wood

Gündüz

Oral

Akyüz

et al. 2016

CERNE

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“…Earlier studies [11][12][13][14][15] consisted of distinguishing these classes of woods based on chemical composition-related Raman spectral differences. Although the chemical composition of wood is complex and varies from species to species, there are three structural polymeric components, namely, cellulose, lignin, and hemicellulose, which are common to all woods.…”

Section: Woodsmentioning

confidence: 99%

Raman Spectroscopic Characterization of Wood and Pulp Fibers

Agarwal

2008

Characterization of Lignocellulosic Materials

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This chapter reviews applications of Raman spectroscopy in the field of wood and pulp fibers. Most of the literature examined was published between 1998 and 2006. In addi tion to introduction, this chapter contains sections on wood and components, mechanical pulp, chemical pulp, modified/treated wood, cellulose I crystallinity of wood fibers, and the "self-absorption" phenomenon in near-infrared (IR) Fourier-transform (FT)-Raman spectroscopy. When needed, the sections are further categorized into various subsections. For example, the section on wood and components contains a variety of topics ranging from Raman band assignments to molecular changes during tensile deformation. From this review it will become clear that Raman spectroscopy has become an essential analytical technique in the field of wood and pulp fibers. IntroductionTechniques that can provide information on the chemical constitution of wood and pulp fibers and on processing-related changes of these materials are of great interest. Raman spectroscopy [1] is one such technique that provides fundamental knowledge on a molecu lar level and does not require chemical treatment of the sample (contrary to the case, for example, in fluorescence and electron microscopy). It has become an important analyt ical technique for nondestructive, qualitative, and quantitative analysis of materials. The technique is useful in a number of areas, including analytical measurements, mechan istic studies, and structural determinations. Studying a sample is very simple and usually involves sampling an area of interest by directing a laser excitation beam and analyzing the collected molecularly specific scattered light [2]. Although initially, obtaining good-quality Raman data required operator skill and training, this has started to change as a result of the commercial availability of compact, easy-to-use, integrated instruments at reasonable cost.Nevertheless, although the specificity of Raman spectroscopy is very high, its sensitivity is somewhat poor. Considering that only a small number of the incident laser photons are inelastically scattered, the detection of analytes present in very low concentrations is limited. To overcome this problem, special Raman signal-enhancing techniques can be applied. The two most prominent approaches are the resonance Raman effect [3] and the

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“…Another important aspect is that careful identification of the most informative wavelengths can make future measurements simpler and cheaper. The problem of variable selection specifically directed towards spectroscopic data is not new, and interesting approaches can be found in References [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A variable selection strategy for supervised classification with continuous spectroscopic data

Indahl

Næs

2004

Journal of Chemometrics

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In this paper we present a new variable selection method designed for classification problems where the X data are discretely sampled from continuous curves. For such data the loading weight vectors of a PLS discriminant analysis inherit the continuous behaviour, making the idea of local peaks meaningful. For successive components the local peaks are checked for importance before entering the set of selected variables. Our examples with NIR/NIT show that substantial simplification of the X space can be obtained without loss of classification power when compared with 'benchmark fullspectrum' methods.

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Raman Spectroscopy and Genetic Algorithms for the Classification of Wood Types

Cited by 82 publications

References 17 publications

Physical, Morphological Properties and Raman Spectroscopy of Chestnut Blight Diseased Castanea Sativa Mill. Wood

Physical, Morphological Properties and Raman Spectroscopy of Chestnut Blight Diseased Castanea Sativa Mill. Wood

Raman Spectroscopic Characterization of Wood and Pulp Fibers

A variable selection strategy for supervised classification with continuous spectroscopic data

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