Identification of spectral and colorimetric reflectance patterns of dry wood of Peltogyne purpurea Pittier

Main Article Content

Juan Carlos Valverde
Dagoberto Arias Aguilar
Kevin Arias
Marvin Castillo
Cornelia Miller
Heileen Aguilar
Daniel Flores

Abstract

Hyperspectral and colorimetric patterns were determined from anatomical and physical aspects for the characterization of dry Peltogyne purpurea Pittier wood from natural forest trees. A sample of 24 P. purpurea wood discs was taken and anatomical characteristics (density and diameter of vessels, percentage of solitary vessels), wood density, colour (CIELab) and hyperspectral reflectance in the range of 310 to 1 100 nm in the dry wood, both in sapwood and heartwood section, were determined. Significant anatomical differences were found between sapwood and heartwood; a higher presence of solitary vessels was found in sapwood (34.28%) but with a vessel density of 27.07 pores mm-2 and diameter of 148.53 μm In the colorimetry analysis, brightness and b* (yellowing) were higher in sapwood, but with significantly lower values in a* (redness), thus generating a total color difference (higher than 14.55). As regards reflectance, three significant differentiation points were found between both sections, which were at 460, 580 and 1 020 nm; when correlating these three segments, no correlation was found with the anatomical aspects, but with the density of the wood (higher than 0.69), at the color level no relationship was found with the color parameter L* (brightness), while a* only showed inverse correlations at 580 nm and linear ones with b* at 580 nm. In this study no colorimetric relationships with anatomical aspects were found.

Downloads

Download data is not yet available.

Article Details

How to Cite
Valverde, J. C., Arias Aguilar, D., Arias, K., Castillo, M., Miller, C., Aguilar, H., & Flores, D. (2020). Identification of spectral and colorimetric reflectance patterns of dry wood of Peltogyne purpurea Pittier. Cuban Journal of Forest Science, 8(2), 262–281. Retrieved from https://cfores.upr.edu.cu/index.php/cfores/article/view/589
Section
Scientific articles

References

AFFONSO, C., ROSSI, A.L.D., VIEIRA, F.H.A. y DE CARVALHO, A.C.P. de L.F., (2017). Deep learning for biological image classification. Expert Systems with Applications [en línea], vol. 85, pp. 114-122. [Consulta: 13 mayo 2020]. ISSN 0957-4174. DOI 10.1016/j.eswa.2017.05.039. Disponible en: http://www.sciencedirect.com/science/article/pii/S0957417417303627.

BAIANO, A., (2017). Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: A review. Journal of Food Engineering [en línea], vol. 214, pp. 10-15. [Consulta: 13 mayo 2020]. ISSN 0260-8774. DOI 10.1016/j.jfoodeng.2017.06.012. Disponible en: http://www.sciencedirect.com/science/article/pii/S0260877417302546.

BONIFAZI, G., CALIENNO, L., CAPOBIANCO, G., LO MONACO, A., PELOSI, C., PICCHIO, R. y SERRANTI, S., (2015). Modeling color and chemical changes on normal and red heart beech wood by reflectance spectrophotometry, Fourier Transform Infrared spectroscopy and hyperspectral imaging. Polymer Degradation and Stability [en línea], vol. 113, pp. 10-21. [Consulta: 13 mayo 2020]. ISSN 0141-3910. DOI 10.1016/j.polymdegradstab.2015.01.001. Disponible en: http://www.sciencedirect.com/science/article/pii/S0141391015000129.

CAO, X., GE, Y., LI, R., ZHAO, J. y JIAO, L., (2019). Hyperspectral imagery classification with deep metric learning. Neurocomputing [en línea], vol. 356, pp. 217-227. [Consulta: 13 mayo 2020]. ISSN 0925-2312. DOI 10.1016/j.neucom.2019.05.019. Disponible en: http://www.sciencedirect.com/science/article/pii/S0925231219306800.

CAREVIÆ, I., SERDAR, M., ŠTIRMER, N. y UKRAINCZYK, N., (2019). Preliminary screening of wood biomass ashes for partial resources replacements in cementitious materials. Journal of Cleaner Production [en línea], vol. 229, pp. 1045-1064. [Consulta: 13 mayo 2020]. ISSN 0959-6526. DOI 10.1016/j.jclepro.2019.04.321. Disponible en: http://www.sciencedirect.com/science/article/pii/S0959652619314180.

CESPRINI, E., RESENTE, G., CAUSIN, V., URSO, T., CAVALLI, R. y ZANETTI, M., (2020). Energy recovery of glued wood waste A review. Fuel [en línea], vol. 262, pp. 116520. [Consulta: 13 mayo 2020]. ISSN 0016-2361. DOI 10.1016/j.fuel.2019.116520. Disponible en: http://www.sciencedirect.com/science/article/pii/S0016236119318745.

CHI, G., MA, J., SHI, Y. y CHEN, X., (2016). Hyperspectral remote sensing of cyanobacterial pigments as indicators of the iron nutritional status of cyanobacteria-dominant algal blooms in eutrophic lakes. Ecological Indicators [en línea], vol. 71, pp. 609-617. [Consulta: 13 mayo 2020]. ISSN 1470-160X. DOI 10.1016/j.ecolind.2016.06.014. Disponible en: http://www.sciencedirect.com/science/article/pii/S1470160X16303144.

COLARES, C.J.G., PASTORE, T.C.M., CORADIN, V.T.R., MARQUES, L.F., MOREIRA, A.C.O., ALEXANDRINO, G.L., POPPI, R.J. y BRAGA, J.W.B., (2016). Near infrared hyperspectral imaging and MCR-ALS applied for mapping chemical composition of the wood specie Swietenia Macrophylla King (Mahogany) at microscopic level. Microchemical Journal [en línea], vol. 124, pp. 356-363. [Consulta: 13 mayo 2020]. ISSN 0026-265X. DOI 10.1016/j.microc.2015.09.022. Disponible en: http://www.sciencedirect.com/science/article/pii/S0026265X15002234.

DE SOUSA FERNANDES, D.D., DE ALMEIDA, V.E., FONTES, M.M., DE ARAÚJO, M.C.U., VÉRAS, G. y DINIZ, P.H.G.D., (2019). Simultaneous identification of the wood types in aged cachaças and their adulterations with wood extracts using digital images and SPA-LDA. Food Chemistry [en línea], vol. 273, pp. 77-84. [Consulta: 13 mayo 2020]. ISSN 0308-8146. DOI 10.1016/j.foodchem.2018.02.035. Disponible en: http://www.sciencedirect.com/science/article/pii/S0308814618302620.

FENG, Y., JIANLONG, L., XIAOYU, G., YURONG, Q., XIAOLING, W. y QI, Y., (2010). Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Computers and Electronics in Agriculture [en línea], vol. 70, no. 1, pp. 52-59. [Consulta: 13 mayo 2020]. ISSN 0168-1699. DOI 10.1016/j.compag.2009.08.010. Disponible en: https://europepmc.org/article/agr/ind44306392.

HALL, E.M., BRADY, S.P., MATTHEUS, N.M., EARLEY, R.L., DIAMOND, M. y CRESPI, E.J., (2017). Physiological consequences of exposure to salinized roadside ponds on wood frog larvae and adults. Biological Conservation [en línea], vol. 209, pp. 98-106. [Consulta: 13 mayo 2020]. ISSN 0006-3207. DOI 10.1016/j.biocon.2017.02.013. Disponible en: http://www.sciencedirect.com/science/article/pii/S0006320716304517.

HERRERO-LANGREO, A., SCANNELL, A.G.M. y GOWEN, A., (2020). Chapter 3.5 - Hyperspectral imaging for food-related microbiology applications. En: J.M. AMIGO (ed.), Data Handling in Science and Technology [en línea]. S.l.: Elsevier, Hyperspectral Imaging, pp. 493-522. [Consulta: 13 mayo 2020]. Disponible en: http://www.sciencedirect.com/science/article/pii/B9780444639776000201.

HOLM, S., THEES, O., LEMM, R., OLSCHEWSKI, R. y HILTY, L.M., (2018). An agent-based model of wood markets: Scenario analysis. Forest Policy and Economics [en línea], vol. 95, pp. 26-36. [Consulta: 13 mayo 2020]. ISSN 1389-9341. DOI 10.1016/j.forpol.2018.07.005. Disponible en: http://www.sciencedirect.com/science/article/pii/S138993411830234X.

KIAT, P.E., MALEK, M.A. y SHAMSUDDIN, S.M., (2020). Net carbon stocks change in biomass from wood removal of tropical forests in Sarawak, Malaysia. Journal of King Saud University - Science [en línea], vol. 32, no. 1, pp. 1096-1099. [Consulta: 13 mayo 2020]. ISSN 1018-3647. DOI 10.1016/j.jksus.2019.09.012. Disponible en: http://www.sciencedirect.com/science/article/pii/S1018364719317835.

KNAUF, M., (2016). The wood market balance as a tool for calculating wood use's climate change mitigation effect An example for Germany. Forest Policy and Economics [en línea], vol. 66, pp. 18-21. [Consulta: 13 mayo 2020]. ISSN 1389-9341. DOI 10.1016/j.forpol.2016.02.004. Disponible en: http://www.sciencedirect.com/science/article/pii/S1389934116300156.

KOBAYASHI, K., HWANG, S.-W., OKOCHI, T., LEE, W.-H. y SUGIYAMA, J., (2019). Non-destructive method for wood identification using conventional X-ray computed tomography data. Journal of Cultural Heritage [en línea], vol. 38, pp. 88-93. [Consulta: 13 mayo 2020]. ISSN 1296-2074. DOI 10.1016/j.culher.2019.02.001. Disponible en: http://www.sciencedirect.com/science/article/pii/S1296207418306927.

LI, F., LU, H. y ZHANG, P., (2019). An innovative multi-kernel learning algorithm for hyperspectral classification. Computers & Electrical Engineering [en línea], vol. 79, pp. 106456. [Consulta: 13 mayo 2020]. ISSN 0045-7906. DOI 10.1016/j.compeleceng.2019.106456. Disponible en: http://www.sciencedirect.com/science/article/pii/S0045790619309693.

LIU, Y., ZHOU, S., HAN, W., LIU, W., QIU, Z. y LI, C., (2019). Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Analytica Chimica Acta [en línea], vol. 1086, pp. 46-54. [Consulta: 13 mayo 2020]. ISSN 0003-2670. DOI 10.1016/j.aca.2019.08.026. Disponible en: http://www.sciencedirect.com/science/article/pii/S0003267019309675.

MACHADO, J.S., PEREIRA, F. y QUILHÓ, T., (2019). Assessment of old timber members: Importance of wood species identification and direct tensile test information. Construction and Building Materials [en línea], vol. 207, pp. 651-660. [Consulta: 13 mayo 2020]. ISSN 0950-0618. DOI 10.1016/j.conbuildmat.2019.02.168. Disponible en: http://www.sciencedirect.com/science/article/pii/S0950061819304635.

MVONDO, R.R.N., MEUKAM, P., JEONG, J., MENESES, D.D.S. y NKENG, E.G., (2017). Influence of water content on the mechanical and chemical properties of tropical wood species. Results in Physics [en línea], vol. 7, pp. 2096-2103. [Consulta: 13 mayo 2020]. ISSN 2211-3797. DOI 10.1016/j.rinp.2017.06.025. Disponible en: http://www.sciencedirect.com/science/article/pii/S2211379717304862.

NABEELA, F., MURAD, W., KHAN, I., MIAN, I.A., REHMAN, H., ADNAN, M. y AZIZULLAH, A., (2015). Effect of wood ash application on the morphological, physiological and biochemical parameters of Brassica napus L. Plant Physiology and Biochemistry [en línea], vol. 95, pp. 15-25. [Consulta: 13 mayo 2020]. ISSN 0981-9428. DOI 10.1016/j.plaphy.2015.06.017. Disponible en: http://www.sciencedirect.com/science/article/pii/S0981942815300462.

POWERS, J.S., BECKNELL, J.M., IRVING, J. y PÈREZ-AVILES, D., (2009). Diversity and structure of regenerating tropical dry forests in Costa Rica: Geographic patterns and environmental drivers. Forest Ecology and Management [en línea], vol. 258, no. 6, pp. 959-970. [Consulta: 13 mayo 2020]. ISSN 0378-1127. DOI 10.1016/j.foreco.2008.10.036. Disponible en: http://www.sciencedirect.com/science/article/pii/S0378112708008165.

PRÄGER, F., PACZKOWSKI, S., SAILER, G., DERKYI, N.S.A. y PELZ, S., (2019). Biomass sources for a sustainable energy supply in Ghana A case study for Sunyani. Renewable and Sustainable Energy Reviews [en línea], vol. 107, pp. 413-424. [Consulta: 13 mayo 2020]. ISSN 1364-0321. DOI 10.1016/j.rser.2019.03.016. Disponible en: http://www.sciencedirect.com/science/article/pii/S1364032119301492.

RUFFINATTO, F., CREMONINI, C., MACCHIONI, N. y ZANUTTINI, R., (2014). Application of reflected light microscopy for non-invasive wood identification of marquetry furniture and small wood carvings. Journal of Cultural Heritage [en línea], vol. 15, no. 6, pp. 614-620. [Consulta: 13 mayo 2020]. ISSN 1296-2074. DOI 10.1016/j.culher.2013.11.013. Disponible en: http://www.sciencedirect.com/science/article/pii/S1296207413002227.

SCHMIDT, C., WESTERMANN, H.-H. y STEINHILPER, R., (2019). An investigation of buzz saw blade cutting forces depending on tool geometry for cutting frozen wood. Procedia Manufacturing [en línea], vol. 33, pp. 778-785. [Consulta: 13 mayo 2020]. ISSN 2351-9789. DOI 10.1016/j.promfg.2019.04.098. Disponible en: http://www.sciencedirect.com/science/article/pii/S2351978919305797.

SCHREINER, B., SONA, B. y LOOS, H., (2018). The smell of wood and its impact on physiological responses. International Journal of Psychophysiology [en línea], pp. 131:S40. [Consulta: 13 mayo 2020]. DOI. 10.1016/j.ijpsycho.2018.07.123. Disponible en: https://www.researchgate.net/publication/328060187_The_smell_of_wood_and_its_impact_on_physiological_responses.

SIMO-TAGNE, M., RÉMOND, R., ROGAUME, Y., ZOULALIAN, A. y BONOMA, B., (2016). Modeling of coupled heat and mass transfer during drying of tropical woods. International Journal of Thermal Sciences [en línea], vol. 109, pp. 299-308. [Consulta: 13 mayo 2020]. ISSN 1290-0729. DOI 10.1016/j.ijthermalsci.2016.06.012. Disponible en: http://www.sciencedirect.com/science/article/pii/S1290072916307232.

TAN, K., WANG, H., CHEN, L., DU, Q., DU, P. y PAN, C., (2020). Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. Journal of Hazardous Materials [en línea], vol. 382, pp. 120987. [Consulta: 13 mayo 2020]. ISSN 0304-3894. DOI 10.1016/j.jhazmat.2019.120987. Disponible en: http://www.sciencedirect.com/science/article/pii/S0304389419309410.

ULRICI, A., SERRANTI, S., FERRARI, C., CESARE, D., FOCA, G. y BONIFAZI, G., (2013). Efficient chemometric strategies for PETPLA discrimination in recycling plants using hyperspectral imaging. Chemometrics and Intelligent Laboratory Systems [en línea], vol. 122, pp. 31-39. [Consulta: 13 mayo 2020]. ISSN 0169-7439. DOI 10.1016/j.chemolab.2013.01.001. Disponible en: http://www.sciencedirect.com/science/article/pii/S0169743913000129.

WHITAU, R., DILKES-HALL, I.E., DOTTE-SAROUT, E., LANGLEY, M.C., BALME, J. y O'CONNOR, S., (2016). X-ray computed microtomography and the identification of wood taxa selected for archaeological artefact manufacture: Rare examples from Australian contexts. Journal of Archaeological Science: Reports [en línea], vol. 6, pp. 536-546. [Consulta: 13 mayo 2020]. ISSN 2352-409X. DOI 10.1016/j.jasrep.2016.03.021. Disponible en: http://www.sciencedirect.com/science/article/pii/S2352409X1630089X.

WILCZYÑSKI, S., KOPROWSKI, R., MARMION, M., DUDA, P. y B£OÑSKA-FAJFROWSKA, B., (2016). The use of hyperspectral imaging in the VNIR (4001000nm) and SWIR range (10002500nm) for detecting counterfeit drugs with identical API composition. Talanta [en línea], vol. 160, pp. 1-8. [Consulta: 13 mayo 2020]. ISSN 0039-9140. DOI 10.1016/j.talanta.2016.06.057. Disponible en: http://www.sciencedirect.com/science/article/pii/S0039914016304830.

YANG, H., CHAO, W., WANG, S., YU, Q., CAO, G., YANG, T., LIU, F., DI, X., LI, J., WANG, C. y LI, G., (2019). Self-luminous wood composite for both thermal and light energy storage. Energy Storage Materials [en línea], vol. 18, pp. 15-22. [Consulta: 13 mayo 2020]. ISSN 2405-8297. DOI 10.1016/j.ensm.2019.02.005. Disponible en: http://www.sciencedirect.com/science/article/pii/S2405829718313850.