NIR spectroscopy for tree species identification based on different samples: a review
Abstract
Near-infrared spectroscopy is applied in a wide range of forestry studies. Among them is tree species identification based on leaf, wood, bark and seed samples. There are different mathematical pretreatments applied to spectra and a great range of processing or classification methods, as well as variation in the accuracy/precision of the results. This paper is a review of the main manuscripts found by searching the databases of Google and the Office to Coordinate Improvement of University Personnel (CAPES), using the species names along with identification/distinction/discrimination and NIR as keywords. It describes the state of the art regarding the use of near-infrared techniques for tree species identification, from 1969 to the first months of 2024, encompassing the use of different samples, devices and main analytic methods.
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References
Abasolo M, Lee DJ, Raymond C, Meder R, Shepherd M, 2013. Deviant near-infrared spectra identifies Corymbia hybrids. For Ecol Manag 304: 121-131. https://doi.org/10.1016/j.foreco.2013.04.040
Abe H, Kurata Y, Watanabe K, Ishikawa A, Noshiro S, Fujii T, Iwasa M, Kaneko H, Wada H, 2020. The separation of softwood and hardwood in historical wooden statues of the Nazenji-temple in Japan using NIR spectroscopy. IAWA J 41: 740-750. https://doi.org/10.1163/22941932-bja10038
Abe H, Kurata Y, Watanabe K, Kitin P, Kojima M, Yazaki K, 2022. Longitudinal transmittance of visible and near-infrared light in the wood of 21 conifer species. IAWA J 43: 403-412. https://doi.org/10.1163/22941932-bja10103
Abe H, Watanabe H, Ishikawa A, Noshiro S, Fujii T, Iwasa M, Kaneko H, Wada H, 2015. Non-destructive method for species identification of historical wooden statues using NIR spectroscopy: analysis using wood samples of the internationally authorized collection. Mokuzai Hozon, 41: 162-170.
Adedipe OE, Dawson-Andoh B, Slahor J, Osborn L, 2008. Classification of red oak (Quercus rubra) and White Oak (Quercus alba) wood using a near infrared spectrometer and soft independent modelling of class analogies. JNIRS 16: 49-57. https://doi.org/10.1255/jnirs.760
Amendola L, Firmani P, Bucci R, Marini F, Biancolillo A, 2020. Authentication of sorrento walnuts by NIR spectroscopy coupled with different chemometric classification strategies. Appl Sci 10: 4003. https://doi.org/10.3390/app10114003
Asner GP, Martin RE, Carranza-Jiménez L, Sinca F, Tupayachi R, Anderson CB, Martinez P, 2014. Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. New Phytol 204: 127-139. https://doi.org/10.1111/nph.12895
Atkinson MD, Jervis AP, Sangha RS, 1997. Discrimination between Betula pendula, Betula pubescens, and their hybrids using near-infrared reflectance spectroscopy. Can J For 27: 1896-1900. https://doi.org/10.1139/x97-141
Bächle H, Zimmer B, Wegener G, 2012. Classification of thermally modified wood by FT-NIR spectroscopy and SIMCA. Wood Sci Technol 46: 1181-1192. https://doi.org/10.1007/s00226-012-0481-z
Bastos LF, Alessio KO, Menezes CM, Da Costa AB, Marder L, 2012. Aplicação da espectroscopia no infravermelho próximo e da análise exploratória de dados para identificação de madeiras na indústria de móveis. Tecno-Lógica 16: 25-29. https://doi.org/10.17058/tecnolog.v16i1.2670
Bergo MCJ, Pastore TCM, Coradin VTR, Wiedenhoeft AC, Braga JWB, 2016. NIRS identification of Swietenia macrophylla is robust across specimens from 27 countries. IAWAJ 37: 420-430. https://doi.org/10.1163/22941932-20160144
Borga P, Hämäläinen M, Theander O, 1992. Correlations between near-infrared spectra of wet-stored timber and the storage time in relation to the water quality. Holzforschung 46: 299-303. https://doi.org/10.1515/hfsg.1992.46.4.299
Borraz-Martínez S, Boqué R, Simó J, Mestre M, Gras A, 2019. Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy. Talanta 204: 320-328. https://doi.org/10.1016/j.talanta.2019.05.105
Braga JWB, Pastore TCM, Coradin VTR, Camargos JAA, Da Silva AR, 2011. The use of near infrared spectroscopy to identify solid wood specimens of Swietenia macrophylla, Cites Appendix II. IAWA J 32: 285-296. https://doi.org/10.1163/22941932-90000058
Brunner M, Eugster R, Trenka E, Bergamin-Strotz L, 1996. FT-NIR Spectroscopy and wood identification. Holzforschung 50: 130-134. https://doi.org/10.1515/hfsg.1996.50.2.130
Castillo R, Contreras D, Freer J, Ruiz J, Valenzuela S, 2008b. Supervised pattern recognition techniques for classification of Eucalyptus species from leaves NIR spectra. JCCHEMS 53: 1709-1713. https://doi.org/10.4067/s0717-97072008000400016
Castillo R, Otto M, Freer J, Valenzuela S, 2008a. Multivariate strategies for classification of Eucalyptus globulus genotypes using carbohydrates content and NIR spectra for evaluation of their cold resistance. J Chemom 22: 268-280. https://doi.org/10.1002/cem.1126
Cavender-Bares J, Meireles J, Couture J, Kaproth M, Kingdon C, Singh A, Serbin S, Center A, Zuniga E, Pilz G, Townsend P, 2016. Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity. Remote Sen 8: 221. https://doi.org/10.3390/rs8030221
Chow SZ. Infrared spectral study of woody tissues from four conifers. J Wood Sci 1972, 5, 27-33.
Ciarnoschi LD, De Oliveira LC, Simeone MLF, Panero FDS, Panero PDS, Rodriguez AR, Filho EGA, Pereira MG, Da Silva LM, 2022. Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum Benth organs by Near-Infrared spectrophotometry. Microchem J 180: 107- 621. https://doi.org/10.1016/j.microc.2022.107621
Clark ML, Roberts DA, 2012. Species-level differences in hyperspectral metrics among tropical rainforest trees as determined by a tree-based classifier. Remote Sen 4: 1820-1855. https://doi.org/10.3390/rs4061820
Costa ACPR, Ramalho FMG, Costa LR, Trugilho PF, Hein PRG, 2019. Classification of commercial charcoal for domestic use by near infrared spectroscopy. Biomass Bioenergy 127: 105280. https://doi.org/10.1016/j.biombioe.2019.105280
Costa LR, Trugilho PF, Hein PRG, 2018. Evaluation and classification of eucalypt charcoal quality by near infrared spectroscopy. Biomass Bioenergy 112: 8592. https://doi.org/10.1016/j.biombioe.2018.02.017
Cozzolino D, 2014. Use of infrared spectroscopy for in-field measurement and phenotyping of plant properties: instrumentation, data analysis, and examples. Appl Spectrosc Rev 49: 564-584. https://doi.org/10.1080/05704928.2013.878720
Cozzolino D, Restaino E, Fassio A, 2010. Discrimination of yerba mate (Ilex paraguayensis St.Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis. Sens Instrum Food Qual Saf 4: 67-72. https://doi.org/10.1007/s11694-010-9096-y
Cremonez VG, Klitzke RJ, Da Silva EJ, De Muñiz GIB, Nisgoski S, 2019. Influence of age on the discrimination of Tectona grandis by VIS/NIR spectroscopy. FLORAM 26: e20170159. https://doi.org/10.1590/2179-8087.015917
Danusevicius D, Masaitis G, Mozgeris G, 2014. Visible and near infrared hyperspectral imaging reveals significant differences in needle reflectance among Scots pine provenances. Silvae Genetic 63: 169-180. https://doi.org/10.1515/sg-2014-0022
Davrieux F, Rousset PLA, Pastore TCM, De Macedo LA, Quirino WF, 2010. Discrimination of native wood charcoal by infrared spectroscopy. Quim Nova 33: 1093-1097. https://doi.org/10.1590/s0100-40422010000500016
Dawson-Andoh B, Adedipe OE, 2012. Rapid spectroscopic separation of three Canadian softwoods. Wood Sci Technol 46: 1193-1202. https://doi.org/10.1007/s00226-012-0468-9
Durgante FM, Higuchi N, Almeida A, Vicentini A, 2013. Species spectral signature: discriminating closely related plant species in the Amazon with near-infrared leaf-spectroscopy. For Ecol Manag 291: 240-248. https://doi.org/10.1016/j.foreco.2012.10.045
Espinoza JA, Hodge GR, Dvorak WS, 2012. The potential use of near infrared spectroscopy to discriminate between different pine species and their hybrids. JNIRS 20: 437-447. https://doi.org/10.1255/jnirs.1006
Farhadi M, Tigabu M, Pietrzykowski M, Danusevičius D, Odén PC, 2017. Application of near infrared spectroscopy for authentication of Picea abies seed provenance. New For 48: 629-642. https://doi.org/10.1007/s11056-017-9589-1
Farhadi M, Tigabu M, Stener L-G, Odén PC, 2015. Feasibility of visible + near infrared spectroscopy for non-destructive verification of European×Japanese larch hybrid seeds. New For 47: 271-285. https://doi.org/10.1007/s11056-015-9514-4
Flæte PO, Haartveit EY, Vadla K, 2006. Near infrared spectroscopy with multivariate statistical modelling as a tool for differentiation of wood from tree species with similar appearance. N Z J For Sci 36: 382-392.
Gierlinger N, Schwanninger M, Wimmer R, 2004. Characteristics and classification of fourier-transform near infrared spectra of the heartwood of different larch species (Larix sp). JNIRS 12: 113-119. https://doi.org/10.1255/jnirs.415
Gonçalves TAP, Navarro AG, Nisgoski S, Sonsin-Oliveira J, 2021. The trees of the Water People: archeological waterlogged wood identification and near-infrared analysis in Eastern Amazonia. Wood Sci Technol 55: 991-1011. https://doi.org/10.1007/s00226-021-01288-4
Hadlich HL, Durgante FM, Santos JD, Higuchi N, Chambers JQ, Vicentini A, 2018. Recognizing Amazonian tree species in the field using bark tissues spectra. For Ecol Manag 427: 296-304. https://doi.org/10.1016/j.foreco.2018.06.002
Heim L, Brancheriau L, Marchal R, Boutahar N, Lotte S, Denaud L, Badel E, Meghar K, Candelier K, 2022. Variation analyses of extractive contents by NIR-spectroscopy bring out the differences between agroforestry and forestry walnut (Juglans regia × nigra) trees. Holzforschung 76: 781-790. https://doi.org/10.1515/hf-2022-0055
Hein PRG, Chaix G, 2014. NIR Spectral heritability: A promising tool for wood breeders? JNIRS. 22: 141-147. https://doi.org/10.1255/jnirs.1108
Horikawa Y, Mizuno-Tazuru S, Sugiyama J, 2015. Near-infrared spectroscopy as a potential method for identification of anatomically similar Japanese diploxylons. J Wood Sci 61: 251-261. https://doi.org/10.1007/s10086-015-1462-2
Huang B, Liu J, Jiao J, Lu J, Lv D, Mao J, Zhao Y, Zhang Y, 2022. Applications of machine learning in pine nuts classification. Sci Rep 12: 8799. https://doi.org/10.1038/s41598-022-12754-9
Humphreys JR, O’Reilly-Wapstra JM, Harbard JL, Davies NW, Griffin AR, Jordan GJ, Potts BM, 2008. Discrimination between seedlings of Eucalyptus globulus, E. nitens and their F1 hybrid using near-infrared reflectance spectroscopy and foliar oil content. Silvae Genet 57: 262-269. https://doi.org/10.1515/sg-2008-0040
Hwang S-W, Horikawa Y, Lee W-H, Sugiyama J, 2016. Identification of Pinus species related to historic architecture in Korea using NIR chemometric approaches. J Wood Sci 62: 156-167. https://doi.org/10.1007/s10086-016-1540-0
Jiang W, Zhou C, Han G, Via B, Swain T, Fan Z, Liu S, 2017. Classification and identification of plant fibrous material with different species using near infrared technique - a new way to approach determining biomass properties accurately within different species. Front Plant Sci 7: 1-9. https://doi.org/10.3389/fpls.2016.02000
Juola J, Hovi A, Rautiainen M, 2022. A spectral analysis of stem bark for boreal and temperate tree species. Ecol Evol 12: e8718. https://doi.org/10.1002/ece3.8718
Juola J, Hovi A, Rautiainen M, 2020. Multiangular spectra of tree bark for common boreal tree species in Europe. Silva Fenn 54. https://doi.org/10.14214/sf.10331
Kanayama H, Ma T, Tsuchikawa S, Inagaki T, 2019. Cognitive spectroscopy for wood species identification: near infrared hyperspectral imaging combined with convolutional neural networks. Analyst 144: 6438-6446. https://doi.org/10.1039/c9an01180c
Kunze DCGC, Pastore TCM, Rocha HS, Lopes PVDA, Vieira RD, Coradin VTR, Braga JWB, 2021. Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA. Microchem J 171: 106-839. https://doi.org/10.1016/j.microc.2021.106839
Kunze DCGC, Pastore TCM, Fontes PJP, Silva GCB, Sousa AG, Rocha HS, Lopes PVA, Braga JWB, 2024. NIRS technology used for traceability of Cedrela odorata L. commercial shipment in Brazil. Microchem J 199: 110-077. https://doi.org/10.1016/j.microc.2024.110077
Kurata Y, 2017. Nondestructive classification analysis of wood soaked in seawater by using near-infrared spectroscopy. For Prod J 67: 63-68. https://doi.org/10.13073/fpj-d-15-00049
Lang C, Almeida DRA, Costa FRC, 2017. Discrimination of taxonomic identity at species, genus and family levels using Fourier Transformed Near-Infrared Spectroscopy, FT-NIR. For Ecol Manag 406: 219-227. https://doi.org/10.1016/j.foreco.2017.09.003
Lang C, Costa FRC, Camargo JLC, Durgante FM, Vicentini A, 2015. Near Infrared spectroscopy facilitates rapid identification of both young and mature Amazonian tree species. PloS One. 10: e0134521. https://doi.org/10.1371/journal.pone.0134521
Lazarescu C, Hart F, Pirouz Z, Panagiotidis K, Mansfield SD, Barrett JD, Avramidis S, 2017. Wood species identification by near-infrared spectroscopy. Int Wood Prod J 8: 32-35. https://doi.org/10.1080/20426445.2016.1242270
Leandro JGR, Gonzaga FB, De Figueiredo Latorraca JV, 2019. Discrimination of wood species using laser-induced breakdown spectroscopy and near-infrared reflectance spectroscopy. Wood Sci Technol 53: 1079-1091. https://doi.org/10.1007/s00226-019-01119-7
Lengowski EC, De Muñiz GIB, Klock U, Nisgoski S, 2018. Potential use of NIR and visible spectroscopy to analyse chemical properties of thermally treated wood. Maderas Cienc Tecnol 20: 627-640. https://doi.org/10.4067/s0718-221x2018005041001
Li S, Wilkinson KL, Cozzolino D, 2014. The use of near infrared reflectance spectroscopy to identify the origin of oak shavings used in wine aging. J Food Meas Charact 8: 356-361. https://doi.org/10.1007/s11694-014-9196-1
Li Y, Altaner C, 2019. Calibration of near infrared spectroscopy (NIRS) data of three Eucalyptus species with extractive contents determined by ASE extraction for rapid identification of species and high extractive contents. Holzforschung 73: 537-545. https://doi.org/10.1515/hf-2018-0166
Li Y, Via BK, Li Y, Wang G, 2022. Determination of geographical origin and tree species using VIS-NIR and chemometric methods. For Prod J 72: 147-154. https://doi.org/10.13073/fpj-d-22-00011
Li Y, Via BK, Young T, Li Y, 2019. Visible-Near Infrared spectroscopy and chemometric methods for wood density prediction and origin/species identification. Forests 10: 1078. https://doi.org/10.3390/f10121078
Liang L, Fang G, Cui H, Wu T, Zhang X, Zhao Z, 2016. Fast identification of wood species by near infrared spectroscopy coupling with support vector machine. Chem Ind For Prod 36: 55-60. https://doi.org/10.3969/j.issn.0253-2417.2016.01.008
Lima MDR, Ramalho FMG, Trugilho PF, Bufalino L, Júnior AFD, De Paula Protásio T, Hein PRG, 2022. Classifying waste wood from Amazonian species by near-infrared spectroscopy (NIRS) to improve charcoal production. Renew Energy 193: 584-594. https://doi.org/10.1016/j.renene.2022.05.048
Lippert DB, Benedetti ACP, Pereira RS, Schuh MS, Nunes MM, Kervald L, 2014. Spectroradiometry of commercial wood veneers in the visible and near infrared spectra. Floresta 44: 125-132. https://doi.org/10.5380/rf.v44i1.28114
Loewe V, Navarro-Cerrillo RM, García-Olmo J, Riccioli C, Sánchez-Cuesta R, 2017. Discriminant analysis of Mediterranean pine nuts (Pinus pinea L.) from Chilean plantations by near infrared spectroscopy, NIRS. Food Control 73: 634-643. https://doi.org/10.1016/j.foodcont.2016.09.012
Lu W, Arnold RJ, Wang C, Lin Y, Luo J, Meder R, Yang Y, 2021. Defining Eucalyptus urophylla with its hybrid and the rules of genetic recombination using near infrared spectroscopy. JNIRS 29: 236-244. https://doi.org/10.1177/09670335211008257
Luan QF, Li YJ, Jiang JM, Yue HF, Diao SF, 2013. Discrimination of Pinus taeda x P. caribaea var. hondurensis between its allele-species and hybrids using near infrared spectroscopy. JNIRS 21: 203-211. https://doi.org/10.1255/jnirs.1051
Ma T, Inagaki T, Ban M, Tsuchikawa S, 2019. Rapid identification of wood species by near-infrared spatially resolved spectroscopy (NIR-SRS) based on hyperspectral imaging (HIS). Holzforschung 73: 323-330. https://doi.org/10.1515/hf-2018-0128
Ma T, Inagaki T, Tsuchikawa S, 2021. Demonstration of the applicability of visible and near-infrared spatially resolved spectroscopy for rapid and non-destructive wood classification. Holzforschung 75: 419-427. https://doi.org/10.1515/hf-2020-0074
Mancini LT, Ramalho FMG, Trugilho PF, Hein PRG, 2021. Estimation of total extractive content of wood from planted and native forests by near infrared spectroscopy. IForest 14: 18-25. https://doi.org/10.3832/ifor3472-013
Mancini M, Taavitsainen V, Toscano G, 2019. Comparison of three different classification methods performance for the determination of biofuel quality by means of NIR spectroscopy. J Chemom 33. https://doi.org/10.1002/cem.3145
Meder R, Kain D, Ebdon N, Macdonell P, Brawner JT, 2014. Identifying hybridisation in Pinus species using near infrared spectroscopy of foliage. JNIRS 22: 337-345. https://doi.org/10.1255/jnirs.1127
Mees C, Souard F, Delporte C, Deconinck E, Stoffelen P, Stévigny C, Kauffmann JM, De Braekeleer K, 2018. Identification of coffee leaves using FT-NIR spectroscopy and SIMCA. Talanta 177: 4-11. https://doi.org/10.1016/j.talanta.2017.09.056
Mitchell AJ, Ingle HD, Stewart CM, 1969. Infrared spectra of some woods of taxonomic interest. Wood Sci Technol 3: 93-99. https://doi.org/10.1007/bf00639633
Migacz IP, Manfron J, Farago PV, Raman V, De Muñiz GIB, Nisgoski S, 2022. VIS/NIR spectra and color parameters according to leaf age of some Eucalyptus species: influence on their classification and discrimination. For Syst 31. https://doi.org/10.5424/fs/2022312-19242
Monteiro TC, Da Silva RV, Lima JT, Hein PRG, Napoli A, 2010. Use of near infrared spectroscopy to distinguish carbonization processes and charcoal sources. Cerne 16: 381-390. https://doi.org/10.1590/s0104-77602010000300014
Muñiz GIB de, Carneiro ME, Batista FRR, Schardosin FZ, Nisgoski S, 2016. Wood and charcoal identification of five species from the miscellaneous group known in Brazil as “angelim” by near-IR and wood anatomy. Maderas. Cienc Tecnol. 18: 505-522. https://doi.org/10.4067/s0718-221x2016005000045
Muñiz GIB de, Carneiro ME, Nisgoski S, Ramirez MGL, Magalhães WLE, 2013. SEM and NIR characterization of four forest species charcoal. Wood Sci Technol 47: 815-823. https://doi.org/10.1007/s00226-013-0539-6
Nair P, Lodder RA, 1993. Near-IR identification of woods for restoration of historic buildings and furniture. Appl Spectrosc 47: 287-291. https://doi.org/10.1366/0003702934066677
Nascimento CSD, De Araújo RD, Da Silva CE, Nascimento CCD, Da Silva Menezes V, Santos JD, 2022. Near infrared spectroscopy as a tool to discriminate tannins from Amazonian species. Ciênc Agrotec 46: e001422. https://doi.org/10.1590/1413-7054202246001422
Nisgoski S, Batista FRR, Colombo AV, Passos VAD, De Muñiz GIB, 2019a. Near infrared as an auxiliary tool for wood identification in sacred art in Brazil. Braz J Wood Sci 10: 176-188. https://doi.org/10.12953/2177-6830/rcm.v10n3p176-188
Nisgoski S, Batista FRR, Naide TL, Laube NCC, Leão ACR, De Muñiz GIB, 2018a. Discrimination of wood and charcoal from six Caatinga species by near-infrared spectroscopy. Maderas. Cienc Tecnol. 20: 199-210. https://doi.org/10.4067/s0718-221x2018005002401
Nisgoski S, Carneiro ME, Lengowski EC, Schardosin FZ, De Muñiz GIB, 2015b. Potential use of visible and near-infrared spectroscopy for pine species discrimination by examination of needles. South For 77: 243-247. https://doi.org/10.2989/20702620.2015.1052947
Nisgoski S, Muñiz GIB, Morrone SR, Schardosin FZ, França RF, 2015a. NIR and anatomy of wood and charcoal from Moraceae and Euphorbiaceae species. Braz J Wood Sci 6: 183-190. https://doi.org/10.12953/2177-6830/rcm.v6n3p183-190
Nisgoski S, Santos JXD, Vieira HC, Naide TL, Stange R, Da Silva WDS, Souza DV, Gama NC, De Souza Hamada MO, 2023. Provenance identification of leaves and nuts of Bertholletia excelsa Bonpl by near-infrared spectroscopy and color parameters for sustainable extraction. Sustainability 15: 15606. https://doi.org/10.3390/su152115606
Nisgoski S, Gonçalves TAP, De Oliveira NM, Bittencourt SC, Lima GS, De Muñiz GIB, 2018b. Influence of toposequence position of Stryphnodendron adstringens trees on discrimination of samples based on spectroscopy. Braz J Wood Sci 9: 112-122. https://doi.org/10.12953/2177-6830/rcm.v9n2p112-122
Nisgoski S, Gonçalves T A P, Sonsin-Oliveira J, Ballarin AW, Muñiz GIB, 2021. Near-infrared spectroscopy for discrimination of charcoal from Eucalyptus and native Cerrado species - contribution to a database for forestry supervision. For Sci 67: 419-432. https://doi.org/10.1093/forsci/fxab015
Nisgoski S, Klock U, Batista FRR, Schardosin FZ, Consalter R, Motta ACV, 2016a. Uso do NIR na discriminação de árvores em diferentes adubações com base nos espectros da madeira e da casca. Braz J Wood Sci 7: 100-110. https://doi.org/10.12953/2177-6830/rcm.v7n2p100-110
Nisgoski S, De Muñiz G, Gonçalves T, Ballarin A, 2017a. Use of visible and near-infrared spectroscopy for discrimination of eucalypt species by examination of solid samples. JTFS 29: 371-379. https://doi.org/10.26525/jtfs2017.29.3.371379
Nisgoski S, De Oliveira AA, De Muñiz GIB, 2017b. Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra. Wood Sci Technol 51: 929-942. https://doi.org/10.1007/s00226-017-0915-8
Nisgoski S, Schardosin FZ, Batista FRR, De Muñiz GIB, Carneiro ME, 2016b. Potential use of NIR spectroscopy to identify Cryptomeria japonica varieties from southern Brazil. Wood Sci Technol 50: 71-80. https://doi.org/10.1007/s00226-015-0783-z
Nisgoski S, Vieira HC, Gonçalves TAP, Afonso CM, De Muñiz GIB, 2019b. Impact of carbonization parameters on anatomic aspects and near-infrared spectra of three species from Mozambique. Wood Sci Technol 53: 1373-1394. https://doi.org/10.1007/s00226-019-01134-8
Nisgoski S, Vieira HC, Gonçalves TAP, Afonso CM, De Muñiz GIB, 2020. Charcoal anatomy and NIR spectra of Spirostachys africana, Terminalia sp. and Colophospermum mopane in different carbonization process. SN Applied Sciences 2: 908. https://doi.org/10.1007/s42452-020-2719-y
Novaes TV, Ramalho FMG, Da Silva Araújo E, Lima MDR, Da Silva MG, Ferreira GC, Hein PRG, 2023. Discrimination of Amazonian forest species by NIR spectroscopy: wood surface effects. Eur J Wood Wood Prod 81: 159-172. https://doi.org/10.1007/s00107-022-01862-y
O’Reilly-Wapstra JM, Freeman JS, Barbour R, Vaillancourt RE, Potts BM, 2013. Genetic analysis of the near-infrared spectral phenome of a global Eucalyptus species. Tree Genetics & Genomes 9: 943-959. https://doi.org/10.1007/s11295-013-0607-y
Pace J-HC, De Figueiredo Latorraca J-V, Hein P-RG, De Carvalho AM, Castro JP, Da Silva C-ES, 2019. Wood species identification from Atlantic forest by near infrared spectroscopy. For Syst 28: 1-15. https://doi.org/10.5424/fs/2019283-14558
Pan X, Li K, Chen Z, Yang Z, 2021. Identifying wood based on near-infrared spectra and four gray-level co-occurrence matrix texture features. Forests 12: 1527-1535. https://doi.org/10.3390/f12111527
Pan X, Qiu J, Yang Z, 2022. Identification of five similar cinnamomum wood species using portable near-infrared spectroscopy. Spectrosc 49: 16-23. https://doi.org/10.56530/spectroscopy.zg7089n4
Park SY, Kim JH, Kim JC, Yang SY, Yeo H, Choi IG, 2021. Classification of softwoods using wood extract information and near infrared spectroscopy. Bioresources 16: 5301-5312. https://doi.org/10.15376/biores.16.3.5301-5312
Park SY, Kim JC, Kim JH, Yang SY, Kwon O, Yeo H, Cho KC, Choi IG, 2017. Possibility of wood classification in Korean softwood species using near-infrared spectroscopy based on their chemical compositions. J Korean Wood Sci Technol 45: 202-212. https://doi.org/10.5658/wood.2017.45.2.202
Pasquini C, 2018. Near infrared spectroscopy: A mature analytical technique with new perspectives–A review. Anal Chimi Acta 1026: 8-36.
Pastore TCM, Braga JWB, Coradin VTR, Magalhães WLE, Okino EYA, Camargos JAA, De Muñiz GIB, Bressan OA, Davrieux F, 2011. Near infrared spectroscopy (NIRS) as a potential tool for monitoring trade of similar woods: Discrimination of true mahogany, cedar, andiroba, and curupixá. Holzforschung 65: 73-80. https://doi.org/10.1515/hf.2011.010
Prades C, García-Olmo J, Romero-Prieto T, De Ceca JLG, López-Luque R, 2010. Methodology for cork plank characterization (Quercus suber L.) by near-infrared spectroscopy and image analysis. Meas Sci Technol 21: 065602. https://doi.org/10.1088/0957-0233/21/6/065602
Ramalho FMG, Andrade JM, Hein PRG, 2018. Rapid discrimination of wood species from native forest and plantations using near infrared spectroscopy. For Syst 27: e008. https://doi.org/10.5424/fs/2018272-12075
Ramalho FMG, Hein PRG, Andrade JM, Napoli A, 2017. Potential of near-infrared spectroscopy for distinguishing charcoal produced from planted and native wood for energy purpose. Energ Fuel. 31: 1593-1599. https://doi.org/10.1021/acs.energyfuels.6b02446
Rana R, Müller G, Naumann A, Polle A, 2008. FTIR spectroscopy in combination with principal component analysis or cluster analysis as a tool to distinguish beech (Fagus sylvatica L.) trees grown at different sites. Holzforschung 62: 530-538. https://doi.org/10.1515/hf.2008.104
Raobelina AC, Chaix G, Razafimahatratra AR, Rakotoniaina SP, Ramananantoandro T, 2023. Use of a portable near infrared spectrometer for wood identification of four Dalbergia species from Madagascar. Wood Fiber Sci 55: 4-17. https://doi.org/10.22382/wfs-2023-03
Reis CA, Cisneros AB, Da Silva EL, De Muñiz GIB, Morrone SR, Nisgoski S, 2019. NIR spectroscopy and wood anatomy to distinguish Prosopis alba wood and charcoal from natural and planted forest. Int Wood Prod J 10: 168-177. https://doi.org/10.1080/20426445.2019.1701173
Richardson AD, Reeves JB, Gregoire TG, 2003. Multivariate analyses of visible/near infrared (VIS/NIR) absorbance spectra reveal underlying spectral differences among dried, ground conifer needle samples from different growth environments. New Phytol 161: 291-301. https://doi.org/10.1046/j.1469-8137.2003.00913.x
Rocha HS, Braga JWB, Kunze DCGC, Coradin VTR, Pastore TCM, 2021. Identification of mahogany sliced veneer using handheld near-infrared spectroscopy device and multivariate data analysis. IAWA J 42: 336-347. https://doi.org/10.1163/22941932-bja10054
Rojas-Rioseco M, Del P Castillo R, González-Campos J, Ipinza R, Sanhueza MI, Hasbún R, 2022. Phylogeographic origin authentication of Araucaria araucana, Mol.) K Koch seedlings through the application of spectroscopy techniques in different infrared ranges and chemometric methods. New For 54: 467-489. https://doi.org/10.1007/s11056-022-09933-x
Russ A, Fišerová M, Gigac J, 2009. Preliminary study of wood species identification by NIR spectroscopy. Wood Research 54: 23-32.
Sandak A, Sandak J, Negri M, 2011. Relationship between near-infrared (NIR) spectra and the geographical provenance of timber. Wood Sci Technol 45: 35-48. https://doi.org/10.1007/s00226-010-0313-y
Sandak A, Sandak J, Pradzynski W, Zborowska M, Negri M, 2009. Near infrared spectroscopy as a tool for characterization of wood surface. Folia For Pol 40: 31-40.
Sandak A, Sandak J, Zborowska M, Prądzyński W, 2010. Near infrared spectroscopy as a tool for archaeological wood characterization. J Archaeol Sci 37: 2093-2101. https://doi.org/10.1016/j.jas.2010.02.005
Santos JXD, Vieira HC, Silva EL, Muñiz GIB, Soffiatti P, Nisgoski S, 2020. Near infrared spectroscopy for separation of tauari wood in Brazilian amazon native forest. JTFS 32: 227-236. https://doi.org/10.26525/jtfs2020.32.3.227
Santos JXD, Vieira HC, Souza DV, De Menezes MC, De Muñiz GIB, Soffiatti P, Nisgoski S, 2021. Discrimination of “Louros” wood from the Brazilian Amazon by near-infrared spectroscopy and machine learning techniques. Eur J Wood Wood Prod 79: 989-998. https://doi.org/10.1007/s00107-021-01685-3
Schimleck LR, Michell AJ, Vinden P, 1996. Eucalypt wood classification by NIR spectroscopy and principal components analysis. Appita J 49: 319-324.
Shou G, Zhang W, Gu Y, Zhao D, 2014. Application of near infrared spectroscopy for discrimination of similar rare woods in the Chinese market. JNIRS 22: 423-432. https://doi.org/10.1255/jnirs.1136
Silva ALD da, Filho EGA, Silva LMA, Tavares OCH, Pereira MG, De Campos T, Da Silva LM, 2021. Near infrared spectroscopy to rapid assess the rubber tree clone and the influence of maturation and disease at the leaves. Microchem J 168: 106478. https://doi.org/10.1016/j.microc.2021.106478
Silva CE da, Nascimento CS, Freitas JA, Araújo RD, Durgante FM, Zartman CE, Nascimento CC, Higuchi N, 2024. Alternative identification of wood from natural fallen trees of the Lecythidaceae family in the Central Amazonian using FT-NIR spectroscopy. Int For Rev 26: 29-44. https://doi.org/10.1505/146554824838457844
Silva DC, Pastore TCM, Soares LF, De Barros FAS, Bergo MCJ, Coradin VTH, Gontijo AB, Sosa MH, Chacón CB, Braga JWB, 2018. Determination of the country of origin of true mahogany (Swietenia macrophylla King) wood in five Latin American countries using handheld NIR devices and multivariate data analysis. Holzforschung 72: 521-530. https://doi.org/10.1515/hf-2017-0160
Snel FA, Braga JWB, Da Silva D, Wiedenhoeft AC, Costa A, Soares R, Coradin VTR, Pastore TCM, 2018. Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES. Wood Sci Technol 52: 1411-1427. https://doi.org/10.1007/s00226-018-1027-9
Soares LF, Da Silva DC, Bergo MCJ, Coradin VTR, Braga JWB, Pastore TCM, 2017. Avaliação de espectrômetro NIR portátil e PLS-DA para a discriminação de seis espécies similares de madeira amazônicas. Quím Nova 40: 418-426. https://doi.org/10.21577/0100-4042.20170014
Sohi A, Avramidis S, Mansfield S, 2017. Near-infrared spectroscopic separation of green chain sub-alpine fir lumber from a spruce-pine-fir mix. Bioresources 12. https://doi.org/10.15376/biores.12.2.3720-3727
Tigabu M, Farhadi M, Stener LG, Odén P, 2018. Visible + near infrared spectroscopy as taxonomic tool for identifying birch species. Silva Fenn 52. https://doi.org/10.14214/sf.9996
Tigabu M, Oden PC, Lindgren D, 2005. Identification of seed sources and parents of Pinus sylvestris L. using visible–near infrared reflectance spectra and multivariate analysis. Trees 19: 468-476. https://doi.org/10.1007/s00468-005-0408-5
Toscano G, Rinnan Å, Pizzi A, Mancini M, 2017. The use of near-infrared (NIR) spectroscopy and principal component analysis (PCA) to discriminate bark and wood of the most common species of the pellet sector. Energ Fuel 31: 2814-2821. https://doi.org/10.1021/acs.energyfuels.6b02421
Tsuchikawa S, Inagaki T, Ma T, 2023. Application of near-infrared spectroscopy to forest and wood products. Curr For Rep 9: 401-412. https://doi.org/10.1007/s40725-023-00203-3
Tsuchikawa S, Inoue K, Noma J, Hayashi K, 2003a. Application of near-infrared spectroscopy to wood discrimination. J Wood Sci 49: 29-35. https://doi.org/10.1007/s100860300005
Tsuchikawa S, Yamato K, Inoue K, 2003b. Discriminant analysis of wood-based materials using near-infrared spectroscopy. J Wood Sci 49: 275-280. https://doi.org/10.1007/s10086-002-0471-0
Tsuchikawa S, Kobori H, 2015. A review of recent application of near infrared spectroscopy to wood science and technology. J Wood Sci 61: 213-220
Tuncer FD, 2022. Comparing modelling performance of chemometric methods for wood discrimination by near infrared spectroscopy. Wood Mater Sci Eng 18: 422-433. https://doi.org/10.1080/17480272.2022.2039960
Tuncer FD, Dogu D, Akdeniz E, 2021. Efficiency of preprocessing methods for discrimination of anatomically similar pine species by NIR spectroscopy. Wood Mater Sci Eng 18: 212-221. https://doi.org/10.1080/17480272.2021.2012821
Vieira HC, Santos JXD, Souza DV, Rios PDA, De Muñiz GIB, Morrone SR, Nisgoski S, 2020. Near-infrared spectroscopy for the distinction of wood and charcoal from Fabaceae species: comparison of ANN, KNN and SVM models. For Syst 29: e020. https://doi.org/10.5424/fs/2020293-16965
Vieira HC, Santos JXD, Da Silva EL, Rios PDA, De Muñiz GIB, Morrone SR, Nisgoski S, 2021. Potential of the near-infrared spectroscopy for the discrimination of wood and charcoal of four native Myrtaceae species in southern Brazil. Wood Mater Sci Eng 16: 188-195. https://doi.org/10.1080/17480272.2019.1689296
Wan Z, Yang H, Xu J, Mu H, Qi D, 2024. BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification. J For Res 35. https://doi.org/10.1007/s11676-023-01652-z
Wang CK, Zhao P, Li ZY, Li XH, 2022. Comparison of VIS/NIR spectral curves plus RGB images with hyperspectral images for the identification of Pterocarpus species. Holzforschung 76: 579-591. https://doi.org/10.1515/hf-2021-0194
Wang Y, Xiang J, Tang Y, Chen W, Xu Y, 2021. A review of the application of near-infrared spectroscopy (NIRS) in forestry. Appl Spectrosc Rev 57: 300-317.
Watanabe K, Hart F, Mansfield SD, Avramidis S, 2010. Detection of wet-pockets on the surface of Tsuga heterophylla Raf. Sarg. by near infrared spectroscopy. Holzforschung 64: 55-60. https://doi.org/10.1515/hf.2010.014
Watanabe K, Abe H, Kataoka Y, Noshiro S, 2011. Species separation of aging and degraded solid wood using near infrared spectroscopy. Jpn J Histor Bot 19: 117-124.
Xie J, Qi J, Huang X, Zhou N, Hu Y, 2015. Comparative analysis of modern and ancient buried Phoebe zhennan wood: surface color, chemical components, infrared spectroscopy, and essential oil composition. J For Res 26: 501-507. https://doi.org/10.1007/s11676-015-0034-z
Xu L, Yan SM, Cai CB, Zhong W, Yu XP, 2013. Rapid analysis of geographical origins and age of Torreya grandis seeds by NIR spectroscopy and pattern recognition methods. J Spectrosc 2013: 1-9. https://doi.org/10.1155/2013/468509
Xue X, Chen Z, Wu H, Gao H, 2022. Identification of Guiboutia species by NIR-HSI spectroscopy. Sci Rep 12:11507. https://doi.org/10.1038/s41598-022-15719-0
Yang SY, Kwon O, Park Y, Chung H, Kim H, Park SY, Choi IG, Yeo H, 2020. Application of neural networks for classifying softwood species using near infrared spectroscopy. JNIRS 28: 298-307. https://doi.org/10.1177/0967033520939320
Yang SY, Park Y, Chung H, Kim H, Park SY, Choi IG, Kwon O, Yeo H, 2019. Soft independent modelling of class analogy for classifying lumber species using their Near-Infrared spectra. J Korean Wood Sci Technol 47: 101-
https://doi.org/10.5658/wood.2019.47.1.101
Yang Z, Jiang ZH, Lü B, 2012b. Investigation of near infrared spectroscopy of rosewood. Spectrosc Spect Anal 32(9): 2405-2408. https://doi.org/10.3964/j.issn.1000-0593(2012)09-2405-04
Yang Z, Liu Y, Pang X, Li K, 2015. Preliminary investigation into the identification of wood species from different locations by near infrared spectroscopy. Bioresources 10(4): 8505-8517. https://doi.org/10.15376/biores.10.4.8505-8517
Yang Z, Lü B, Huang AM, Liu YN, Xie XQ, 2012a. Rapid identification of softwood and hardwood by near infrared spectroscopy of cross-sectional surfaces. Spectrosc Spect Anal 32(7): 1785-1789. https://doi.org/10.3964/j.issn.1000-0593(2012)07-1785-05
Yeon S, Park SY, Kim JH, Kim JC, Yang SY, Yeo H, Kwon O, Choi IG, 2019. Effect of organic solvent extractives on Korean softwoods classification using near-infrared spectroscopy. J Korean Wood Sci Technol 47(4): 509-518. https://doi.org/10.5658/wood.2019.47.4.509
Zhang X, Yu H, Li B, Li WJ, Li X, Bao C, 2014. Discrimination of Pinus yunnanensis, P. kesiya and P. densata by FT-NIR. JOCPR 6(4): 142-149.
Zhao P, Li ZY, Wang CK, 2021. Wood species recognition based on visible and near-infrared spectral analysis using fuzzy reasoning and decision-level fusion. J Spectrosc :1-16. https://doi.org/10.1155/2021/6088435
Zhou Z, Rahimi S, Avramidis S, 2020a. On-line species identification of green hem-fir timber mix based on near infrared spectroscopy and chemometrics. Eur J Wood Wood Prod 78: 151-160. https://doi.org/10.1007/s00107-019-01479-8
Zhou Z, Rahimi S, Avramidis S, Fang Y, 2020b. Species- and moisture-based sorting of green timber mix with near infrared spectroscopy. Bioresources 15: 317-330. https://doi.org/10.15376/biores.15.1.317-330
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