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Reflectance spectroscopy and machine learning as a tool for the categorization of twin species based on the example of the Diachrysia genus

cris.lastimport.scopus2025-10-23T06:55:23Z
cris.virtual.author-orcid0000-0002-7825-3273
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cris.virtual.author-orcid0000-0002-3512-1350
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cris.virtualsource.author-orcida0620c5e-2f34-4cfa-8f0b-5819132ad9fd
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cris.virtualsource.author-orcid884ee7cf-babf-4820-934b-f49db8b5e1b5
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dc.abstract.enIn our work we used noninvasive point reflectance spectroscopy in the range from 400 to 2100 nm coupled with machine learning to study scales on the brown and golden iridescent areas on the dorsal side of the forewing of Diachrysia chrysitis and D. stenochrysis. We used our approach to distinguish between these species of moths. The basis for the study was a statistically significant collection of 95 specimens identified based on morphological feature and gathered during 23 years in Poland. The numerical part of an experiment included two independent discriminant analyses: stochastic and deterministic. The more sensitive stochastic approach achieved average compliance with the species identification made by entomologists at the level of 99–100%. It demonstrated high stability against the different configurations of training and validation sets, hence strong predictors of Diachrysia siblings distinctiveness. Both methods resulted in the same small set of relevant features, where minimal fully discriminating subsets of wavelengths were three for glass scales on the golden area and four for the brown. The differences between species in scales primarily concern their major components and ultrastructure. In melanin-absent glass scales, this is mainly chitin configuration, while in melanin-present brown scales, melanin reveals as an additional factor.
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Bioinżynierii
dc.affiliation.instituteKatedra Entomologii i Ochrony Środowiska
dc.contributor.authorDyba, Krzysztof
dc.contributor.authorWąsala, Roman
dc.contributor.authorPiekarczyk, Jan
dc.contributor.authorGabała, Elżbieta
dc.contributor.authorGawlak, Magdalena
dc.contributor.authorJasiewicz, Jarosław
dc.contributor.authorRatajkiewicz, Henryk
dc.date.access2025-10-07
dc.date.accessioned2025-10-07T08:31:52Z
dc.date.available2025-10-07T08:31:52Z
dc.date.copyright2022-02-17
dc.date.issued2022
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if4,4
dc.description.number15 May 2022
dc.description.points140
dc.description.versionfinal_published
dc.description.volume273
dc.identifier.doi10.1016/j.saa.2022.121058
dc.identifier.issn1386-1425
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5223
dc.identifier.weblinkhttps://www.sciencedirect.com/science/article/pii/S1386142522002062
dc.languageen
dc.relation.ispartofSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
dc.relation.pagesart. 121058
dc.rightsCC-BY-NC-ND
dc.sciencecloudnosend
dc.share.typeOTHER
dc.subject.enchemometry
dc.subject.enLDA
dc.subject.enrandom forest
dc.subject.enLepidoptera
dc.subject.enNoctuidae
dc.titleReflectance spectroscopy and machine learning as a tool for the categorization of twin species based on the example of the Diachrysia genus
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.volume273