Determination of pesticide residual levels in strawberry (Fragaria) by near-infrared spectroscopy

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dc.authoridAyvaz, Huseyin/0000-0001-9705-6921
dc.contributor.authorYazici, Arzu
dc.contributor.authorTiryaki, Gulgun Yildiz
dc.contributor.authorAyvaz, Huseyin
dc.date.accessioned2025-01-27T20:43:36Z
dc.date.available2025-01-27T20:43:36Z
dc.date.issued2020
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractBACKGROUND In this study, an infrared-based prediction method was developed for easy, fast and non-destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria x ananassa Duch, cv. Albion) samples using near-infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near-infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic-mass spectrometric analysis. RESULTS AND CONCLUSION Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross-validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. (c) 2019 Society of Chemical Industry
dc.description.sponsorshipCanakkale Onsekiz Mart University, Scientific Research Coordination Unit [FYL-2017-1124]
dc.description.sponsorshipThis work was supported by Canakkale Onsekiz Mart University, Scientific Research Coordination Unit (Project number FYL-2017-1124).
dc.identifier.doi10.1002/jsfa.10211
dc.identifier.endpage1989
dc.identifier.issn0022-5142
dc.identifier.issn1097-0010
dc.identifier.issue5
dc.identifier.pmid31849062
dc.identifier.scopus2-s2.0-85079160923
dc.identifier.scopusqualityQ1
dc.identifier.startpage1980
dc.identifier.urihttps://doi.org/10.1002/jsfa.10211
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24284
dc.identifier.volume100
dc.identifier.wosWOS:000511019600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of The Science of Food and Agriculture
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectChemometrics
dc.subjectnear-infrared
dc.subjectpesticide residue
dc.subjectPLSR
dc.subjectstrawberry
dc.titleDetermination of pesticide residual levels in strawberry (Fragaria) by near-infrared spectroscopy
dc.typeArticle

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