Portable infrared sensing technology for phenotyping chemical traits in fresh market tomatoes

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dc.authoridRodriguez-Saona, Luis/0000-0002-6615-1296
dc.contributor.authorAkpolat, Hacer
dc.contributor.authorBarineau, Mark
dc.contributor.authorJackson, Keith A.
dc.contributor.authorAykas, Didem P.
dc.contributor.authorRodriguez-Saona, Luis E.
dc.date.accessioned2024-10-04T18:52:40Z
dc.date.available2024-10-04T18:52:40Z
dc.date.issued2020
dc.departmentBayburt Üniversitesien_US
dc.description.abstractOur objective was to develop predictive regression algorithms based on infrared spectroscopy to screen for selected quality traits directed at optimizing the selection capabilities of fresh market tomatoes. Fresh tomato (681) samples were harvested from multiple locations (Florida, Virginia, California and South Carolina) during the 2016 and 2018 seasons at various ripening stages. Spectra were collected by transmittance and attenuated total reflectance (ATR) either from the tomato surface or juice. Reference methods included soluble solid content, titratable acidity, sugars, organic acids, and lycopene. Partial least squares regression using surface spectra showed good correlation for lycopene and ascorbic acid (r(cv) > 0.9) but modest correlation coefficients (r(cv) 0.54-0.80) for all other traits, while juice spectra gave high correlation coefficients (r(cv) > 0.94) and excellent predictive performance (RPD range 3-10) for all quality traits except ascorbic acid (r(cv) > 0.79). Multiple quality traits were simultaneously determined by using a single drop of sample providing fast (< 1 min) measurements and minimal sample preparation based on unique spectral fingerprints.en_US
dc.description.sponsorshipLipman Family Farms (Florida, US)en_US
dc.description.sponsorshipThis study was supported by Lipman Family Farms (Florida, US) through a research grant and by providing tomato samples.en_US
dc.identifier.doi10.1016/j.lwt.2020.109164
dc.identifier.issn0023-6438
dc.identifier.issn1096-1127
dc.identifier.scopus2-s2.0-85079613396en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.lwt.2020.109164
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3608
dc.identifier.volume124en_US
dc.identifier.wosWOS:000525726800037en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofLwt-Food Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInfrared spectroscopyen_US
dc.subjectTomato qualityen_US
dc.subjectPrediction algorithmsen_US
dc.titlePortable infrared sensing technology for phenotyping chemical traits in fresh market tomatoesen_US
dc.typeArticleen_US

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