Akpolat, HacerBarineau, MarkJackson, Keith A.Aykas, Didem P.Rodriguez-Saona, Luis E.2024-10-042024-10-0420200023-64381096-1127https://doi.org/10.1016/j.lwt.2020.109164http://hdl.handle.net/20.500.12403/3608Our 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.eninfo:eu-repo/semantics/closedAccessInfrared spectroscopyTomato qualityPrediction algorithmsArticle12410.1016/j.lwt.2020.1091642-s2.0-85079613396Q1WOS:000525726800037Q1