Portable infrared sensing technology for phenotyping chemical traits in fresh market tomatoes
Küçük Resim Yok
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Our 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.
Açıklama
Anahtar Kelimeler
Infrared spectroscopy, Tomato quality, Prediction algorithms
Kaynak
Lwt-Food Science and Technology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
124
