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Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries.

Michela PalumboRosaria CozzolinoCarmine LaurinoLivia MalorniGianluca PicarielloFrancesco SianoMatteo StoccheroMaria CefolaAntonia CorvinoRoberto RomanielloGiuliana Gorrasi
Published in: Foods (Basel, Switzerland) (2022)
Electronic nose (e-nose), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and image analysis (IA) were used to discriminate the ripening stage (half-red or red) of strawberries (cv Sabrosa, commercially named Candonga), harvested at three different times (H1, H2 and H3). Principal component analysis (PCA) performed on the e-nose, ATR-FTIR and IA data allowed us to clearly discriminate samples based on the ripening stage, as in the score space they clustered in distinct regions of the plot. Moreover, a correlation analysis between the e-nose sensor and 57 volatile organic compounds (VOCs), which were overall detected in all the investigated fruit samples by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), allowed us to distinguish half-red and red strawberries, as the e-nose sensors gave distinct responses to samples with different flavours. Three suitable broad bands were individuated by PCA in the ATR-FTIR spectra to discriminate half-red and red samples: the band centred at 3295 cm -1 is generated by compounds that decline, whereas those at 1717 cm -1 and at 1026 cm -1 stem from compounds that accumulate during ripening. Among the chemical parameters (titratable acidity, total phenols, antioxidant activity and total soluble solid) assayed in this study, only titratable acidity was somehow correlated to ATR-FTIR and IA patterns. Thus, ATR-FTIR spectroscopy and IA might be exploited to rapidly assess titratable acidity, which is an objective indicator of the ripening stage.
Keyphrases
  • gas chromatography mass spectrometry
  • dna damage response
  • high resolution
  • single molecule
  • deep learning
  • gas chromatography
  • quantum dots
  • artificial intelligence