The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm−1. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R2 values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended.

FT-NIR Spectroscopy for the Quality Characterization of Apricots (Prunus Armeniaca L.)

BERARDINELLI, ANNACHIARA;CEVOLI, CHIARA;SILAGHI, FLORINA AURELIA;FABBRI, ANGELO;RAGNI, LUIGI;GIUNCHI, ALESSANDRO;
2010

Abstract

The nondestructive assessment of apricot fruit quality (Bora cultivar) was carried out by means of FT-NIR reflectance spectroscopy in the wavenumber range 12000 to 4000 cm−1. Samples were harvested at four different ripening stages and scanned by a fiber optical probe immediately after harvesting and after a storage of 3 d (2 d at 4 °C and 1 d at 18 °C); the flesh firmness (FF), the soluble solids content (SSC), the acidity (A), and the titratable acidity (malic and citric acids) were then measured by destructive methods. Soft independent modeling of class analogy (SIMCA) analysis was used to classify spectra according to the ripening stage and the storage: partial least squares regression (PLS) models to predict FF, SSC, A, and the titratable acidity were also set-up for both just harvested and stored apricots. Spectral pretreatments and wavenumber selections were conducted on the basis of explorative principal component analysis (PCA). Apricot spectra were correctly classified in the right class with a mean classification rate of 87% (range: 80% to 100%). Test set validations of PLS models showed R2 values up to 0.620, 0.863, 0.842, and 0.369 for FF, SSC, A, and the titratable acidity, respectively. The best models were obtained for the SSC and A and are suitable for rough screening; a lower power prediction emerged for the other maturity indices and the relative predictive models are not recommended.
JOURNAL OF FOOD SCIENCE
Berardinelli A; Cevoli C; Silaghi F A; Fabbri A; Ragni L; Giunchi A; Bassi D
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/92513
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