Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and temperature. (October 2020)

• Record Type: Journal Article
• Authors: Babellahi, Farahmand; Amodio, Maria L.; Marini, Federico; Chaudhry, Muhammad M.A.; de Chiara, Maria L.V.; Mastrandrea, Leonarda; Colelli, Giancarlo
• Is Part Of: Biosystems engineering. Volume 198(2020)
• Page Start: 137
• Page End: 146
• Publication Date: 2020-10
• Subjects: Eggplant -- Chilling injury -- ASCA -- PLS-DA -- rDCV -- NIR spectroscopy; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2020.08.008
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : The early non-destructive detection of chilling injury (CI) in aubergine fruit was investigated using spectroscopy. CI is a physiological disorder that occurs when the fruit is subjected to temperatures lower than 12 °C. Reference measurements of CI were acquired by visual appearance analysis, measuring electrolyte leakage (EL), mass loss and firmness evaluations which demonstrated that even before three days of storage at 2 °C, the CI process was initiated. An ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of temperature and storage time on the Fourier transform – near infra-red (FT-NIR) spectral fingerprints. The ASCA model demonstrated that temperature, duration of storage, and their interaction had a significant effect on the spectra. In addition, it was possible to highlight the main variations in the experimental results with reference to the effects of the main factors, and with respect to storage time, to discover any major monotonic trends with time. Partial least squares-discriminant analysis (PLS-DA) was used as a supervised classification method to discriminate between fruit based on chilling and safe temperatures. In this case, only significant spectral wavebands which were significantly influenced by the effect of temperature based on ASCA were utilised. PLS-DA prediction accuracy was 87.4 ± 2.7% as estimated by a repeated double-cross-validation procedure (50 runs) and the significance of the observed discrimination was verified by means of permutation tests. The outcomes of this study indicate a promising potential for near infra-red spectroscopy (NIRS) to provide non-invasive, rapid and reliable detection of CI in aubergine fruit. Graphical abstract: Image 1 Highlights: ASCA method was applied to aubergine fruit stored at safe and chilling temperature. All main factors and their interaction had a significant effect on FT-NIR spectra. The most affected spectra introduced by ASCA were assigned as PLS-DA input. PLS-DA successfully classified aubergine based on their storage temperature.