Prediction of storage disorders of kiwifruit ( Actinidia chinensis) based on visible-NIR spectral characteristics at harvest

• Published in: Postharvest biology and technology 2004-05, Vol.32 (2), p.147-158
• Main Authors: Clark, C.J., McGlone, V.A., De Silva, H.N., Manning, M.A., Burdon, J., Mowat, A.D.
• Format: Article
• Language: English
• Subjects: Actinidia chinensis; Biological and medical sciences; classification; cold storage; color; crops; discriminant analysis; Food industries; Fruit; Fruit and vegetable industries; fruits; Fundamental and applied biological sciences. Psychology; Kiwifruit; lead; Multivariate analysis; near-infrared spectroscopy; NIR; Non-destructive testing; postharvest diseases; prediction; reflectance; soluble solids; sorting; Storage disorders; storage quality; storage time; wavelengths
• ISSN: 0925-5214; 1873-2356
• DOI: 10.1016/j.postharvbio.2003.11.004

Visible (VIS)-near infrared (NIR) analysis (300–1140 nm) was performed on 15,000 kiwifruit ( Actinidia chinensis Planch. var . chinensis ‘Hort16A’) sampled on three occasions across commercial harvest, for the purpose of predicting their storage potential during a 24-week cold (−1.5 to 1.5 °C) storage period. Destructive measurements for dry matter (DM), soluble solids content (SSC), and flesh colour were also determined on an additional set of cohorts ( N=3600) to develop predictive models with NIR spectral properties. Nineteen percent of all fruit developed disorders during storage, the dominant loss category being rots on chill-injured fruit. Canonical discriminant analysis (CDA) was used to optimise the separation between the categories ‘sound’ fruit and those developing any disorder, using relative reflectance intensities at 227 wavelengths at harvest as quantitative variables. By using CDA classification and sorting, it was estimated that the overall incidence of disorders could have been reduced from 33.9 to 17.9% at our earliest harvest, and from 14.7 to 8.5% at the second harvest. Where the categories were ‘sound’ fruit and those affected by a single disorder—chill-injury—estimates based on classification by CDA across all harvests indicated a reduction in disorder incidence from 13.7 to 6.8% could have been achieved. Fruit that eventually developed chill-injury and rots were found to be those less mature at harvest, i.e., based on their NIR profile, the population of affected fruit contained less DM, appreciably lower SSC and greener flesh colour than their unaffected counterparts. Extrapolating these results to an on-line setting suggests classification into ‘sound’ and ‘affected’ groupings following NIR analysis at harvest could identify the least mature fruit and lead to a useful reduction in the incidence of postharvest storage disorders in this crop.