The application of FT-NIRS for the detection of bruises and the prediction of rot susceptibility of 'Hass' avocado fruit.

Background: A rapid non-destructive in-line grading system that can rapidly and accurately assess individual avocado fruit for internal quality attributes, including bruises and rots, would allow the avocado industry to provide a more consistent fruit quality to the consumer, optimise market distribution and ensure maximum yield for the producer and retailer. Fourier transform-near-infrared (FT-NIR) spectroscopy was investigated to detect bruises and rot susceptibility as an indication of shelf-life in avocado fruit at both the sprung stage of ripeness and eating ripe fruit.

Results: The classification models (principal component linear discriminant analysis, partial least squares discriminant analysis and support vector machine) for each of three growing seasons found hard green fruit that were deliberately bruised could be correctly detected with 70-78% accuracy after 2-5 h following impact damage and with 83-89% accuracy after 24 h.

Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy.

Background: The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit.