Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Wedding, B.B., Wright, C., Grauf, S., White, Ronald, Tilse, B., and Gadek, P. (2013) Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit. Postharvest Biology and Technology, 75. pp. 9-16.

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Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact 'Hass' avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The Rᵥ² and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but ≤ 1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of Rᵥ² = 0.89, RMSEP = 1.43% dry matter with a bias of -0.021 in the range 16.1-39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890-980, 1005-1050, 1330-1380 and 1700-1790 nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole 'Hass' avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation.

Item ID: 24540
Item Type: Article (Research - C1)
ISSN: 0925-5214
Keywords: fruit, avocado, dry matter, maturity, near infrared spectroscopy, eating quality
Funders: Australian Research Council (ARC)
Date Deposited: 09 Jan 2013 05:37
FoR Codes: 07 AGRICULTURAL AND VETERINARY SCIENCES > 0706 Horticultural Production > 070605 Post Harvest Horticultural Technologies (incl Transportation and Storage) @ 100%
SEO Codes: 82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8202 Horticultural Crops > 820204 Hops @ 100%
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