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

• Published in: Postharvest biology and technology Vol. 75; pp. 9 - 16
• Main Authors: Wedding, B.B., Wright, C., Grauf, S., White, R.D., Tilse, B., Gadek, P.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.01.2013; Elsevier
• Subjects: absorbance; Avocado; avocados; Biological and medical sciences; carbohydrates; Dry matter; dry matter content; Eating quality; Food industries; Fruit; Fruit and vegetable industries; fruits; Fundamental and applied biological sciences. Psychology; Maturity; model validation; Near infrared spectroscopy; oils; prediction; reflectance; seasonal variation; spectroscopy; statistics; Near infrared spectroscopy; Avocado; Dry matter; Fruit; Maturity; Eating quality; Variability; Persea americana; Prediction; Dry matter content; Near infrared spectrometry; Palatability; Seasonal variation; Dicotyledones; Angiospermae; Spermatophyta; Fourier-transformed infrared spectrometry; Lauraceae
• ISSN: 0925-5214; 1873-2356
• DOI: 10.1016/j.postharvbio.2012.04.016

► The ability of FT-NIR spectroscopy in diffuse reflectance mode to non-invasively predict dry matter content of whole intact Hass avocado was assessed. ► The ability of FT-NIR to account for seasonal variation of dry matter content of Hass avocado from three consecutive growing seasons from the same farm was assessed. ► Results highlight the importance of including seasonal variation in the development of a calibration model for such prediction. ► Results indicate the potential of FT-NIR spectroscopy in diffuse reflectance to be used in an inline setting. 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 1700–1790nm 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.