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Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging

Lookup NU author(s): Dr Barbara Sturm, Stuart Crichton

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Abstract

© 2017 Society of Chemical Industry Copyright © 2017 Society of Chemical Industry. BACKGROUND: The potential of hyperspectral imaging (500-1010nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9mm thickness subjected to air drying at 50°C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). RESULTS: The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1]-R[λ2] and R[λ1]:R[λ2], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511nm]-R[994nm]) and spatial domain (i.e. relative area shrinkage of slice). CONCLUSIONS: Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices.

Publication metadata

Author(s): Moscetti R, Sturm B, Crichton SO, Amjad W, Massantini R

Publication type: Article

Publication status: Published

Journal: Journal of the Science of Food and Agriculture

Year: 2018

Volume: 98

Issue: 7

Pages: 2507-2517

Print publication date: 01/05/2018

Online publication date: 11/11/2017

Acceptance date: 04/10/2017

ISSN (print): 0022-5142

ISSN (electronic): 1097-0010

Publisher: John Wiley and Sons Ltd

URL: https://doi.org/10.1002/jsfa.8737

DOI: 10.1002/jsfa.8737

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