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Hyperspectral imaging for the determination of potato slice moisture content and chromaticity during the convective hot air drying process

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

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Abstract

© 2017 IAgrE Hyperspectral imaging (HSI) was utilised for the determination of moisture content of potato slices with three thicknesses (5 mm, 7 mm, 9 mm) at three drying temperatures (50 °C, 60 °C, 70 °C) during convective drying in a laboratory hot air dryer. The Page, thin-layer drying model was found better to explain the drying kinetics with a fitting accuracy of R2 (0.96–0.99) and lowest reduced Chi-square (0.00024–0.00090), Root mean square errors (RMSE) (0.014–0.026), and relative percentage error (1.5%–5.1%) under the used drying conditions. Spectral data were analysed using partial least squares regression (PLS) analysis, a multivariate calibration technique, alongside Monte Carlo Uninformative Variable Elimination (MCUVE-PLS) and competitive adaptive reweighted sampling (CARS-PLS). The feasibility of both moisture content and CIELAB prediction with a reduced wavelength set from the Visible near-infrared (VNIR) region (500–1000 nm) was investigated with these three models. The PLS model (R2 = 0.93–0.98, RMSE = 0.16–0.36 and the lowest number of optimal wavelengths = 6, for all drying conditions) was found suitable to implement for the moisture visualisation procedure. Potato chromaticity was also shown to be predictable during drying using a similar number of wavelengths, with PLS models for CIELAB a* performing well (R2 = 0.91–0.65, RMSE = 0.61–1.78). PLS Models for CIELAB b* more variably (R2 = 0.91–0.62, RMSE = 2.16–4.42) due to potato colour mainly varying along this axis. The current study showed that hyperspectral imaging was a useful tool for non-destructive measurement and visualisation of the moisture content and chromaticity during the drying process.

Publication metadata

Author(s): Amjad W, Crichton SOJ, Munir A, Hensel O, Sturm B

Publication type: Article

Publication status: Published

Journal: Biosystems Engineering

Year: 2018

Volume: 166

Pages: 170-183

Print publication date: 01/02/2018

Online publication date: 20/12/2017

Acceptance date: 05/12/2017

ISSN (print): 1537-5110

ISSN (electronic): 1537-5129

Publisher: Academic Press

URL: https://doi.org/10.1016/j.biosystemseng.2017.12.001

DOI: 10.1016/j.biosystemseng.2017.12.001

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