Elsevier, Thermal Science and Engineering Progress, Volume 18, 1 August 2020
Quality attributes such as moisture content, colour parameters and shrinkage of apples change undesirably during the drying process. Drying is a highly dynamic process, thus, an effective optimisation in terms of product quality and process performance requires continuous non-invasive measurement of the parameters in question. In this study, visual to near infra-red hyperspectral imaging was used in combination with the respective gold standard methods to estimate moisture ratio, CIELab chromaticity, browning index, shrinkage, and rehydration ratio of apple slices during the hot air-drying process. Two varieties (cv. Elstar and Golden delicious) of apples at three slice thicknesses (2, 3, and 4 mm) were dried at 60 °C and 70 °C. Prediction models for the space-averaged spectral reflectance curves were built using the partial least square regression method and including both varieties. The performance of moisture ratio prediction was excellent (adj R2 = 0.94, RMSEP = 0.076) and the Variable Importance in the Projection value cut off above 0.8 at 970 nm and L* at 685 nm. Similarly, partial least square regression modelling showed a good prediction for a*, b* value, BI, shrinkage and acceptable prediction for L* and RR. The model performance was robust to the system settings irrespective of slice thickness, drying temperature and apple variety. Additionally, method comparisons using Bland-Altman, Bablok, and Deming regression were performed. The results confirm that the compared destructive (laboratory gold standard) and non-destructive hyperspectral methods can be interchangeably used within the limit of agreement (±1.96 standard deviations) and precision for determination of the MR, CIELAB chromaticity and BI, shrinkage, and rehydration ratio. Therefore, these results confirm that hyperspectral imaging system can be used in online monitoring of the apples during the drying process, and thus, in the optimisation of product and process performance quality attributes.