Comparative analysis of methods and model prediction performance evaluation for continuous online non-invasive quality assessment during drying of apples from two cultivars. (1st August 2020)

• Record Type: Journal Article
• Authors: Shrestha, Luna; Crichton, Stuart O.J.; Kulig, Boris; Kiesel, Bernadette; Hensel, Oliver; Sturm, Barbara
• Is Part Of: Thermal science and engineering progress. Volume 18(2020)
• Publication Date: 2020-08-01
• Subjects: adj R2 Adjusted coefficient of determination -- AOAC Association of Analytical Communities -- BI Browning index -- CI Confidence interval -- CIELab Chromacity -- F& V Fruit and vegetable -- HSI Hyperspectral imaging -- LoA Limits of agreement -- LV Latent variables -- MC Moisture content -- MR Moisture ratio -- NIPALS Nonlinear Iterative Partial Least Squares -- NIR Near infra-red -- PCA Principal component Analysis -- PLS Partial least square -- PRESS Predicted residual error sum of square -- RMSE Root mean square error -- RR Rehydration ratio -- S Shrinkage -- SD Standard deviation -- VIP Variable Importance in projection -- Vis/NIR Visible to near infra-red -- WHO World Health Organization; PLS -- Method comparison -- Variable Importance in projection (VIP) -- Vis/NIR hyperspectral imaging system -- Dried apple slices; Heat engineering -- Periodicals; Heat engineering; Thermodynamics; Periodicals; 621.402
• DOI: 10.1016/j.tsep.2019.100461
• Languages: English
• ISSNs: 2451-9049

Abstract

Abstract: 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 R 2 = 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.