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Comparative analysis of methods and model prediction performance evaluation for continuous online non-invasive quality assessment during drying of apples from two cultivars

Shrestha, L.; Crichton, S.O.J.; Kulig, B.; Kiesel, B.; Hensel, O. and Sturm, B. (2020) Comparative analysis of methods and model prediction performance evaluation for continuous online non-invasive quality assessment during drying of apples from two cultivars. Thermal Science and Engineering Progress, 18, pp. 1-14.

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Summary

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.


EPrint Type:Journal paper
Keywords:PLSMethod comparison, Variable Importance in projection (VIP), Vis/NIR hyperspectral imaging system, Dried apple slices, BÖLN, BOELN, BOEL, BÖL, FKZ 17OE005, SUSORGANICPLUS, Core Organic Cofund, SusOrgPlus
Agrovoc keywords:
Language
Value
URI
English
apples
http://aims.fao.org/aos/agrovoc/c_541
English
infrared spectrophotometry
http://aims.fao.org/aos/agrovoc/c_28568
Subjects: Food systems > Food security, food quality and human health
Food systems > Processing, packaging and transportation
Research affiliation: European Union > CORE Organic Cofund > SusOrgPlus
Germany > Federal Organic Farming Scheme - BOELN > Food > Processing
Germany > University of Kassel > Department of Agricultural Engineering and Agricultural Engineering in the Tropics and Subtropics
H2020 or FP7 Grant Agreement Number:727495
DOI:10.1016/j.tsep.2019.100461
Related Links:https://www.susorgplus.eu, https://orgprints.org/cgi/search/advanced?addtitle%2Ftitle=&keywords=17OE005&projects=BOEL&_order=bypublication&_action_search=Suchen
Deposited By: von Gersdorff, Gardis J.E.
ID Code:38728
Deposited On:08 Jan 2021 12:01
Last Modified:08 Jan 2021 12:01
Document Language:English
Status:Published
Refereed:Peer-reviewed and accepted

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