Optimization of the organic fruit and vegetable drying process by using non-destructive techniques
Author(s)
Raponi, Flavio
Date Issued
May 13, 2020
Type
Doctoral Thesis
Abstract
The aim of this thesis was to set the basis for the development of a smart drier prototype able to optimize the hot-air drying process of organic apples (var. Gala) and carrots (var. Romance) using non-destructive techniques such as near infrared (NIR) spectroscopy and computer vision (CV). For the intended purpose, a quality-by-design approach was applied. The datasets acquired during the process were subjected to chemometrics in order to develop regression and classification models able to predict physicochemical changes and recognise dehydration phases. Excellent model performances (R2=0.91-0.98) were achieved in predicting physicochemical changes (e.g. water activity and soluble solid content) using NIR. The prediction of color changes gave good results (R2 =0.80-0.87). Classification models of drying phases provided from good (>0.85) to excellent (>0.95) results in terms of sensitivity and specificity ratios. CV was feasible for the real-time prediction of changes in morphological (e.g. area shrinkage and eccentricity) and physicochemical (moisture content, drying rate and color) properties on products. In particular, linear regression models gave excellent results (R2=0.993-0.999) in predicting changes in moisture content from area shrinkage of product during drying. Finally, results demonstrate the feasibility of NIR spectroscopy and CV to be used as non-destructive technologies embedded to a smart hot- air drier and also set the basis for a scale up of the process.
Additional information
Dottorato di ricerca in Scienze, tecnologie e biotecnologie per la sostenibilità
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