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http://dx.doi.org/10.11002/kjfp.2015.22.1.1

Drying kinetics and optimization for thin-layer drying processes of raspberries (Rubus coreanus Miq.) using statistical models and response surface methodology  

Teng, Hui (Food and Bio-Industry Research Institute, Kyungpook National University)
Lee, Won Young (Food and Bio-Industry Research Institute, Kyungpook National University)
Publication Information
Food Science and Preservation / v.22, no.1, 2015 , pp. 1-11 More about this Journal
Abstract
Raspberries are a good resource of polyphenols and have a powerful antioxidant activity, but shelf life for raspberries is short which brings a lot of economic losses. In this study, we try to use cool-air ($20{\sim}40^{\circ}C$) or hot-air ($60{\sim}100^{\circ}C$) to produce semi-dried raspberries with extended shelf life, and to determine the best method for improving fruit quality by minimizing nutrient losses during drying processes. The effects of process variables (drying temperature and processing time) on the quality of final dried raspberries were investigated. Response surface methodology was employed to establish statistical models for simulating the drying processes, and the moisture residue content and the loss ratios of total phenolic content (TPC), vitamin C (VC), and ellagic acid (EA) that result from the drying processes of raspberries using either hot or cool-air were predicted. Superimposed contour plots have been successfully used in the determination of the optimum zone within the experimental region. Optimal conditions determined for achieving minimal losses of TPC, VC, and EA, and a final moisture residue of 45% using the hot-air drying process were a drying temperature of $65.75^{\circ}C$ and a processing time of 4.3 hr. While for the cool-air process, the optimal conditions predicted were $21.3^{\circ}C$ and 28.2 hr. Successful application of response surface methodology provided scientific reference for optimal conditions of semi-drying raspberries, minimizing nutrient losses and improving product quality.
Keywords
raspberry; response surface methodology; semi-dry; optimization;
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