Abstract
The aim of the work was to develop an optimized routine for apple drying. The interaction of the drying parameters air temperature (35–85°C), dew point temperature (5–30°C), and air velocity (2.0–4.8 m/s) with drying time, color changes, and shrinkage was determined. Non-invasive online measurement techniques in the form of artificial vision systems in visible and infrared spectrum were developed and applied to guarantee an uninterrupted process. Quantification methods for the determination of color and shape changes of apple slices were established based on the images taken.
Results show that digital images are a feasible alternative for the monitoring of the relative changes in L* (R2 = 0.92, p < 0.001), a* (R2 = 0.96, p < 0.001), and b* (R2 = 0.96, p < 0.001) during the drying of apples. It was observed that the color parameters as a function of moisture content follow a third-order development while shrinkage was linear (p < 0.001). The developed models for drying time tdr (R2 = 0.99, p < 0.001), Total Color Difference ΔE (R2 = 0.95, p < 0.001), and shrinkage S (R2 = 0.68, p < 0.05) illustrate high interdependencies of the factors involved for the quality criteria studied. Throughout the parameter space investigated, increasing air velocity was shown to have a positive effect on the quality criteria investigated.
ACKNOWLEDGMENTS
The authors wish to acknowledge the German Federal Ministry of Economics and Technology (BMWI) and the German Academic Exchange Service (DAAD) for their financial support within the programs PROINNO2 and a fellowship within the Postdoc-Program of the German Academic Exchange Service (DAAD). They also want to express their gratitude to Mrs. Anna Nuñez Vega and Mr. David Starkmann for their help.
Notes
a Arithmetic mean.
b Standard deviation.
a Standard deviation.
a A = ϑA; B = ϑDp; C = vA.
b Coefficient of determination.
c Pr>F.
d Arithmetic mean.
e Standard deviation.