Abstract
Agglomerated foods have gained attention in recent years due to their convenience in use. Turmeric powder has been subjected to agglomeration process at different moisture contents (10–28%) and steaming times (0–60min). Experimental cumulative particle size distribution data of agglomerated samples can be predicted well (0.951⩽r⩽0.999, p⩽0.01) with Rosin–Rammler–Bennett model. The functional properties related to hydration characteristics like wetting time (10–35s) and sinking time (15–115s) of agglomerated samples decrease with an increase in moisture content and/or steaming time. Microstructural observation shows that the non-agglomerated sample possesses spheroids and ellipsoids of different sizes. The size of agglomerates ranges between 50 and 160μm; their shape varies from spheroid to elongated ellipsoids. Image analysis infers that the size related parameters increase with an increase in moisture content/steaming time. A four-layered artificial neural network having a structure of 2–10-8–4 has been developed to predict the agglomeration process of turmeric powder.
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