Neural network prediction of physical property changes of dried carrot as a function of fractal dimension and moisture content

The relationship between microstructural and physical properties of dried foods is difficult to quantify. This study uses artificial neural network analysis (ANN) to predict shrinkage and rehydration of dried carrots, based on inputs of moisture content and normalized fractal dimension analysis (ΔD/D0) of the cell wall structure. Several drying techniques were used including conventional hot air (HAD), low pressure superheated steam (LPSSD), and freeze drying (FD). Dried carrot sections were examined by light microscopy and the fractal dimension (D) determined using a box counting technique. Optimized ANN models were developed for HAD, LPSSD, HAD + LPSSD, and HAD + LPSSD + FD, based on 1-10 hidden layers and neurons per hidden layer. ANN models were then tested against an independent dataset. Measured values of shrinkage and rehydration were predicted with an R2 > 0.95 in all cases.