DEVELOPMENT OF PREDICTIVE MODELS FOR QUALITY CONTROL OF GALA APPLES DURING DRYING

This thesis research project is aimed at setting up prediction models based on NIR spectroscopy, for quality control of organic apple wedges (Malus domestica B., var. Gala) during hot-air drying process (horizontal flow) up to 8 h. Hot-water and microwave blanching were both tested at 95°C for 5 min and 850 W for 45 sec, respectively, as pre-treatments to control the occurrence of enzymatic browning during drying. However, hot-water blanching had a negative impact on the appearance of the apple wedges, which were subjected to non-enzymatic discoloration (e.g. Maillard’s reaction). PLS regression showed good performances for the prediction of aw (RMSE = 0.03-0.04; R2 = 0.97-0.98), moisture (RMSE = 0.04-0.05; R2 = 0.97-0.98), SSC (RMSE = 4.54-4.99 °Brix; R2 = 0.96-0.97) and changes in chroma (RMSE = 2.31-2.75; R2 = 0.81-0.86) during drying. Also PLSDA classification showed very good metrics (total accuracy > 95%) in recognising 3-drying steps, both for control and microwave-treated samples. Features selection by iPLS and iPLSDA algorithms showed results better/equal than models based on full spectrum. For these results, the implementation of low-cost NIR sensors on drier device, seems feasible