Abstract:
The jackfruit is the largest edible fruit but remains underutilized due to challenges such as sticky latex, labor-intensive peeling/coring, and lack of mechanization. This study developed and evaluated a jackfruit peeling, coring, and cutting machine to enhance processing efficiency. Performance was modeled using response surface methodology (RSM) and artificial neural network (ANN). Three jackfruit sizes (small, medium, and large) and three machine speeds (90, 120, and 150 RPM) were evaluated for peeling time (26.1–50.3 s), peeling efficiency (71.6%–85.3%), coring time (15.5–29.9 s), coring efficiency (74.7%–96.0%), and bulb wastage (6.2%–17.6%). RSM showed high model adequacy (R2 ≥ 0.97) and ANN confirmed prediction reliability (R2 = 0.81–0.99; mean square error = 4.4–44.9). Increasing fruit size significantly increased peeling and coring times but decreased efficiencies. Machine speeds caused minor variations. Optimized conditions of 120 RPM fruit holder speed and 150 RPM corer speed gave maximum desirability (0.869). The machine had a payback period of 2 years and benefit–cost ratio of 2.32 versus 2.66 for manual peeling/coring. The mechanized jackfruit processing will promote enhanced utilization of this nutritious fruit.
