HomeIngredientArtificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries

Artificial Neural Network Assisted Multiobjective Optimization of Postharvest Blanching and Drying of Blueberries

November 14, 2022

Foods. 2022 Oct 25;11(21):3347. doi: 10.3390/foods11213347.

ABSTRACT

This study aimed to optimize the postharvest blanching and drying process of blueberries using high-humidity air impingement (HHAIB) and hot-air-assisted infrared (HAIR) heating. A novel pilot-scale hot-air-assisted carbon-fiber infrared (IR) blanching/drying system was developed. Fresh blueberries with an average diameter of 10~15 mm were first blanched with high-humidity air at 110 °C and 12 m/s velocity for different durations (30, 60, 90, and 120 s); subsequently, the preblanched blueberries were dried at different IR heating temperatures (50, 60, 70, 80, and 90 °C) and air velocities (0.01, 0.5, 1.5, and 2.5 m/s), following a factorial design. The drying time (DT), specific energy consumption (SEC), ascorbic acid content (VC), and rehydration capacity (RC) were determined as response variables. A three-layer feed-forward artificial neural network (ANN) model with a backpropagation algorithm was constructed to simulate the influence of blanching time, IR heating temperature, and air velocity on the four response variables by training on the experimental data. Objective functions for DT, SEC, VC, and RC that were developed by the ANN model were used for the simultaneous minimization of DT and SEC and maximization of VC and RC using a nondominated sorting genetic algorithm (NSGA II) to find the Pareto-optimal solutions. The optimal conditions were found to be 93 s of blanching, 89 °C IR heating, and a 1.2 m/s air velocity, which resulted in a drying time of 366.7 min, an SEC of 1.43 MJ/kg, a VC of 4.19 mg/100g, and an RC of 3.35. The predicted values from the ANN model agreed well with the experimental data under optimized conditions, with a low relative deviation value of 1.43-3.08%. The findings from this study provide guidance to improve the processing efficiency, product quality, and sustainability of blueberry postharvest processes. The ANN-assisted optimization approach developed in this study also sets a foundation for the smart control of processing systems of blueberries and similar commodities.

PMID:36359960 | DOI:10.3390/foods11213347

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About The Author

Patrick Blanchard MD

Meet Dr. Blanchard Dr. Blanchard’s medical practice is an embodiment of Integrative health which brings conventional and complementary approaches together in a coordinated way. Conveniently located in sunny Florida and over the internet with ValiseMD’s secure HIPPA compliant telXmed servers. Since 1994, patients with a wide range of challenging medical problems have achieved optimum health using the best of natural medicine, judiciously combined with high-tech innovations. Breakthroughs are often achieved even after patients have consulted mainstream specialists and holistic practitioners. Dr. Blanchard is founder and CEO of ValiseMD, Inc. He is board certified in Family Medicine since 1994 and awarded Fellow of the American Academy of Family Physicians in 2001. He received the ‘Teacher of the Year’ award from the University of Kansas School of Medicine at completion of his residency in Family Medicine. He completed a fellowship at Wake Forest University in the field of vascular neurosonology. He holds a medical patent in the field of Gastroenterology. He holds an unrestricted license to practice medicine and surgery in Florida. He started his medical career as a Emergency Medical Technician, then as a Paramedic and later a Medical Doctor.