Journal
FOOD AND BIOPROCESS TECHNOLOGY
Volume 12, Issue 4, Pages 551-562Publisher
SPRINGER
DOI: 10.1007/s11947-018-2231-1
Keywords
Artificial intelligence; Artificial neural network; LF-NMR; Carrot cube; Microwave vacuum drying
Categories
Funding
- National Key R&D Program of China [2017YFD0400901]
- Jiangsu Province (China) Agricultural Innovation Project [CX(17)2017]
- Jiangsu Province Key Laboratory Project of Advanced Food Manufacturing Equipment and Technology [FMZ201803]
- Jiangsu Province (China) Collaborative Innovation Center for Food Safety and Quality Control Industry Development Program, National First-class Discipline Program of Food Science and Technology [JUFSTR20180205]
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In this paper, intelligent technology of combined low field NMR (LF-NMR) and back propagation artificial neural network (BP-ANN) was used to monitor moisture content in carrot during microwave vacuum drying. The relationship between different drying powers (200, 300, and 400W) and NMR signals (A(21), A(22), A(23), and A(total)) was investigated. Results show that as the drying time elapsed, the NMR signals of A(total) and A(23) decrease all drying conditions, A(21) and A(22) tend to increase at high moisture content and then decrease, which is consistent with the state of water while changes during drying. NMR signals can be used as indicators for online monitoring of moisture and control of the drying process. With NMR signals as input variables, a BP-ANN model was built optimized by transfer function, training function, and the number of neurons to model the moisture content (output). Compared with a linear regression model and multiple linear regression model, the BP-ANN model with the topology of 4-25-1, transfer function of tansig and purelin, and training function of trainlm outperformed the fitting performance and accuracy. This shows that the combined approach of utilizing LF-NMR and BP-ANN has great potential in intelligent online monitoring and control applications for carrot drying.
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