Journal
LWT-FOOD SCIENCE AND TECHNOLOGY
Volume 116, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.lwt.2019.108548
Keywords
Moisture content; LF-NMR; Artificial intelligence; Microwave vacuum drying; Typical fruits and vegetables
Categories
Funding
- National Key R&D Program of China [2017YFD0400901]
- Jiangsu Province Agricultural Innovation Project [(17)2017]
- Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment Technology [FM-2019-03]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX19-1816]
- National First-class Discipline Program of Food Science and Technology [JUFSTR20180205]
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To set up a rapid real-time nondestructive detection of moisture content, this paper reported the results of a combination of LF-NMR and BP-ANN to monitor the relationship between drying parameters and state of water under different microwave vacuum drying conditions. Three kinds of materials, carrot (fruit), banana (vegetable) and pleurotus eryngii (edible fungus), were tested in the experiment of applicability. The resulted showed that the information of A(total) and T(23 )can be used to analyze the drying behavior and the information of A(20 ) A(21) and A(22) can be used as the fingerprint characteristics of material discrimination. Three classic models (PLS, SVM and BP-ANN) were compared to study the prediction ability of moisture content with the inputs of A(20), A(21), A(22), A(23) and A(total). The performance of BP-ANN model was the best. Although the BP-ANN model of mixed species was not as good as the BP-ANN model of single fruit or vegetable, it still had excellent predictive performances with R-2 0.9969 and RMSE 0.0184 to meet the needs of current industry and production.
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