Simultaneous, Rapid and Nondestructive Determination of Moisture, Fat Content and Storage Time in Leisure Dried Tofu Using LF-NMR

Leisure dried tofu is a kind of small packaged food which is popular with consumers in China. However, during the storage of leisure dried tofu, moisture and fat may be lost and deteriorate. For their own benefit, bad business operators might forge or mark the production date and shelf life. Therefo...

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Bibliographic Details
Published in:Analytical Sciences 2021/02/10, Vol.37(2), pp.301-307
Main Authors: XIA, Alin, ZHANG, Yu, ZHAO, Liangzhong, QIN, Pan
Format: Article
Language:English
Subjects:
Analytical Chemistry
Artificial neural networks
Back propagation networks
Calibration
Chemistry
China
fat content
Fats - analysis
Food Storage
Humidity
Leisure
Leisure dried tofu
LF-NMR
Magnetic Resonance Spectroscopy
Moisture
Neural networks
NMR
Nuclear magnetic resonance
Nutrients
Packaged food
Shelf life
Soy Foods - analysis
Soy products
storage dynamics
storage time
Time Factors
Tofu
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Summary:Leisure dried tofu is a kind of small packaged food which is popular with consumers in China. However, during the storage of leisure dried tofu, moisture and fat may be lost and deteriorate. For their own benefit, bad business operators might forge or mark the production date and shelf life. Therefore, it is necessary to explore a method to determine simultaneously the moisture, fat content, and storage time of leisure dried tofu. Samples were measured for obtaining transverse relaxation data by using low-field nuclear magnetic resonance (LF-NMR) spectrometer. The experimental data were analyzed and modeled by methods including partial least squares (PLS) or back-propagation artificial neural network (BP-ANN). The results show that the models can be used to predict the moisture, fat content, and storage time rapidly, nondestructively, accurately, and simultaneously. Furthermore, in order to explore the changes of nutrients in leisure dried tofu with the storage time, the storage dynamics of moisture and fat was considered by a using corresponding calibration model.
ISSN:0910-6340
1348-2246
DOI:10.2116/analsci.20P223