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

• 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
• ISSN: 0910-6340; 1348-2246
• DOI: 10.2116/analsci.20P223

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.