Abstract
Objective: 270 milk powder samples from 6 different brands were detected and distinguished by low field nuclear magnetic resonance combined with chemometrics. Methods: Three chemometrics methods of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and backpropagation artificial neural network (BP-ANN) were used to process experimental data of samples statistically. Results: The PCA method based on three-dimensional projection could not achieve the purpose of rapid identification of milk powder brand; the correct recognition rates of training and prediction sets were 66.1% and 52.2% for the PLS-DA method, respectively, which was low in credibility and challenging to realize the rapid identification of milk powder brand; the correct recognition rates of training and prediction sets of were 99.4% and 100.0% for the BP-ANN method respectively. Conclusion: The combination of low field nuclear magnetic resonance and BP-ANN can identify the milk powder brand well.
Publication Date
8-28-2021
First Page
105
Last Page
109
DOI
10.13652/j.issn.1003-5788.2021.08.017
Recommended Citation
Li, YANG; A-lin, XIA; and Yu, ZHANG
(2021)
"Fast identification of milk powder brand based on low field nuclear magnetic resonance technology,"
Food and Machinery: Vol. 37:
Iss.
8, Article 17.
DOI: 10.13652/j.issn.1003-5788.2021.08.017
Available at:
https://www.ifoodmm.cn/journal/vol37/iss8/17
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