Title: | Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR |
Author(s): | Sun Y; Zhang M; Bhandari B; Yang P; |
Address: | "State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China. State Key Laboratory of Food Science and Technology, Jiangnan University, 214122 Wuxi, Jiangsu, China; Jiangsu Province Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, China. Electronic address: min@jiangnan.edu.cn. School of Agriculture and Food Sciences, University of Queensland, Brisbane, QLD, Australia. Suzhou Niumang Analytical Instrument Corporation, 215000 Suzhou, Jiangsu, China" |
DOI: | 10.1016/j.foodres.2019.02.019 |
ISSN/ISBN: | 1873-7145 (Electronic) 0963-9969 (Linking) |
Abstract: | "Low-field nuclear magnetic resonance (LF-NMR) and electronic nose combined with Gas chromatography mass spectrometry (GC-MS) were used to collect the data of moisture state and volatile substances to predict the flavor change of ginger during drying. An back propagation artificial neural network (BP-ANN) model was established with the input values of LF-NMR parameters and the output values of sensors for different flavor substances obtained from electronic nose. The results showed that fresh ginger contained three water components: bound water (T(21)), immobilized water (T(22)) and free water (T(23)), with the corresponding peak areas of A(21), A(22) and A(23), respectively. During drying, the changes of A(21) and A(22) were not significant, while A(23) and A(Total) decreased significantly (p?ª+ª+.05). Linear discriminant analysis (LDA) of electronic nose data showed that samples with different drying time can be well distinguished. Hierarchical clustering analysis (HCA) confirmed that the electronic nose characteristic sensor data S(4), S(5), S(8) and S(13) corresponded with the data measured by GC-MS. The correlation analysis between LF-NMR parameters and characteristic sensors showed that A(23) and A(Total) were significantly correlated with the volatile components (p?ª+ª+.05). The results of the BP-ANN prediction showed that the model fitted well and had strong approximation ability (R?ª+>?ª+0.95 and error?ª+ª+3.65%) and stability, which indicated that the ANN model can accurately predict the flavor change during ginger drying based on LF-NMR parameters" |
Keywords: | *Desiccation Discriminant Analysis Electronic Nose Flavoring Agents/*isolation & purification Gas Chromatography-Mass Spectrometry/methods Ginger/*chemistry Magnetic Resonance Spectroscopy/*methods *Microwaves *Vacuum Volatile Organic Compounds/isolation; |
Notes: | "MedlineSun, Yanan Zhang, Min Bhandari, Bhesh Yang, Peiqiang eng Research Support, Non-U.S. Gov't Canada 2019/03/20 Food Res Int. 2019 May; 119:417-425. doi: 10.1016/j.foodres.2019.02.019. Epub 2019 Feb 10" |