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.
Food Res Int. 2019 May;119:417-425. doi: 10.1016/j.foodres.2019.02.019. Epub 2019 Feb 10.
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), immobilized water (T) and free water (T), with the corresponding peak areas of A, A and A, respectively. During drying, the changes of A and A were not significant, while A and A decreased significantly (p < .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, S, S and S corresponded with the data measured by GC-MS. The correlation analysis between LF-NMR parameters and characteristic sensors showed that A and A were significantly correlated with the volatile components (p < .05). The results of the BP-ANN prediction showed that the model fitted well and had strong approximation ability (R > 0.95 and error < 3.65%) and stability, which indicated that the ANN model can accurately predict the flavor change during ginger drying based on LF-NMR parameters.
低场核磁共振（LF-NMR）和电子鼻与气相色谱质谱联用（GC-MS）结合，用于收集水分状态和挥发性物质的数据，以预测生姜在干燥过程中的风味变化。建立了一个反向传播人工神经网络（BP-ANN）模型，输入值为 LF-NMR 参数，输出值为电子鼻对不同风味物质的传感器值。结果表明，新鲜生姜含有三种水分成分：结合水（T）、固定水（T）和自由水（T），对应的峰面积分别为 A、A 和 A。在干燥过程中，A 和 A 的变化不明显，而 A 和 A 显著下降（p < .05）。电子鼻数据的线性判别分析（LDA）表明，不同干燥时间的样品可以很好地区分。层次聚类分析（HCA）证实，电子鼻特征传感器数据 S、S、S 和 S 与 GC-MS 测量的数据相对应。LF-NMR 参数与特征传感器的相关性分析表明，A 和 A 与挥发性成分显著相关（p < .05）。BP-ANN 预测结果表明，模型拟合良好，具有很强的逼近能力（R > 0.95，误差 < 3.65%）和稳定性，这表明 ANN 模型可以基于 LF-NMR 参数准确预测生姜干燥过程中的风味变化。
