Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR

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 (T21), immobilized water (T22) and free water (T23), with the corresponding peak areas of A21, A22 and A23, respectively. During drying, the changes of A21 and A22 were not significant, while A23 and ATotal decreased significantly (p < 0.05). 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 S4, S5, S8 and S13 corresponded with the data measured by GC-MS. The correlation analysis between LF-NMR parameters and characteristic sensors showed that A23 and ATotal were significantly correlated with the volatile components (p < 0.05). 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.

Improving storage quality of refrigerated steamed buns by mung bean starch composite coating enriched with nano‐emulsified essential oils

“…As shown in the radar diagram, the samples had high signals in sensors S1, S5, S6, S7, S9, and S13. These sensors represented aromatic compounds, alcohols, sulfur organic compounds, aliphatic hydrocarbons, and polar aromatic compounds, respectively (Chen, Zhang, Bhandari, & Guo, 2018; Sun, Zhang, Bhandari, & Yang, 2019; Wang, Zhang, Wang, & Martynenko, 2018). This was consistent with the GC–MS data of steamed stuffed bun in the research of Zhou and Yang (2016) that is, the main flavor substances included alcohols, hydrocarbons, aldehydes, aromatic compounds, sulfur compounds, heterocyclic compounds, and so on.…”

Effect of blanching and freezing on the physical properties, bioactive compounds, and microstructure of garlic (Allium sativum L.)

“…T 2 relaxation spectra of garlic during blanching and freezing treatments were obtained by multiexponential fitting of raw CPMG data, as depicted in Figure 4. In blanched and frozen garlic, three water populations were observed, defined as T 21 , T 22 , and T 23 and assigned as bound water, immobile water, and free water, respectively (Sun, Zhang, Bhandari, & Yang, 2019). The changes in T 21 , T 22 , and T 23 and the water distribution ratios (M 21 , M 22 , and M 23 ) of the samples are shown in Table 2.…”

Changes in the Volatile Components of Candied Kumquats in Different Processing Methodologies with Headspace–Gas Chromatography–Ion Mobility Spectrometry

“…The reason for this finding was probably that volatile alcohols were being primarily produced by ethanol dehydrogenase, and the increase in hot-air drying time led to the passivation or inactivation of relevant enzymes of synthetic aroma substances and caused the corresponding loss of volatile alcohol substances [53]. This result may also be attributed to the drying process promoting the esterification reaction of alcohols, reducing the alcohol content, and increasing the ester content [54].…”