期刊
JOURNAL OF FOOD BIOCHEMISTRY
卷 46, 期 6, 页码 -出版社
WILEY
DOI: 10.1111/jfbc.13875
关键词
characteristic volatiles fingerprinting; gas chromatography ion mobility spectroscopy; gas chromatography-mass spectrometry; green wheat; volatile compounds
资金
- Basic Scientific Research Project of Henan Academy of Agricultural Sciences [2021ZC68]
- Outstanding Youth Fund Project of Henan Academy of Agricultural Sciences [2020JQ04]
- Major Science and Technology Research Program of Henan Province [212102110339]
Different treatments of green wheat were evaluated for volatile components using GC-IMS and GC-MS, identifying key aroma substances and characteristic flavor notes. This study provides a rapid and visualized method for classifying and identifying wheat samples based on their volatile compounds.
Volatile components in green wheat under different treatments including raw, washing, blanching, precooling, freezing, steaming, boiling, frying, and freeze-drying were evaluated by gas chromatography-ion mobility spectroscopy (GC-IMS) and gas chromatography-mass spectrometry (GC-MS). Five key aroma substances including n-hexanal, benzaldehyde, nonanal, 2-pentylfuran, and (E)-oct-2-enal were found by Venn diagram and odor activity values (OAV). Furthermore, according to volatile fingerprints characteristics and the aroma profile of sensory evaluation, it was found that green wheat under different treatments mainly presented seven characteristic flavor notes including sweet flowers, fat fragrance, mushroom hay, waxy aldehyde, citrus fruity, vegetable-like bean, and bitter almond from the sensory evaluation, and they could be divided into four categories, which was consistent with the results of PCA and GC-IMS. Hence, the volatile compounds of green wheat samples could be visualized and identified quickly via GC-IMS and the samples could be clearly classified based on the difference of volatile compounds. Practical applications In the study, fingerprints coupled with cluster analysis were a visualized method for the identification of volatile compounds. Meanwhile, a new method, Venn diagram with OAV, was used to identify the key aroma of products. Finally, a rapid method to classify products by GC-IMS was performed. In future practical applications, GC-IMS can be used to classify products from different origins and different manufacturers. Similarly, it can identify fake and inferior products and whether the products have deteriorated. In addition, this research will provide a new strategy to find the relationship between flavor compounds and various processed technologies toward different cereals.
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