Characterization of Volatile Component Changes in Peas under Different Treatments by GC-IMS and GC-MS

Author:

Zhang Kangyi1ORCID,Zhang Can1ORCID,Zhuang Haining2ORCID,Liu Yue3ORCID,Feng Tao4ORCID,Nie Bin5ORCID

Affiliation:

1. Institute of Agricultural Products Processing, Henan Academy of Agricultural Sciences, Zhengzhou 450008, Henan, China

2. School of Health & Society Care, Shanghai Urban Construction Vocational College, Shanghai 201415, China

3. Henan University of Technology, Zhengzhou 450008, Henan, China

4. School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China

5. Rural Industry Development Division, Department of Agriculture and Rural Affairs of Henan Province, Zhengzhou 450008, Henan, China

Abstract

Volatile profiles of peas under 9 kinds of different treatments including native, washing, blanching, precooling, freezing, steaming, boiling, frying, and freeze-drying were characterized by GC-IMS and GC-MS. The differences of volatile compounds in different peas were observed from the characteristic fingerprints by GC-IMS. The Venn diagram found that the common flavor substances codetected by GC-IMS and GC-MS were n-hexanal, nonanal, 1-octene-3-ol, benzaldehyde, 6-methyl-5-hepten-2-one, trans-2-octenal, and 2-ethyl-3,5-dimethylpyrazine, which were speculated to be the key flavor substances of peas. The cluster analysis of the heat map conducted towards the differences of volatile components in peas under different treatments; the results indicated that peas could be mainly divided into four groups, which was consistent with the above conclusion of GC-IMS. Eight sensory descriptors were used to evaluate the aroma notes: sweet flowers, fat fragrance, waxy aldehydes, mushroom hay, roasted potato with nuts, vegetable-like bean, spicy dry tar, and bitter almond from the sensory analysis, and the sensory analysis also showed good agreement with the results of GC-IMS and GC-MS. The results indicated that the volatile compounds of peas under different treatments could be visualized and identified quickly via GC-IMS, and the samples could be clearly classified based on the difference of volatile compounds. Practical Application. In the study, fingerprints coupled with cluster analysis were a visualized method for the identification of volatile compounds. Meanwhile, a new method, the Venn diagram with OAV, was used to identify the key-aroma of products. Finally, a rapid method is established to classify products by GC-IMS. 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 technology towards different cereals.

Funder

Henan Academy of Agricultural Sciences

Publisher

Hindawi Limited

Subject

Safety, Risk, Reliability and Quality,Food Science

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