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Identification of discriminating chemical compounds in banana species and their odor characterization using GC-MS, statistical, and clustering analysis.
Jha, Sunil Kumar; Zhang, Jian; Hayashi, Kenshi; Liu, Chuanjun.
Afiliación
  • Jha SK; Unitedworld School of Computational Intelligence, Karnavati University, Gandhinagar, 382422 India.
  • Zhang J; School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Changqing Garden, Hankou, Wuhan, 430023 China.
  • Hayashi K; Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395 Japan.
  • Liu C; Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395 Japan.
J Food Sci Technol ; 59(1): 402-408, 2022 Jan.
Article en En | MEDLINE | ID: mdl-35068584
ABSTRACT
The study aims identification of discriminating chemical constituents in the banana odor grown in Philippines and Ecuador using GC-MS characterization. Ester is recognized as a major chemical class in selected banana odor. Odors discriminating compounds like, 2-hexenal, ethyl acetate, and hexanoic acid, ethyl ester, etc. have been identified. Besides, other odors generating chemical compounds (alcohols, esters, aldehydes, and ketones) have been recognized. Furthermore, principal component analysis (PCA) and hierarchical cluster analysis were implemented to differentiate banana odors. PCA achieved 100% discrimination of selected bananas odors using the peak area information about recognizing chemical compounds. Odor identity and discrimination of selected bananas have been achieved successfully. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13197-021-05298-9.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Food Sci Technol Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Food Sci Technol Año: 2022 Tipo del documento: Article