Frontiers in chemistry · 2026
Analysis of volatile compounds in Aglaia odorata flower extracts with different possessing methods by HS-SPME-GC-MS and E-nose.
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Yang Pengfei, Kong Lingqi, Wang Qiongbo, Liu Qiang, Duan Xiujin, Hu Chen, Feng Zhengbo, Yang Qi, Jv Huabo
Аннотация
INTRODUCTION: To investigate the impact of different processing methods on the volatile components in Aglaia odorata flower extract (AOFEs).
METHODS: Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and electronic nose (E-Nose) analysis were employed to characterize volatiles of extracts obtained by ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and heated reflux extraction (HRE). Multidimensional assessment using aroma radar charts, orthogonal partial least squares-discriminant analysis (OPLS-DA), K-means clustering, and relative odor activity value (ROAV) revealed significant processing-dependent variations.
RESULTS AND DISCUSSION: The results indicated that the fiber coated with DVB/CAR/PDMS had optimal extraction efficiency. A total of 46 compounds were identified, including eight alcohols, four aldehydes, one acid, 25 terpenes, seven ketones, and one heterocyclic compound. UAE and MAE had 36 and 38 compounds respectively, sharing similar compositional profiles but differing in concentrations, while HRE produced only 25 compounds Sensory evaluation and E-Nose results revealed differences in the aroma profiles of the extracts, with UAE and MAE extracts exhibiting intensified floral and sweet notes, whereas HRE displayed prominent green and spicy characteristics. K-means clustering categorized volatile evolution trends into four distinct subclasses. OPLS-DA identified 13 differential volatiles with variable importance in projection greater than 1, with ROAV analysis further selecting eight key markers, including (1R,7S)-germacra-4(15),5,10(14)-trien-1β-ol, α-humulene, copaene, β-cadinene, (E)-β-caryophylene, Γ-cadinene, humulene oxide II, and caryophyllene oxide. These compounds collectively contribute to the sweet and floral attributes of AOFEs. This study elucidated extraction-method-dependent volatile profiles and aroma characteristics, providing theoretical guidance for process optimization and quality enhancement in AOFEs production.
METHODS: Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and electronic nose (E-Nose) analysis were employed to characterize volatiles of extracts obtained by ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and heated reflux extraction (HRE). Multidimensional assessment using aroma radar charts, orthogonal partial least squares-discriminant analysis (OPLS-DA), K-means clustering, and relative odor activity value (ROAV) revealed significant processing-dependent variations.
RESULTS AND DISCUSSION: The results indicated that the fiber coated with DVB/CAR/PDMS had optimal extraction efficiency. A total of 46 compounds were identified, including eight alcohols, four aldehydes, one acid, 25 terpenes, seven ketones, and one heterocyclic compound. UAE and MAE had 36 and 38 compounds respectively, sharing similar compositional profiles but differing in concentrations, while HRE produced only 25 compounds Sensory evaluation and E-Nose results revealed differences in the aroma profiles of the extracts, with UAE and MAE extracts exhibiting intensified floral and sweet notes, whereas HRE displayed prominent green and spicy characteristics. K-means clustering categorized volatile evolution trends into four distinct subclasses. OPLS-DA identified 13 differential volatiles with variable importance in projection greater than 1, with ROAV analysis further selecting eight key markers, including (1R,7S)-germacra-4(15),5,10(14)-trien-1β-ol, α-humulene, copaene, β-cadinene, (E)-β-caryophylene, Γ-cadinene, humulene oxide II, and caryophyllene oxide. These compounds collectively contribute to the sweet and floral attributes of AOFEs. This study elucidated extraction-method-dependent volatile profiles and aroma characteristics, providing theoretical guidance for process optimization and quality enhancement in AOFEs production.