Machine learning analysis of pre-culture effects on rate-limiting steps in volatile compound dynamics of Mead.

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Tác giả: Wushuang Bai, Jing Dong, Martin Gand, Zisheng Guo, Meng Jiao, Bin Li, Binglin Li, Qian Li, Xian Li, Ziwei Liu, Yibing Qiao, Yiran Wang, Tiantian Zhang, Kexin Zhu

Ngôn ngữ: eng

Ký hiệu phân loại: 809.008 History and description with respect to kinds of persons

Thông tin xuất bản: Netherlands : Food chemistry: X , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 724842

A novel two-step fermentation process was developed to enhance mead flavor quality. Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) with three columns was used to analyze the volatile profiles of meads, along with sensory evaluation and machine learning. Compared to traditional mead (TM), our novel mead (NM) reduced off-flavor compounds by 37.6 %, with isoamyl alcohol decreasing 1.26-fold and ethyl laurate 2.09-fold. Meanwhile, aromatic compounds increased by 39.41 %, with isoamyl acetate rising 3.31-fold, ethyl caproate 2.79-fold, and phenylethyl alcohol 1.69-fold. Sensory evaluation revealed a significant reduction in bitterness (41.1 %) and irritation (42.5 %), while fruity, sweet, and pleasantly sour flavors increased by 27.4 %, 36.9 %, and 45.5 % for NM. Key aroma compounds (benzaldehyde, 2,3-butanediol, cedrol) were identified via recombination and omission experiments. Dynamic monitoring and machine learning identified key rate-limiting steps, including the oxidation of benzeneacetaldehyde (phenylethyl alcohol synthesis), isovaleraldehyde (isoamyl alcohol synthesis), and the conversion of octanoic acid to decanoic acid.
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