Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.

Yunfan Wang; Xuemei Zhao; Rui Li; Xilin Wang; Xinguang Fan; Yanhui Kong; Xiulian Li; Xiuli Ma; Xiangquan Zeng; Huamin Li; Wenli Liu; Yanju Li; Ziyi Wang; Honghong Lv; Danliangmin Song; Hansheng Gong

doi:10.1016/j.foodres.2026.120097

PubMed · 42680362

Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.

Yunfan Wang·Xuemei Zhao·Rui Li·Xilin Wang·Xinguang Fan·Yanhui Kong·Xiulian Li·Xiuli Ma·Xiangquan Zeng·Huamin Li·Wenli Liu·Yanju Li·Ziyi Wang·Honghong Lv·Danliangmin Song·Hansheng Gong

View original source ↗Export metadata ↓

Abstract

This study focused on L.brevis LD1.0600 with excellent fermentation traits: it analyzed genome-wide key regulatory genes for micro-metabolites, combined with fermented cherry juice flavor metabolomics data, and used machine learning to explore correlations between gene regulation, metabolite production, and flavor formation. The SVM model screened and verified fermented cherry juice VOCs; through OAV and flavor wheel analysis, LD1.0600 emerged as the top-performing strain, with a sweet, fruity dominant aroma. Key aroma-active components (OAV > 100) included 2-methoxy-4-vinylphenol, benzaldehyde, 2-methyl-butanoic acid and hexanoic acid, and 2-methoxy-4-vinylphenol and hexanoic acid elevated by LD1.0600-regulated genes (Chrom1-001884, Chrom1-000925, fabF and Chrom1-000199). At the same time, through research, a "strain screening-SVM screening of DVCs-OAV screening of key aroma components-whole genome sequencing of flavor regulatory genes" system was established. This system can not only be applied to the screen fermentation strains, but also can be extended to the application of other fermentation products.

Explore related subjects

Fermentation Odorants Prunus avium Fruit and Vegetable Juices Volatile Organic Compounds Taste Whole Genome Sequencing Metabolomics Machine Learning Correlation analysis Flavoromics Levilactobacillus brevis Machine learning Whole genome sequencing Journal Article

Keep this discovery

BibTeX RIS

Yunfan Wang, Xuemei Zhao, Rui Li, Xilin Wang, Xinguang Fan, Yanhui Kong, Xiulian Li, Xiuli Ma, Xiangquan Zeng, Huamin Li, Wenli Liu, Yanju Li, Ziyi Wang, Honghong Lv, Danliangmin Song, Hansheng Gong. 2026-07-19. Exploring the mechanism of aroma production in fermented cherry juice by L. brevis LD1.0600 using flavomics and whole genome analysis.. https://doi.org/10.1016/j.foodres.2026.120097

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Journal Article resource Fermentation subject Fruit and Vegetable Juices subject Journal Article subject Machine Learning subject Metabolomics subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

PubMed2026

Integrated electronic nose, GC-MS, and metagenomic analyses reveal volatile flavor and microbial community differences in heap-fermented grains of Jiangxiangxing Baijiu across different fermentation degrees.

The fermentation degree of heap-fermented grains in Jiangxiangxing Baijiu production is a critical factor influencing base Baijiu quality. However, conventional assessment methods largely rely on empirical experience and therefore suffer from limited objectivity and accuracy. In this study, integrated volatile profiling and metagenomic approaches were employed to investigate volatile characteristics and microbial functional potential differentiation in fermented grains with different fermentation degrees (under-fermented, normally fermented, and over-fermented). Significant differences in physicochemical properties were observed among fermentation degrees, particularly in acidity and reducing sugar content. Electronic nose analysis revealed distinct sensor response patterns among different fermentation degrees, indicating differences in overall volatile odor fingerprint patterns. A total of 81 volatile compounds were identified by HS-SPME-GC-MS, with aldehydes, ketones, and pyrazines showing pronounced variations among fermentation degrees, and acetaldehyde exhibiting strong discriminatory potential. LEfSe analysis identified 18 microbial taxa as potential biomarkers associated with different fermentation degrees, including Pichia kudriavzevii, Lentibacillus daiqui, and Acetobacter pasteurianus. Correlation analysis revealed significant positive associations between acetaldehyde levels and Acetobacter abundance. Furthermore, KEGG, CAZy, and eggNOG analyses revealed differentiated functional potentials among fermentation degrees, providing insights into the potential metabolic basis associated with flavor differentiation. Overall, these findings highlight that fermentation degree differentiation is closely associated with coordinated changes in physicochemical conditions, microbial communities, and functional potentials, providing ecological insights into flavor differentiation and theoretical support for objective fermentation degree evaluation and quality control of Jiangxiangxing Baijiu production.

Fermentation↗

PubMed2026

Comparative profiling of microbial community structure, enzyme potential, metabolic features, and volatile composition in craft and Jiafan Huangjiu processes.

Craft Huangjiu and Jiafan Huangjiu represent two distinct industrial Huangjiu product outcomes with contrasting volatile profiles. This study compared craft Huangjiu (L70) and Jiafan Huangjiu (L79) to characterize their physicochemical, microbial, gene-level functional, metabolic, and volatile features. Because L70 involved mid-fermentation addition of finished Huangjiu, this comparison was not intended to isolate the sole effect of fermentation interruption versus continued fermentation. L79 showed more extensive carbon and nitrogen utilization, with lower residual substrates and higher ethanol and acetic acid contents than L70, whereas L70 retained a less complete fermentation state. At the volatile level, GC-MS and volatile metabolomics consistently showed an ester-enriched profile in L79 and a more alcohol-dominant profile in L70. FlavorDB-based putative annotation and threshold-based OAV analysis further indicated distinct database-assigned descriptor distributions and potential odor-active compounds, with more OAV > 1 ester-related compounds in L79. Metagenomic analysis showed that L70 was dominated by Lactobacillus acetotolerans, whereas L79 contained higher relative abundances of Saccharomyces cerevisiae, Aspergillus oryzae, Aspergillus flavus, and Fructilactobacillus fructivorans. Metagenomic functional annotation showed higher representation of hydrolysis-related CAZy genes and ester-related enzyme annotations in L79. KEGG-based pathway mapping further indicated greater gene-level potential for ethanol-, acetate-, and acetyl-CoA-related metabolism in L79. Accordingly, the L70 profile should be interpreted as the integrated final-product outcome of process intervention, exogenous input, and subsequent fermentation. The findings provide a comparative basis for future flavor regulation and process optimization in Huangjiu and other fermented alcoholic beverages.

Volatile Organic Compounds↗

PubMed2026

The effect of olfactory training and odor exposure on visual selective attention.

Olfactory training (OT) is a well-established, non-invasive intervention that improves olfactory function by repeated, structured exposure to odorants. Beyond sensory outcomes, it also influences multiple aspects of cognition. Yet, its influence on attention, a fundamental process underlying many cognitive domains, remains unclear. This study examined the impact of a 12-week OT program on visual selective attentional performance and the effect of on-task odor exposure. Ninety-four healthy participants were initially included, and randomized into OT or placebo groups. Participants completed the d2 Test of Attention simultaneously with and without odor exposure in a randomized sequence at baseline, post-training, and a follow-up four weeks after stopping the training. The d2 test measures commission errors, omission errors, and concentration performance. Ninety-three participants (OT=51, placebo=42) completed baseline measurement, 74 (OT=39, placebo=35) returned for post-training session, and 57 participants (OT=29, placebo=28) returned for the follow-up appointment. Results showed that OT did not significantly improve d2 test performance. In the primary model, on-task PEA exposure reduced commission errors compared to the odorless condition, suggesting a possible transient effect of olfactory input on the inhibitory control in the context of the d2 attentional test. This effect, however, was no longer significant once the follow-up data were included and should be regarded as preliminary. Overall, these findings raise the possibility of a distinction between relatively long-term OT and short-term odor exposure: while OT did not improve visual selective attention, brief on-task odor exposure may transiently enhance inhibitory control during attentional tasks, a possibility that remains to be confirmed.

Humans↗