ArticleNPJ science of food2026
Molecular sensory science combined with machine learning for exploring key odor-active compounds and aging-feature compounds of Shanxi aged vinegar.
Article in NPJ science of food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Aroma compounds in fermented foods are from raw material or produced by microorganisms, contributing to their diverse aroma profiles. The key odor-active compounds (OACs) present in Shanxi aged vinegar (SAV), one of China's famous vinegars, were systematically analyzed by molecular sensory science. A total of 152 OACs were identified in this study. Moreover, 87 OACs were quantitatively determined, and 37 OACs exhibited their odor activity values (OAVs) larger than 1. The aroma recombination and omission experiments confirmed that ketones, pyrazines, lactones, and acids are the most significant contributors to the aroma profiles of SAV. The correlation analysis confirmed that the absence of specific aroma compounds not only directly impacts the sensory characteristics associated with the missing compounds but also influences other sensory attributes. Therefore, 47 OACs, classified as key OACs through the verification of recombination and omission test, based on AEDA (FD ≥ 400) or OAV (≥1). Lastly, 6 aging-related markers (methional, 1-dodecanol, acetoin, benzyl acetate, propanoic acid, and trimethyl-pyrazine) were determined by machine learning based on key OACs. These results provide a standard for screening the key OACs that determine aroma characteristics of SAV, as well as for product quality control.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.