Sensory score prediction and key aroma compounds characterization in fermented chopped pepper
Year: 2025
Type: article
Abstract: Fermented chopped pepper (FCP) exhibits complex and variable aroma profiles, making it challenging to accurately predict its sensory scores and identify key aroma compounds. In this study, electronic nose ( E -nose) combined with machine learning methods were applied for the prediction of FCPs sensory scores. The random forest (RF) demonstrated the highest predictive accuracy among support vector machine (SVM), multiple linear regression (MLR), and back propagation neural network (BPNN). E -nose combined with the trained RF was used to predict the sensory scores of FCPs from eight regions. Totally, 97 volatile compounds and 19 odor-active compounds were detected by GC × GC-O-Q-TOF-MS in the top-performing sample (FCP-1). Among these, 34 compounds exhibited odor activity values (OAV) greater than 1. Aroma recombination and omission experiments confirmed that linalool, phenethyl alcohol, methional, 3-isobutyl-2-methoxypyrazine, ethyl trans-4-decenoate, β-ionone, spiroxide, ethyl 2-methylbutyrate, α-terpineol, 4-ethylphenol, β-damascenone, and nerolidol were the key aroma compounds in FCP-1. • Four machine learning models predicted sensory scores of fermented chopped peppers. • GC × GC-Q-TOF-MS identified 97 volatiles in FCP-1, with 34 having OAV > 1. • Esters, aldehydes, pyrazine, etc., were the main volatile compounds in FCP-1. • 12 key aroma compounds were identified through recombination and omission tests.
Source: Food Chemistry X
Institution: Hunan Agricultural University
Language: English
Subfield: Food Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,330
Award: G7045315778
OpenAlex ID: W4412044211