Integrating Machine Learning and Expert Sensory Evaluation to Identify Key Drivers of Tomato Fruit Quality: A Multi-Model and Age-Stratified Analysis.

Yihang Zhu, Chenxu Liu, Zhuping Yao, Rongqing Wang, Baoliang Xie, Yuan Cheng, Xiaobin Zhang

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Characterizing Shared Sensory Priorities and Age-Group Differences for Feng-Flavor Baijiu: A Consumer Survey Using Hierarchical Weighting and Interpretable Machine Learning.Foods (Basel, Switzerland) · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yihang ZhuState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.

Chenxu LiuState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.ORCID 0009-0008-7811-9147

Zhuping YaoState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.

Rongqing WangState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.

Baoliang XieState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.

Yuan ChengState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.ORCID 0000-0002-4740-1843

Xiaobin ZhangState Key Laboratory for Quality and Safety of Agro-Products, Institute of Digital Agriculture, Vegetable Research Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.

Funding

China Agriculture Research System CARS-23-G44National Natural Science Foundation of China 32341044Ningbo Science and Technology Commissioner Project 2024S221Science and Technology Department of Zhejiang Province 2021C02065Zhejiang "Nine aspects of three Rural issues" Science and Technology Cooperation Plan 2024SNJF014

6 · The paper itself

Abstract

Individual biochemical indicators are insufficient for comprehensive tomato food flavor quality assessment, necessitating multi-parameter models of the core soluble taste matrix. We hypothesized that age stratification of trained sensory assessors would expose differential biochemical variable importance profiles in flavor quality prediction. Accordingly, this study aimed to: (1) construct and compare multiple regression models linking eight biochemical indicators to sensory scores, (2) identify key quality drivers via feature selection, and (3) examine whether age stratification alters the identified sensory drivers. Eight baseline taste indicators across 62 tomato cultivars were evaluated by 30 age-stratified trained sensory panelists (<40 and ≥40 years), using cross-validation to ensure model robustness against small-sample constraints. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Boruta were applied. Random forest achieved the best performance (R

Indexed as

age stratificationflavor-related metabolitesprecision breedingrandom foresttomato fruit quality

Identifiers

PMID42450476

DOI10.3390/foods15132358

PMCPMC13360765

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.