Evidence map›Paper›PMID 41300044›Full record

ArticleFoods (Basel, Switzerland)2025

KCQI: Novel Index for Assessment of Comprehensive Quality of Kiwifruit During Shelf Life Using Hyperspectral Imaging and One-Dimensional Convolutional Neural Networks.

Yongxian Wang, Kaisen Zhang, Yi Liu, Junsheng Liu, Ruofei Liu, Bo Ma, Linlin Sun, Linlong Jing, Xinpeng Cao, Hongjian Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Foods (Basel, Switzerland), 2025. 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.

  1. Tissue-Resolved Metabolomic Profiling of TenFoods (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

11 authors.

Yongxian WangCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Kaisen ZhangCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Yi LiuCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Junsheng LiuCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Ruofei LiuCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Bo MaCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Linlin SunCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Linlong JingCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Xinpeng CaoCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Hongjian ZhangCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.
Jinxing WangCollege of Mechanical and Electrical Engineering, Shandong Agricultural University, Taian 271018, China.

Funding

China Postdoctoral Science Foundation 2025M772484Postdoctoral Innovation Program of Shandong Province SDCX-ZG-202503062Shandong Province Key R&D Plan 2022CXGC020701Young Talent of Lifting engineering for Science and Technology in Shandong SDAST2024QTA050
6 · The paper itself

Abstract

Non-destructive assessment of kiwifruit quality is critical for postharvest preservation and grading. This paper proposes a novel quantitative evaluation method for the kiwifruit comprehensive quality index (KCQI) during shelf life, based on hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN). Hyperspectral images of two kiwifruit cultivars were acquired at four shelf-life stages using an HSI system, and six quality parameters were measured as reference standards. Based on correlation and factor analyses, five key parameters-soluble solids content, firmness, L*, b*, and chroma-were selected to construct the KCQI. Three spectral band selection methods and three modeling algorithms were compared, with the competitive adaptive reweighted sampling (CARS)-1D-CNN model yielding the highest prediction accuracy (RP2 = 0.82, RMSEP = 0.26, RPD

Indexed as

1D-CNNActinidia spp.comprehensive qualitypostharvest lifeprediction model

Identifiers

PMID41300044
PMCPMC12651449

What Socratic 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 Socratic graph.