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 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing 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.
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
10 authors.
Yan ZhaoSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.ORCID 0009-0006-1727-6697
Xinyi DuSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.ORCID 0009-0000-4604-1092
Shuang LiuSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.
Mengqi SunSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.
Limin ManSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.
Mingxia ZhuSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.
Guiqin LiuSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.ORCID 0000-0002-6230-9885
Muhammad Zahoor KhanSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.ORCID 0000-0001-5890-2996
Changfa WangSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.ORCID 0000-0002-5186-1703
Mengmeng LiSchool of Agriculture and Biology, School of Materials Science and Engineering, Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China.
Funding
National Key R&D Program of China 2022YFD1600103, 2023YFD1302004the Key R&D Program Project of Shandong Province 2021TZXD012the Shandong Province Modern Agricultural Technology System Donkey Industrial Innovation Team SDAIT-27the Shandong Provincial Natural Science Foundation ZR2022QC130the Support Program for Innovation and Technology in Universities of Shandong province 2023KJ354
6 · The paper itself
Abstract
The production of high-quality specialty meats has emerged as a prominent research focus within the livestock industry, under the broader concept of big food. However, the composition and variances of volatile compounds (VOCs) in donkey meat (DM) and horse meat (HM) remain unclear, which complicates their effective identification. In the present study, the VOCs of DM and HM were analyzed using gas chromatography-ion mobility spectrometry (GC-IMS) in combination with a multivariate analysis. Our results indicate that a total of 39 VOCs were identified in both DM and HM. These VOCs were categorized into five groups: aldehydes (39.53%), ketones (28.89%), alcohols (28.89%), acids (6.98%), and furans (2.33%). Compared with HM, the concentration of aldehydes, ketones, and alcohols in DM is significantly higher. (
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.
Characterization and Discrimination of Volatile Compounds of Donkey and Horse Meat Based on Gas Chromatography-Ion Mobility Spectrometry. · full record | OpenQuestion