Evidence map›Paper›PMID 40520695›Full record

ReviewFood chemistry: X2025

Transforming Agri-food waste: Innovative pathways toward a zero-waste circular economy.

Arun Kumar Pandey, Sheetal Thakur, Rahul Mehra, Raj Sukhwinder Singh Kaler, Maman Paul, Arun Kumar

Abstract readReview
In one paragraph

Review in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

6 authors.

Arun Kumar PandeyAmity Institute of Biotechnology, Amity University, Rajasthan, Jaipur 303002, India.
Sheetal ThakurUniversity Centre for Research & Development, Department of Biotechnology, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India.
Rahul MehraUniversity Centre for Research & Development, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India.
Raj Sukhwinder Singh KalerDepartment of Food Science and Technology, Guru Nanak Dev University, Amritsar 143005, Punjab, India.
Maman PaulDepartment of Physiotherapy, Guru Nanak Dev University, Amritsar 143005, Punjab, India.
Arun KumarDepartment of Food Science and Technology, Graphic Era (Deemed to be University), Dehradun 248002, Uttarakhand, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agri-food waste is one of the most abundant biomass resources globally, generated through ongoing agricultural activities and food industry operations. Around 30 % of food produced for human consumption is lost during production and processing, contributing to an estimated 150 billion metric tons of waste annually-an amount projected to grow by 7.5 % each year. Conventional disposal methods such as landfilling, and incineration result in significant environmental harm and economic loss. However, agri-food waste contains valuable organic compounds that can be repurposed into value-added products, supporting the transition to a zero-waste circular economy. This review compiles recent advancements in the generation, categorization, and valorization of agri-food waste, highlighting its potential in biotechnology for producing antibiotics, fermented foods, nutraceuticals, and industrial materials. By aligning with UN Sustainable Development Goals (SDGs) 11, 12, and 13, the review underscores the need for increased research, technological innovation, and public awareness to foster sustainable waste management practices.

Indexed as

Agri-wasteCircular economyFood wasteValorizationWaste-categorizationWaste management

Identifiers

PMID40520695
PMCPMC12167462

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.