A novel rapid non-contact detection method for frozen and thawed foods : Based on bio-impedance spectroscopy analysis and neural network (FCNN) recognition model.

Degang Xu, Qixiong Wen, Yuedong Xie, Wuliang Yin, Jiawei Tang

Abstract read

In one paragraph

Article in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Degang XuCentral South University, Changsha, 410006, China.

Qixiong WenCentral South University, Changsha, 410006, China.

Yuedong XieBeihang University, Beijing, 100191, China.

Wuliang YinThe University of Manchester, Manchester, M13 9PL, UK.

Jiawei TangCentral South University, Changsha, 410006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposed a novel rapid non-contact detection method for measuring frozen and thawed foods based on an innovative "data + neural network model" strategy. The purpose of the study is to develop a fast and effective detection method for frozen-thawed food, thereby overcoming critical industrial limitations such as slow sweeping speed and wide frequency range. The proposed model replaces the conventional full-spectroscopy scanning approach by requiring only the measurement signal ratio (

Indexed as

Bio-impedanceClassification of food industryFinite element methodMagnetic inductionNon-contact measurement

Identifiers

PMID41816652

DOI10.1016/j.crfs.2026.101358

PMCPMC12973513

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LicenceCC BY-NC-ND

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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.