Real-time detection system for food contaminants based on smart sensors

By using a multimodal sensor array, a dynamic baseline prediction model, and a signal separation network, the signal coupling problem caused by matrix changes during food processing was solved, enabling real-time detection and cross-scenario adaptation of food contaminants, and improving detection accuracy and efficiency.

CN122109460APending Publication Date: 2026-05-29开封市产品质量检验检测中心
View PDF 0 Cites 0 Cited by

Patent Information

Application Number
CN202610410074.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-31
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing food contaminant detection systems suffer from signal coupling due to matrix changes during food processing, making it difficult to effectively separate trace contaminant signals. Furthermore, they are difficult to adapt quickly across different scenarios, resulting in insufficient detection accuracy and high deployment costs.

Method used

By employing a multimodal sensor array combined with a time-series graph attention network and a variable decoupled autoencoder, a dynamic baseline prediction model and a signal separation network are constructed to achieve real-time decoupling of contaminant signals and matrix change signals during food processing. Furthermore, cross-scenario adaptive calibration is achieved through a meta-learning framework.

Benefits of technology

It enables real-time identification and accurate quantification of trace contaminant signals during food processing, reducing system deployment costs and timelines, and improving the timeliness of contamination incident detection and the accuracy of response.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122109460A_ABST
    Figure CN122109460A_ABST
Patent Text Reader

Abstract

The application discloses a kind of real-time detection method and system of food pollutant based on intelligent sensor, comprising: through the multimodal sensor array of deployment in food processing production line key station, sensor response signal is collected;Baseline response under the condition of no pollution is predicted by establishing dynamic baseline prediction model;Signal separation network based on variational decoupling autoencoder is constructed, residual signal is mapped to low-dimensional latent space by sharing encoder and is divided into baseline error subspace and pollutant signal subspace, respectively by two independent decoders reconstructing baseline prediction error signal and pollutant candidate signal;Multi-scale time-frequency transform is carried out to pollutant candidate signal to identify pollutant type and estimate concentration;Through distributed intelligent agent cooperation, pollution traceability and hierarchical early warning are carried out;Cross-scene adaptive calibration is realized using meta-learning framework.The application realizes the real-time decoupling of pollutant signal and matrix change signal in food processing process.
Need to check novelty before this filing date? Find Prior Art