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Related Experiment Video

Updated: Sep 16, 2025

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
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Machine Learning-Based Shelf Life Estimator for Dates Using a Multichannel Gas Sensor: Enhancing Food Security.

Asrar U Haque1, Mohammad Akeef Al Haque2, Abdulrahman Alabduladheem3

  • 1Department of Computer Science, College of Computer Science and Information Technology CCSIT, King Faisal University, Al AHSA 31982, Saudi Arabia.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an IoT system using gas sensors and machine learning to predict date fruit shelf life. The novel approach offers a low-cost, objective method for real-time spoilage detection, enhancing food security.

Keywords:
DHT11 sensorIoTcold storage room (CSR)datesedge impulsefood securitygas concentrationsmachine learning (ML)multichannel gas sensorrelative humidity (RH)shelf life

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Area of Science:

  • Agricultural Science
  • Food Science
  • Sensor Technology

Background:

  • Dates are a nutrient-rich food requiring optimal preservation for food security.
  • Current methods for assessing date shelf life are subjective, error-prone, and lack scalability.
  • Existing cold storage systems lack real-time spoilage detection and shelf-life prediction capabilities.

Purpose of the Study:

  • To develop a novel Internet of Things (IoT)-based system for estimating the shelf life of date fruits.
  • To provide an objective, scalable, and low-cost method for real-time shelf-life prediction.
  • To reduce postharvest losses in the date supply chain through improved shelf-life assessment.

Main Methods:

  • Integration of multichannel gas sensors to detect spoilage-related gases (methane, nitrogen dioxide, carbon monoxide).
  • Utilization of a lightweight machine learning model deployed on an edge device (Arduino Nano 33 BLE Sense board).
  • Real-time capture of gas emissions and environmental data for freshness classification.

Main Results:

  • Achieved a classification accuracy of 91.9% for freshness detection.
  • Obtained an Area Under the Curve (AUC) of 0.98, indicating high model performance.
  • Successfully deployed the system on an edge computing device for practical application.

Conclusions:

  • The developed IoT system offers a reliable and objective method for real-time shelf-life prediction of dates.
  • This technology significantly enhances the reliability of shelf-life assessment, reducing postharvest losses.
  • The system contributes to improved food security by optimizing date preservation and supply chain management.