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High temperatures accelerate the spoilage of dairy products, particularly milk, posing significant challenges for food safety and waste management. This study presents a novel sensor-based detection system designed to monitor milk spoilage by measuring real-time changes in pH levels and carbon monoxide (CO) concentrations, which are key indicators of microbial activity and biochemical shifts in milk. The system utilizes an Arduino UNO microcontroller integrated with a pH sensor and an MQ-7 gas sensor to assess milk freshness through a combination of pH and CO data. Results are displayed on an LCD screen with intuitive indicators of freshness status—categorized as “fresh,” “not so fresh,” or “spoiled”—ensuring ease of interpretation for users. Experimental validation across various milk samples demonstrates the system’s effectiveness in early spoilage detection. Future developments may focus on non-invasive methods and IoT-based miniaturization, incorporating machine learning algorithms for residual life prediction through blockchain integration. This approach promises to reduce food wastage and enhance food security, offering an accessible and sustainable solution for milk spoilage detection.

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Development of Rapid and Reliable Technique for Milk Spoilage Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Ritika Patki,
  • Srushti Jamewar,
  • Tanika Mathur,
  • Mridula Korde

摘要

High temperatures accelerate the spoilage of dairy products, particularly milk, posing significant challenges for food safety and waste management. This study presents a novel sensor-based detection system designed to monitor milk spoilage by measuring real-time changes in pH levels and carbon monoxide (CO) concentrations, which are key indicators of microbial activity and biochemical shifts in milk. The system utilizes an Arduino UNO microcontroller integrated with a pH sensor and an MQ-7 gas sensor to assess milk freshness through a combination of pH and CO data. Results are displayed on an LCD screen with intuitive indicators of freshness status—categorized as “fresh,” “not so fresh,” or “spoiled”—ensuring ease of interpretation for users. Experimental validation across various milk samples demonstrates the system’s effectiveness in early spoilage detection. Future developments may focus on non-invasive methods and IoT-based miniaturization, incorporating machine learning algorithms for residual life prediction through blockchain integration. This approach promises to reduce food wastage and enhance food security, offering an accessible and sustainable solution for milk spoilage detection.