Development of A Labview-Based Electronic Nose Using Esp32 and Machine Learning for Food Spoilage Detection

The electronic nose (E-Nose) provides a rapid and non-destructive method for evaluating the quality andsafety of food products. In this, we try to address the design and implementation of a low-cost E-Nose for detectingthe extent of food deterioration. The hardware architecture comprises an array of metal oxide semiconductor (MOS)gas sensors, including MQ-135, MQ-4, and MQ-2 sensors, which are used to detect volatile organic compounds(VOCs), carbon dioxide (CO₂), methane (CH₄), and ammonia. To ensure high precision, the ADS1115 16-bit ADC isemployed, along with the DHT22 sensor for environmental correction. The array is integrated into an acrylic sealedcompartment equipped with an ESP32-S3 Xiao microcontroller and an active cleaning fan. The software architectureis constructed using NI LabVIEW and incorporates producer-consumer design patterns and event-based statemachine designs. During classification, differential voltages and slopes are extracted and transmitted through aPython node to principal component analysis (PCA) and k-nearest neighbour (k-NN) classification algorithms. Thissystem demonstrates high accuracy in classifying different thresholds of deterioration and offers the food industry areal-time, scalable, and highly efficient surveillance solution. By enabling the early detection of spoilage, thisframework directly contributes to environmental sustainability by reducing food waste and lowering the carbonfootprint of the agricultural supply chain