Implementing a neuromorphic classifier for NIR spectra analysis of fruits

Near-infrared (NIR) spectroscopy is widely used in agriculture and the food industry to classify fruits and determine ripeness, soluble solids content, pH and acidity. Neuromorphic technology offers the potential for low-power real-time analysis systems based on NIR spectroscopy signals. This study presents a development pipeline for a neuromorphic classifier using Spiking Neural Networks (SNNs) to classify NIR spectra of fruit species. The SNN-based algorithm is implemented in the DYNAP-SE neuromorphic device. The classifier's performance is compared to non-spiking Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Python is used throughout the development, showcasing its versatility as a development tool.