Early and swift identification of fungal-infection using infrared spectroscopy

Fungal pathogens pose significant threats to agricultural crops and food products, leading to economic losses, compromised food quality, and health hazards. Early detection is crucial for effective control and treatment. This study explores Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy for rapid fungal detection in bread. Using a machine learning algorithm (Random Forest), FTIR-ATR accurately distinguished between pure and infected bread samples, achieving 86% overall accuracy and 84% accuracy in identifying specific fungi like Rhizopus and Aspergillus on the first day of infection. These findings highlight FTIR-ATR's potential for early fungal infection detection, promising improved food quality and reduced economic losses through timely intervention.