Development of a machine learning model for systematics of Aspergillus section Nigri using synchrotron radiation-based fourier transform infrared spectroscopy

• Published in: Heliyon Vol. 10; no. 5; p. e26812
• Main Authors: Nuankaew, Salilaporn, Boonyuen, Nattawut, Thumanu, Kanjana, Pornputtapong, Natapol
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.03.2024; Elsevier
• Subjects: Black aspergilli; Convolutional neural network; Deep learning; FTIR spectroscopy; Fungal classification; Infrared synchrotron radiation; Deep learning; Infrared synchrotron radiation; Black aspergilli; Fungal classification; Convolutional neural network; FTIR spectroscopy
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e26812

Aspergillus section Nigri (black aspergilli) fungi are economically important food spoilage agents. Some species in this section also produce harmful mycotoxins in food. However, it is remarkably difficult to identify this fungal group at the species level using morphological and chemical characteristics. The molecular approach for classification is preferable; however, it is time-consuming, making it inappropriate for rapid testing of large numbers of samples. To address this, we explored synchrotron radiation-based Fourier transform infrared microspectroscopy (SR-FTIR) as a rapid method for obtaining data suitable for species classification. SR-FTIR data were obtained from the mycelia/conidia of 22 black aspergilli species. The Convolutional Neural Network (CNN) approach, a supervised deep learning algorithm, was used with SR-FTIR data to classify black aspergilli at the species level. A subset of the data was used to train the CNN model, and the model classification performance was evaluated using the validation data subsets. The model demonstrated a 95.97% accuracy in species classification on the testing (blind) data subset. The technique presented herein could be an alternative method for identifying problematic black aspergilli in the food industry.