Intelligent Microcontroller-Based Infrared Attenuated Total Reflection Spectroscopy for High-Throughput Screening and Discrimination of Foodborne Fungi.

Food safety became one of the most critical issues owing to the large expansion of international trading and emission of various pollutants in air, water and soil. Fungal contamination of food and feed has attracted most of the attention in the last decade because of the emerging analytical tools that facilitate the detection and discrimination of fungal species in imported foodstuff, seeds, grains, plants, meats …etc. In this work, we give an insight on the application of integrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and artificial-intelligence algorithms to the determination and discrimination of fungal species/strains which potentially infect plants, seeds and grains. The proposed method is based on a microcontroller which allows the PC to analyze a large number of samples via serial connection with an UART module. Penicillium chrysogenum, Aspergillus niger, Aspergillus fumigatus, Aspergillus solani, Aspergillus flavus and two different strains of Fusarium oxysporum were used as model microorganisms. The use of artificial-intelligence algorithms herein provides the advantage of automation enabling high throughput screening of large numbers of food samples in less than 5 s. In addition, the classification accuracy is enhanced by applying these machine-learning classification techniques. Principle component analysis (PCA) was used in order to extract the spectral discriminative features from the recorded fungal FTIR spectra. Three intelligent methods of classification; namely, artificial neural network (ANN), support-vector machine (SVM) and k-nearest neighbor (KNN), were used in this study in order to prove that integration of spectroscopic measurements with varying machine-learning methods give a simple analytical tool for detection and classification of foodborne pathogens. All the utilized classifiers gave an accuracy of 100 % and were able to discriminate different species and/or strains of the investigated fungi in few milliseconds.