Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model. (September 2021) - Soni, Aswathi; Al-Sarayreh, Mahmoud; Reis, Marlon M.; Brightwell, Gale
Graphical abstract: Highlights: Dead and live Clostridium sporogenes spores were identified using HSI. Multivariate analysis was successful in quantification the spores on culture media. 1D-CNN approach was successful in quantification of spores on mashed potato. Dead spores in samples interfered with classification accuracy of 1D-CNN approach. Abstract: Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547–1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (10 2 to 10 6 CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4% and 8% in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90–94% when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (<10 2 CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays.