Recent outbreaks of food borne illnesses continue to support the need for rapid and sensitive methods for detection of foodborne pathogens. A method for detecting Listeria monocytogenes in food samples was developed using an automated fiber-optic-based immunosensor, RAPTOR ™. Detection of L. monocytogenes in phosphate buffered saline (PBS) was performed to evaluate both static and flow through antibody immobilization methods for capture antibodies in a sandwich assay. Subsequent detection in frankfurter samples was conducted using a flow through immobilization system. A two stage blocking using biotinylated bovine serum albumin (b-BSA) and BSA was effectively employed to reduce the non-specific binding. The sandwich assay using static or flow through mode of antibody immobilization could detect 1×10 3 cfu/ml in PBS. However, the effective disassociation constant K d and the binding valences for static modes of antibody immobilization in spiked PBS samples was 4×10 5 cfu/ml and 4.9 as compared to 7×10 4 cfu/ml and 3.9 for flow through method of antibody immobilization. Thus the sensitive flow-through immobilization method was used to test food samples, which could detect 5×10 5 cfu/ml of L. monocytogenes in frankfurter sample. The responses at the lowest detectable cell numbers in the frankfurter samples was 92.5 ± 14.6 pA for L. monocytogenes to comparative responses of 27.9 ± 12.2 and 31 ± 14.04 pA obtained from Enterococcus Sensors 2006, 6 809 faecalis and Lactobacillus rhamnosus (control species), respectively. The effective K d and binding valency from spiked frankfurter samples was 4.8×10 5 cfu/ml and 3.1, thus showing highly sensitive detection can be achieved using the RAPTOR ™ biosensor even in the presence of other bacterial species in the matrix.

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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