The present work describes an alternative method for detecting and identifying Listeria monocytogenes in food samples by developing a nanophotonic biosensor containing bioreceptors and optical transducers. The development of photonic sensors for the detection of pathogens in the food industry involves the implementation of procedures for selecting probes against the antigens of interest and the functionalization of the sensor surfaces on which the said bioreceptors are located. As a previous step to functionalizing the biosensor, an immobilization control of these antibodies on silicon nitride surfaces was carried out to check the effectiveness of in plane immobilization. On the one hand, it was observed that a Listeria monocytogenes-specific polyclonal antibody has a greater binding capacity to the antigen at a wide range of concentrations. A Listeria monocytogenes monoclonal antibody is more specific and has a greater binding capacity only at low concentrations. An assay for evaluating selected antibodies against particular antigens of Listeria monocytogenes bacteria was designed to determine the binding specificity of each probe using the indirect ELISA detection technique. In addition, a validation method was established against the reference method for many replicates belonging to different batches of meat-detectable samples, with a medium and pre-enrichment time that allowed optimal recovery of the target microorganism. Moreover, no cross-reactivity with other nontarget bacteria was observed. Thus, this system is a simple, highly sensitive, and accurate platform for L. monocytogenes detection.

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 10⁶ CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample.

This paper presents a method that can be used to detect and identify Salmonella spp. in fresh meat and vegetable samples using a photonic biosensor with specialized bioreceptors. Detection was based on photon transduction. Silicon-nitride-based resonant cavities were used to capture the change in light response when there is specific binding of the immobilized antibody to the sensor surface against the target antigen. A control immobilization experiment was conducted to validate the immobilization process on the biosensor surface prior to biofunctionalization for Salmonella spp. detection. This experiment involved immobilization of pre-selected antibodies on silicon nitride surfaces. Two types of antibodies were suitable. The first was a specific polyclonal antibody with superior antigen-binding capacity across a wide range of concentrations. The second was a monoclonal antibody designed for effective binding at lower concentrations. Rigorous validation was performed. The outcomes were compared with those of the habitual method used to detect Salmonella spp. (reference method). Replicates from different batches of contaminated meat and vegetable samples were analyzed. This comprehensive approach provides a methodologically robust, highly sensitive, and accurate way of rapidly detecting Salmonella spp. in food samples. It has potential implications for improved food safety and quality control.

The current study outlines the advancement of an innovative technique for the simultaneous detection of E. coli O157:H7 and its Shiga-like toxins in food samples by utilizing a photonic label-free biosensor coupled with a microfluidic system. This detection method relies on ring resonator transduction that is functionalized with specific bioreceptors against O157:H7 on silicon nitride surfaces capable of binding specifically to the antigen bacterium and its verotoxins. This experiment included the characterization of selected monoclonal and polyclonal antibodies employed as detection probes through ELISA immunoassays exposed to target bacterial antigens. A thorough validation of photonic immunosensor detection was conducted on inoculated minced beef samples using reference standards for E. coli O157:H7 and its verotoxins (VTx1 and VTx2) and compared to gold-standard quantification. The lowest limit-of-detection values of 10 CFU/mL and 1 ppm were achieved for the detection of bacteria and its verotoxins. In this study, the lowest limit of quantification (LoQ) achieved for bacterial quantification was 100 CFU/mL, and, for verotoxins, it was 2 ppm. This study confirmed the effectiveness of a new quality control and food hygiene method, demonstrating the rapid and sensitive detection of E. coli O157:H7 and its verotoxins. This innovative approach has the potential to be applied in food production environments.

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission-founded PHOTONGATE project. This concept will allow for the quantification of multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a biochemical technology (molecular gates), which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which, combined with porous substrates, will increase the sensitivity, and a photonic technology based on localized surface plasmonic resonance (LSPR) structures that serve as transducers for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm²). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought of as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, which is fully European Union Made, avoiding optical fibers and expensive optical components.

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