Sensing technologies for sanitization of fresh food products

Food contamination poses not only risks to consumer health but also increases food spoilage processes, causing marketing and public health problems. Microorganisms can contaminate various foods, and their detection is fundamental to providing a safe food supply, preventing foodborne diseases, and reducing food spoilage. Indeed, microorganisms are one of the major causes of food spoilage and in the worst cases, their contamination can become a food safety issue, when consumer illnesses are induced. Timely identification of food contaminating microorganisms is of utmost importance not only for the commercialization of safe food products but also for the reduction of food losses and wastes. Detection of target bacteria can be achieved by cultural isolation or by indicators such as products of metabolism (gas, acid, substrates with chromogenic products). Optical oxygen (O2) sensing is based on dynamic collisional quenching of phosphorescence, which produces robust changes in sensor signal upon transition from high (air-saturated) to low (depleted by respiration) levels of dissolved molecular O2; The O2 sensing technology provides fast, onsite, and contactless analysis of microbial growth through oxygen consumption/respiration, with a simple set-up and quantitative real-time readouts. On the other hand, Raman spectroscopy (RS) relies on the application of laser beams to a matrix sample, detecting molecular vibrations of cellular metabolites present in a specimen, in the absence of labels or reagents; RS generates a chemical fingerprint of a specific matrix and has been employed as a method for label-free detection of microorganisms, as well as for their identification and antibiotic or antimicrobial susceptibility evaluation.

The activities will consist of:

• Collection of food-associated bacteria and fungi, including foodborne pathogens, plant pathogenic microbes, and food/environmental contaminants to Raman spectroscopy analysis, evaluating different methodological approaches. Raman spectra profiles will be gathered and after individual peak assignments, comparisons among microorganisms will be performed. The effectiveness of antimicrobial compounds will be evaluated by comparing Raman spectra profiles after treatment. Machine learning approaches will be applied to build a library of the spectra of each microorganism (CNR).
• Optimization of O2 respirometric sensor-based testing systems for the total aerobic microbial count for food applications, with the aim of onsite monitoring pathogenic and spoilage-related microflora in food and on surfaces. The system will include a sensor, an autonomous reader, and a portable incubator, and will be optimized to use larger-size sensor tubes (25 ml), swab vials (10 mL), or sponge-swabs sachets. The O2 sensor system will be validated for use to assess the efficacy of decontamination procedures on artificially inoculated pathogens and endogenous spoilage microflora (CNR).

• Production of a library of the Raman fingerprinting profiles of microbes associated with food, including foodborne pathogens, plant pathogenic microbes, and food/environmental contaminants (M24).
• Setup of at least one sensor system for the detection and enumeration of viable spoilage and pathogenic bacteria (M24).
• Development of Raman spectroscopy-based procedures to identify food-derived microorganisms and verify antimicrobial activity (M36).
• Application of at least one sensor system for the assessment of the effectiveness of decontamination procedures on food and surfaces (M36).