A Multiple Approach Combined with Portable Electronic Nose for Assessment of Post-harvest Sapota Contamination by Foodborne Pathogens

Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue

Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy.

Robust and Rapid Detection of Mixed Volatile Organic Compounds in Flow Through Air by a Low Cost Electronic Nose

This work aims to detect volatile organic compounds (VOC), i.e., acetone, ethanol and isopropyl alcohol (IPA) and their binary and ternary mixtures in a simulated indoor ventilation system. Four metal-oxide-semiconductor (MOS) gas sensors were chosen to form an electronic nose and it was used in a flow-through system. To speed up the detection process, transient signals were used to extracted features, as opposed to commonly used steady-state signals, which would require long time stabilization of testing parameters. Five parameters were extracted including three in phase space and two in time space. Classifier and regression models based on backpropagation neural network (BPNN) were used for the qualitative and quantitative detection of VOC mixtures. The VOCs were mixed at different ratios; ethanol and isopropyl alcohol had similar physical and chemical properties, both being challenging in terms of obtaining quantitative results. To estimate the amounts of VOC in the mixtures, the Levenberg–Marquardt algorithm was chosen in network training. When compared with the multivariate linear regression method, the BPNN-based model offered better performance on differentiating ethanol and IPA. The test accuracy of the classification was 82.6%. The concept used in this work could be readily translated for detecting closely related chemicals.