A Novel Non-Intrusive Continuous Sensor (NICS) to estimate thermal conductivity of food products in manufacturing systems

Abstract

Fouling is a critical issue in commercial food manufacturing. Fouling can cause biofilm formation and pose a threat to safety of the food products. Early detection of fouling can lead to informed decision making about the product’s safety and quality, and effective system cleaning to avoid biofilm formation. In this study, a Non-Intrusive Continuous Sensor (NICS) was designed to estimate the thermal conductivity of the product as they flow through the system at high temperatures as an indicator of fouling. Thermal properties of food products are important for product and process design and to ensure food safety. Online monitoring of thermal properties during production and development stages at higher processing temperatures, ∼140 °C like current aseptic processes, is not possible due to limitations in sensing technology and safety concerns due to high temperature and pressure conditions. Such an in-line and noninvasive sensor can provide information about fouling layer formation, food safety issues, and quality degradation of the products. A computational fluid dynamics model was developed to simulate the flow within the sensor and provide predicted data output. Glycerol, water, reconstituted non-fat dry milk (NFDM), and 4% potato starch solution were selected as products. The product thermal conductivities were estimated at high temperatures (∼140 °C). Scaled sensitivity coefficients and optimal experimental design were taken into consideration to improve the accuracy of parameter estimates. Glycerol, water, NFDM, and 4% potato starch were estimated to have thermal conductivities of 0.292 ± 0.006, 0.638 ± 0.013, 0.598 ± 0.010, 0.487 ± 0.009 W/(m K, respectively. The sensor’s novelty lies in the short duration of the experiments, the non-intrusive aspect of its measurements, and its implementation of sequential estimation.