Prediction of Water Activity in Cured Meat using Microwave Spectroscopy
This work addresses the use of microwave techniques to determine quality parameters in cured meat. The first approach is online monitoring of weight loss in the meat curing process, which is a significant measurement for the meat industry because the weight loss is used as a method of tracking the curing process. Currently, weight loss is measured by using ordinary weighing scales, which is a time-consuming and impractical technique. Thus, a novel method is required to simplify the process by implementing an online monitoring technique. In this work, a set of microwave sensors were modelled using High Frequency Structure Simulation Software and then constructed and tested. Weight loss of the sample and change in the S11-parameter illustrated a strong linear relationship (R2 > 0.98). The prediction model then was developed using the Partial Least Squares method, which exhibited a good capability of microwave sensors to predict weight loss, with R2p (prediction) = 0.99 and root mean square error of prediction (RMSEP) = 0.41. The second approach is to determine water activity (aw) in cured meat, which is the parameter that describes available water for microorganisms and influences different chemical reactions in the product. For the cured meat industry, aw is the only moisture related measurement that is an accepted Hazard Analysis and Critical Control Point plan. This is important for safety reasons, but also for energy optimisation since curing requires controlled continuous temperature and humidity. Currently, aw is being measured by the meat industry using commercially available instruments, which have limitations, namely being destructive, expensive and time-consuming. Few attempts to develop non-destructive methods to predict aw have used X-ray systems (namely Computed Tomography), Near Infrared (NIR) and Hyperspectral Imaging (HSI). Although the techniques provided promising results, they are expensive, impractical and not commercially available for the meat industry. The results from the microwave sensors demonstrated a linear relationship (R2 = 0.75, R2 = 0.86 and R2 = 0.91) between the S11 and aw at 2.4 GHz, 5 GHz and 7 GHz, respectively. The prediction model exhibited a good capability of the sensors to predict aw (R2p = 0.91 and RMSEP = 0.0173)
TX341 Nutrition. Foods and food supply, TK Electrical engineering. Electronics. Nuclear engineering, TP Chemical technology
Networked Digital Library of Theses and Dissertations