Development and assessment of new sensor systems in food packaging applications

The use of optical sensor technology for non-invasive determination of key quality pack parameters improved package/product quality. This technology can be used for optimization of packaging processes, improvement of product shelf-life and maintenance of quality. In recent years, there has been a major focus on O2 and CO2 sensor development as these are key gases used in modified atmosphere packaging (MAP) of food. The first and second experimental chapters (chapter 2 and 3) describe the development of O2, pH and CO2 solid state sensors and its (potential) use for food packaging applications. A dual-analyte sensor for dissolved O2 and pH with one bi-functional reporter dye (meso-substituted Pd- or Ptporphyrin) embedded in plasticized PVC membrane was developed in chapter 2. The developed CO2 sensor in chapter 3 was comprised of a phosphorescent reporter dye Pt(II)- tetrakis(pentafluorophenyl) porphyrin (PtTFPP) and a colourimetric pH indicator α-naphtholphthalein (NP) incorporated in a plastic matrix together with a phase transfer agent tetraoctyl- or cetyltrimethylammonium hydroxide (TOA-OH or CTA-OH). The third experimental chapter, chapter 4, described the development of liquid O2 sensors for rapid microbiological determination which are important for improvement and assurance of food safety systems. This automated screening assay produced characteristic profiles with a sharp increase in fluorescence above the baseline level at a certain threshold time (TT) which can be correlated with their initial microbial load and was applied to various raw fish and horticultural samples. Chapter 5, the fourth experimental chapter, reported upon the successful application of developed O2 and CO2 sensors for quality assessment of MAP mushrooms during storage for 7 days at 4°C.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.