Qualitatitive and quantitative analysis of fish spoilage processes for development of new food monitoring systems

This study investigates the growth and metabolite production of microorganisms causing spoilage of Atlantic cod (Gadus morhua) fillets packaged under air and modified atmosphere (60 % CO2, 40 % O2). Samples were provided by two different retailers (A and B). Storage of packaged fillets occurred at 4 °C and 8 °C. Microbiological quality and metabolite production of cod fillets stored in MAP 4 °C, MAP 8 °C and air were monitored during 13 days, 7 days and 3 days of storage, respectively. Volatile compounds concentration in the headspace were quantified by Selective ion flow tube mass spectrometry and a correlation with microbiological spoilage was studied. The onset of volatile compounds detection was observed to be mostly around 7 log cfu/g of total psychrotrophic count. Trimethylamine and dimethyl sulfide were found to be the dominant volatiles in all of the tested storage conditions, nevertheless there was no close correlation between concentrations of each main VOC and percentages of rejection based on sensory evaluation. According to results it was concluded that they cannot be considered as only indicators of the quality of cod fillets stored in modified atmosphere and air.  

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.