FoodML: Development of a food quality and safety risk management system, using cloud computing, big data and data science

Currently, food quality and safety controls relies heavily on regulatory inspection and sampling regimes. Such approaches are often based on conventional chemical and microbiological analysis, making the ultimate goal of 100% real-time inspection technically, financially and logistically impossible.
Over the past decade, rapid non-invasive techniques (e.g. vibrational spectroscopy, hyperspectral / Multispectral imagining) started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques.
Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine learning algorithms before the results can be interpreted. Although these platform has been showing great potentials to accurately and quantitatively assess freshness profiles (Panagou, Mohareb et al. 2011) (Mohareb, Iriondo et al. 2015) and safety parameters as well as adulteration (Ropodi, Panagou et al. 2016), their dependence on advanced data mining and statistical algorithms made was the main challenge facing their practical implementation across the food production and supply chain.
In order to overcome these challenges, we have developed sorfML (http://elvis.misc.cranfield.ac.uk/SORF), a Web platform prototype compatible with outputs from 5 instrumental platforms (See Figure) which provides means for interactive data visualisation, multivariate analysis (Principal component analysis and hierarchical clustering), as well as the ability to use stored datasets to develop predictive models to estimate food quality. Currently, the platform provides users with means to upload their experimental datasets to the server, thanks to the truly generic MongoDB NoSQL database backend, and to develop classification and regression models to estimate quality parameters.
The aim of this PhD is to expand the existing sorfML platform into a cloud-enabled framework that supports real-time monitoring of food products throughout the production chain. In order to achieve this, a series of advanced portable sensory devices will be deployed to examine their suitability as "Connected devices" in predicting quality and safety indices for various food perishable food products. A series of machine learning and pattern recognition models will be developed and integrated within the cloud system. This includes Ordinary Least Squares, Stepwise Linear classification and regression, Principal Component regression, Partial Least Squares discriminant analysis, support vector machine, Random forests and k-Nearest Neighbours.