MACHINE LEARNING & AUTOMATION

aka Chemometrics

We are conducting curiosity driven, highly innovative fundamental research using multivariate modelling methods such as Partial Least Squares (PLS), Principal Component Analysis (PCA) and Support Vector Machines (SVM) in order to support food analysis. Highlights include the development of the new classification framework (CLPP) that preserves continuity in binary admixtures and a comprehensive framework for augmenting spectral data that would help spectroscopy based chemometric models become more universal and independent of the instrument used doing their calibration or testing. Ultimately, with innovation is food processing, these systems will lead to development of robust e-decision system to support  managers to take informed decisions in real-time and optimise production.
of food factories that together with a r