Image Analysis

Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality.
Background: Microglia, the brain's principal immune cell, are increasingly implicated in Alzheimer's disease (AD), but the molecular interfaces through which these cells contribute to amyloid beta (Aβ)-related neurodegeneration are unclear. We recently identified microglial contributions to the homeostatic and disease-associated modulation of perineuronal nets (PNNs), extracellular matrix structures that enwrap and stabilize neuronal synapses, but whether PNNs are altered in AD remains controversial.