An optical detector for latent fungal infection in produce

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Food waste is a major problem, with huge environmental and economic impact. A significant cause of waste are fungal infections. Ideally, fruit that has been infected should be removed from storage as soon as possible to prevent spread of the infection and potentially the loss of the entire store. However, fungal infections are often latent in their early stages, meaning they are inactive ion or just below the surface and cannot be detected at an early stage, e.g. before the fruit is stored. These latent infection can quickly progress during the storage, infection spreading to the neighbouring produce causing significant losses. If there was a reliable method to detect this latent infection, there is potential to significantly reduce wastage.High-resolution optical imaging methods have shown huge promise in medicine as a means of detecting disease, often at an early stage. There is therefore an obvious potential to translate this technology to agriculture. In particular, optical coherence tomography allows 3D images to be acquired in timescales of around a second, penetrating 1-2 mm into tissue. It provides structural information down to a resolution of less than 10 microns (0.01 mm) without the need for destructive sampling, staining or preparation of the sample in any way. However, while OCT has shown some promise for imaging plants and fruit for some applications, the simple structural information it provides may not provide reliable detection of the subtle changes associated with latent fungal infection.In this project we will investigate and demonstrate a new approach for early detection of fungal infection in fruits. We will bring together the expertise in the design and development of optical imaging systems at the Applied Optics Group, University of Kent, with the horticulture and plant pathology expertise of the Pest and Pathogen Ecology team at NIAB EMR. We will develop a new imaging device designed for the detection of latent, early stage fungus infection and test it using cherry samples known to be carrying the fungus Monilinia laxa.The physical principles of the device will be similar in its elementary structure to the conventional OCT, using a combination of a laser-scanning probe head, a broadband optical source and a fast spectrometer to allow fast 3D imaging. However, unlike in conventional OCT, which provides a static snapshot of the structure of the sample, new investigative modalities will be harnessed based on recent developments in the OCT field for biomedical diagnosis. These will be implemented to perform a more subtle analysis of fruit samples, based on acquiring multiple forms of functional or dynamic information, including looking at how the microstructure of the sample responds to an external mechanical stimulus, and measuring both the magnitude and the frequency of intrinsic fluctuations on the scale of cellular components. These three techniques, known as optical coherence elastography (OCE), speckle variance (SV), and the newly emerging dynamic OCT (DyOCT) will be implemented in a single device, allowing the fruit to be comprehensively evaluated.The sensitivity and accuracy of the optical tools (OCT, OCE, SV, DyOCT) will be benchmarked using destructive methods such as isolation of live pathogen, localisation using fluorescent microscopy and quantification of fungal biomass with quantitative PCR.