Award details

An optical detector for latent fungal infection in produce

Reference	BB/X003744/1
Principal Investigator / Supervisor	Professor Adrian Podoleanu
Co-Investigators / Co-Supervisors	Dr Michael Hughes, Dr Matevz Rupar, Professor Xiangming Xu
Institution	University of Kent
Department	Sch of Physical Sciences
Funding type	Research
Value (£)	184,610
Status	Current
Type	Research Grant
Start date	01/10/2022
End date	30/11/2023
Duration	14 months

Abstract

An imaging system will be developed to allow detection of latent fungal infection in produce with a view to early detection and hence avoidance of additional food spoilage and waste. The project is a collaboration between the Applied Optics Group, University of Kent and the Pest and Pathogen Ecology Team at NIAB EMR, and will employ a PDRA to develop the imaging system, hosted at the University of Kent, and part time technicians at both sites. The imaging probe will implement three functional imaging techniques built on Fourier domain optical coherence tomography. Optical coherence elastography (OCE) measures the response of the sample to the pressure wave from an external mechanical stimulus (an air puff), allowing a map of elastography to be assembled. Speckle variance (SV) analysis provides the amplitude of time variation of coherent speckle in the OCT volume to allow a 3D map of sample micro-motion to be built. Dynamic OCT (DyOCT) is a recently emerged technique in which the frequency dependence of sub-resolution scale fluctuations are analysed to allow distinction to be made between tissue sub-types. It will be possible to generate all three datasets, alongside structural OCT volumes for reference, using fruit samples fixed in place using a specially-designed mount. This will provide the phase stability required for SV and particularly DyOCT measurements which will be made over time periods of seconds to minutes. Following assembly, characterisation and performance testing, the new imaging system will be evaluated primarily on a single cultivar of cherry inoculated with Monilinia laxa and imaged at regular timepoints post-infection, with uninoculated cherries used as a control. Ground truth of infection status will be determined using classical destructive microscopic and modern molecular (qPCR) assessments, as well as through visual assessment of developing disease symptoms.

Summary

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.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	UKRI Basic Technologies [2022]
Funding Scheme	X – not Funded via a specific Funding Scheme

I accept the terms and conditions of use (opens in new window)

export PDF file

back to list new search