Projects
Glucosens
According to the estimates of the World Health Organization (WHO), the number of people with diabetes will increase from 177 million in 2000 to 300 million by 2025. It is also estimated that 9% of all deaths worldwide are due to diabetes. Although diabetes is presently not curable, intensive insulin therapy in diabetic patients can dramatically delay the onset of serious complications. It is demonstrated that tight or continuous blood glucose control can introduce substantial reductions in overall medical care costs. To date, implantable glucose sensors are all based on surface chemical reactions. Such sensors are very stable, accurate and sensitive in vitro. However, once the sensors are implanted, the stability and reliability reduces dramatically after a few days owing in large part to fouling of the sensor surface by proteinaceous material.
They can therefore not be used for long term implantation. Using a spectrometric device encapsulated in a transparent, biocompatible material, long term in vivo operation of glucose sensors could be realized, justifying the surgical procedure to implant the device.
Rationale: Development of implantable single‐chip optical sensors for long‐term continuous glucose monitoring (CGM) based on a silicon photonics integrated spectrometer.
Objectives: While the impact of a CGM‐implant would be huge, there are important scientific risks in the proposed approach. All resources in this project will be geared towards research aiming to eliminate these risks. There are three major risks and associated to those we will develop three generic strategic research platforms: 1) Optical tissue model; 2) Miniaturized optical sensors; 3) Optically transparent biocompatible materials.
The team of MeBioS is devoted in the optical tissue models by investigating the interaction between biological tissues and light photons. The emphasis is on the measurement of optical properties of biological tissues, the analysis of the measured spectra, and finally process design and control.
InsideFood
http://www.insidefood.eu/INSIDEFOOD_WEB/UK/Home.awp
Food microstructure is defined as the organisation of food constituents at the microscale and their interaction. Most solid foods, including bakery products, fruits, vegetables and meat, are microstructured. Many properties of foods that are relavent to food process engineering or quality are related to their microstructure. Examples include sponginess of bread, cripsness or crunchiness of crackers, gas and water transport properties of fruit, colour as related to light scattering properties just beneath the surface of the food. Food processing operations affect food microstructure: existing structures are destroyed and new ones are created. Insight in food microstructure and how it changes during processing operations are essential to produce high quality food. In particular, consumer demands for enhanced nutritional quality (composition), sensory quality (texture, internal defects) and safety (absence of foreign materials) are driving manufacturers to optimise products and processes with respect to microstructure.
In this EU project (FP7-226783), with the acronym InsideFood (‘Integrated sensing and imaging devices for designing, monitoring, and controlling microstructure of foods’), MeBioS Biophotonics Group involves in developing optical microstructure sensors based on diffuse spectroscopy in the visible and near infrared spectral region by innovative optical method (spatially resolved reflectance spectroscopy) for cheap, on-line, non-invasive, non-destructive applications and relating absorption coefficient and reduced scattering coefficient to food microstructure and food composition.
Chameleon (IWT SBO)
More information will follow
Optimization of on-line spectroscopy on raw milk for biomonitoring cow health (FWO)
The goal of the project is to study the complex interaction between Vis/NIR light and highly scattering and absorbing suspensions and emulsions like milk. These light propagation models will serve in a next phase to design optical sensors which can be implemented on-line in milking systems to monitor and promote individual cow health.
Computational optimization of a sensor design
http://www.kuleuven.be/research/researchdatabase/project/3E10/3E100990.htm
The food industry has been altered due to the increasing demand of functional and quality food. The consumer often wishes to eat products that taste as good as sugary or fatty foods, but are much healthier at the same time. Certain limitations occur, due to the relationship between the microstructure of food and the taste experience. Adjusting the chemical composition for health reasons, will often have a negative impact on microstructure, and therefore on the perceived taste. To tackle this problem, one requires fast and non-destructive measuring techniques.
One needs to make a clear distinction in this research between the chemical composition (related to absorption) and the microstructure (related to scattering) of food. An interesting approach to obtain this information, is spatially resolved Vis/NIR spectroscopy. This method uses light propagation models, which allow to compare the measured reflectance/transmittance data with simulated spectra. The result is a set of optical properties, which can be related to the chemical composition (related to absorption) and the microstructure (related to scattering).
When optically characterizing a specific biological tissue, one should ideally use an adapted sensor. One could use a general sensor for tissues, but in this way one might measure a lot of excessive information, which just increases the total processing time. In general, cost efficient sensors are designed by an iterative process of building and testing prototypes. Because of the significant investment for building prototypes, each iteration is a costly and time consuming step. Therefore only a few iterations are being performed. The result is a sub-optimal sensor. When performing this optimization ‘in silico’, one could use the light propagation model to computationally create an optimal sensor design.
Innovative spatially-resolved spectroscopy techniques for non-destructive internal quality inspection of tropical fruits in Vietnam (IRO)
Storage and transport of tropical fruits after harvest induce internal quality changes due to changes in microstructure and compositions. Therefore, good selection of tropical fruits with high quality and storage potential for long distance shipment will provide high quality products at the arrival harbours and avoid shipping waste. However, traditional invasive methods for quality inspection inevitably carry many disadvantages: time-consuming, waste disposal due to destruction of tested products, labour intensive, limitation in number of inspected products. Therefore, innovative and environment-friendly non-destructive ways for quality inspection would have high value for practical applications. In recent years, thanks to the development of optical sensors and spectroscopy techniques, many research results have been published on quality evaluation of foods based on hyperspectral imaging or diffuse reflectance spectroscopy. In these studies black box approaches have been applied to link the measured spectra to the properties of interest purely based on correlation in the calibration data, neglecting the underlying physics. Actually, the measured spectrum is the result of a complex interplay of light scattering and absorption. The former is related to the physical properties, while the latter is caused by the chemical composition of the products. This confounding of information often leads to erroneous assessment when applying statistical models based on measured signals to evaluate or predict individual chemical properties (water content, soluble solids contents in fruits…) or physical properties (firmness, crispness…) of the samples.
This project aims at obtaining the structural as well as the compositional information of tropical fruits in Vietnam by applying innovative spatially-resolved spectroscopy techniques. In the first stage, the technique will be developed in Belgium and implemented on locally available samples: apples, pears, crispy bread, breakfast cereals….In the second stage, the successfully developed technique will be brought to Vietnam for measurements of local tropical fruits (mango, papaya….)
Pear Thinning (IWT)
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Project Crop (EU)
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Sensors for Food
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Nofima Novel Sensor
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