Research Projects in 2009-2010

Method for Improving the Quality of Frozen Foods by Assisting the Freezing Process and Reducing the Size of the Ice Crystals (MINICRYSTAL) (Postdoc)

A. E. Delgado and Da-Wen Sun

Sponsors: EU 7^th Framework

Past research has shown that high power ultrasound (HPU) can initiate ice nucleation and control crystal size distribution during the freezing process, leading to frozen products of better quality. As quality issues take increasing precedence among EU consumers, a key challenge facing frozen food manufacturers is how to reposition their frozen products so consumers can consider them as health-friendly and also of good quality. In response, this project will design and develop a prototype HPU system for its industrial validation in food freezing facilities. It will be cost- effective and easy to operate and easily integrated with commercially available freezing equipment.

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Design and Development of a Prototype HPU System for Its Industrial Validation in Food Freezing Facilities (MINICRYSTAL) (Postdoc)

Z. H. Zhang and Da-Wen Sun

Sponsors: EU 7^th Framework

Freezing is a very widespread process within the food processing industry, as a very important means of preserving fresh foodstuffs. However, ice crystal formed in the frozen foods can causes many irreversible physical and chemical changes within the food matrix, often leaving the final product with lower eating quality than when it was in the fresh state. This project will build upon promising research in the ability of high powered ultrasound (HPU) to initiate ice nucleation and to control crystal size distribution in the frozen product during solidification of liquid food. Past research has also shown promise for HPU to shorten the freezing process and lead to a product of better quality. This project will design and develop a prototype HPU system for its industrial validation in food freezing facilities. It will be cost-effective and easy to operate and easily integrated with commercially available freezing equipment.

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Development of a Novel Non-contact and Rapid Computer Vision System for Quality Evaluation and Control of Pre-sliced Cooked Hams  (Postdoc)

F. Mendoza and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

The production of high quality ham products with an attractive appearance and premium eating quality is an important goal for the meat industry. The digital image of a ham slice contains a large number of image features that can be easily extracted to be read quantitatively by a computer; this is analogous to a real ham slice that has quality attributes (such as colour and texture) which can be qualitatively perceived by human vision. The main goal of this project is to search and identify the most suitable image features that are linked to the quality attributes of ham, and can be also useful for objective quality control in real time. It considers the implementation of a colour calibrated computer vision system based on the CIE colour standard.

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Development of a Novel Non-contact and Rapid Computer Vision System for Quality Evaluation and Control of Pre-sliced Cooked Hams  (Postdoc)

G. El Masry and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

Hyperspectral imaging or imaging spectroscopy is a new technique that combines both imaging and spectroscopy techniques to acquire spatial and spectral information from an object. The three-dimensional image obtained from hyperspectral imaging is called “hypercube”. While the two spatial dimensions (x and y) describe the spatial features of the objects, the third dimension (λ) provides the spectral information for each pixel on the hyperspectral image cube. Because of this combined feature of imaging and spectroscopy, hyperspectral imaging can enhance the capability of detecting some chemical constituents in an object as well as their spatial distributions. Therefore, this project aims to develop a novel hyperspectral imaging system for quantitative and objective determination of meat quality. In order to do this, meat muscle of different attributes will be investigated in visible and near infrared (VIS/NIR) ranges of spectrum and the most critical image attributes relevant to meat quality (palatability) such as protein, water and fat content will be investigated. Measurements based on traditional instruments and sensory analysis will be also carried out to test, train and validate the hyperspectral imaging system, leading to the establishment of reliable meat quality predictors.

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Pork Meat Grading Using a Hyperspectral Imaging Technique (PhD)

D. Barbin D, G ElMasry and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

A near-infrared (NIR) hyperspectral imaging system in the range 900-1700 nm was developed for quality grading of pork meat. Pork samples were pre-classified in three quality grades, as reddish-pink, firm and non-exudative (RFN), pale, soft and exudative (PSE), and dark, firm and dry (DFD) based on measurements of colour, texture and exudation. Spectral data extracted from tested pork classes have shown that there are differences among pork meat qualities allowing the classification of samples based on their spectral features. Some significant wavelengths that are linked to the drip loss, pH and colour attributes were identified from the first derivative plots of the mean spectra. Principal component analysis (PCA) has shown that the spectral information can easily differentiate pork meat according to quality characteristics. Results indicted a potential application of hyperspectral technique as a fast and non-destructive assessment method of pork quality evaluation.

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Classification of Pre-sliced Turkey Ham Using NIR Hyperspectral Imaging (PhD)

A. Iqbal, Da-Wen Sun and P. Allen (Teagasc Ashtown Food Research Centre)

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

This study was carried out to develop a hyperspectral imaging system in the near infrared (NIR) region (900-1700 nm) to assess the quality of cooked turkey hams. Different qualities of turkey hams were studied based on their chemical ingredients and processing parameters used during processing. Hyperspectral images were acquired for ham slices originated from each quality grade and then their spectral data were extracted. Spectral data was analyzed using Principal component analysis (PCA) to reduce the high dimensionality of the data and for selecting some important wavelengths. Out of 241 wavelengths, only five selected wavelengths (980, 1061, 1141, 1215 and 1326 nm) were considered to be the optimum wavelengths for the classification and characterization of turkey hams. The data analysis showed that it is possible to separate different quality turkey hams with few numbers of wavelengths on the basis of their chemical composition.

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Classification of Lamb Muscles by NIR Hyperspectral Imaging (PhD)

M. Kamruzzaman, G. ElMasry and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

The potential of NIR hyperspectral imaging (900-1800 nm) to classify four types of lamb muscles were investigated. Muscles from semitendinosus (ST), semimembranosus (SM), Longissimus dorsi (LD) and Psoas Major (PM) of Charollais breed at 2-day post-mortem were tested in this study. Principal component analysis (PCA) was used for dimensionality reduction and to aid in visualizing the hyperspectral data. Four most effective wavelengths (980, 1141, 1208 and 1441nm) were then selected by PCA loading for the classification purposes. The results showed that hyperspectral imaging has a great capability for the classification of lamb muscles.

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Crystallization Phenomena During Freezing of Foods (PhD)

H. Kiani, A. E. Delgado and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

Water crystallization involving nucleation and crystal growth have been of interest for many years. In this article, the crystallization process, water crystallization and the methods of monitoring freezing process are analyzed. A wide range of methods have been used to evaluate the water crystallization process including light microscopy, electron microscopy, MRI and NMR methods. One of the most researched subjects in freezing processes had been the crystal size distribution and its impact on the structure and quality of the product. It is therefore worthy to study the ice crystallization phenomena in relation to ice crystal size distribution. This requires the knowledge of crystallization kinetics and the methods of controlling it in terms of final crystal size and the location of the crystals. Along with rapid freezing techniques, novel methods have also emerged dealing with ways to control crystal size distribution in foods. The positive effect of ultrasound on crystallization during freezing has proved useful recently and its application to food freezing is promising.

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The Effect of Power Ultrasound on the Freezing Rate and Drip Loss of Immersion Frozen Chicken Breast Meat (MEngSc)

C. Ryan, A. E. Delgado and Da-Wen Sun

Sponsors: University College Dublin

The efficient freezing and thawing of chicken meat is vitally important to the poultry sector. New technological advances have seen the potential use of power ultrasound to the immersion freezing of food products, which has many advantages. Applying ultrasound to freezing processes reduces the freezing time through the zone of ice crystal formation, resulting in better quality frozen products. The application of ultrasound promotes the growth of small uniform sized ice crystals within a product which in turn results in decreased drip loss and decreased freezing damage. Different variables affect the ultrasound assisted immersion freezing process differently, such as the type of holder that the sample is held in, the ultrasonic power level intensity, and the time that the product is exposed to ultrasound.

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Development of a Novel Image Analysis System for Predicting the Palatability of Beef (PhD)

P. Jackman, Da-Wen Sun and P. Allen (Teagasc Ashtown Food Research Centre)

Sponsors: Teagasc Walsh Fellowship, Dublin

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Destructive Characterization and Classification of Ham Surface Patterns Using Digital Image Processing Methods Based on Fractal Metrics and Quaternion Arithmetic (PhD)

N. A. Valous and Da-Wen Sun

Sponsors: EU Food Institutional Research Measure/Department of Agriculture, Fisheries and Food, Dublin

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Computational Fluid Dynamics to Design Naturally Ventilated Calf Building that Promote Animal Health and Welfare (PhD)

T. Norton, Jim Grant (Teagasc Kinsealy Research Centre), R. Fallon  (Teagasc Grange Research Centre) and Da-Wen Sun

Sponsors: Teagasc Walsh Fellowship, Dublin