Research Projects in 1997-1998

Research into the Extension of Cut Flower Life by Rapid Pre-Cooling

Tadhg Brosnan and Da-Wen Sun

Cut Flowers are highly perishable products. As living biological entities, they respire and transpire even after detachment from the parent plant. Their shelf life and vase life can be extended by either retarding or completely inactivating certain physiological and deteriorating processes. The extension of their vase life has great economic advantages for the floriculture industry.

The research project involves the investigation of techniques for extending the life of the cut flowers starting from harvest, through storage, grading, packing, transportation, and retail to the final use. The effects of pre-harvest conditions such as stage of flower development at harvest, fertilisation, control of diseases and pest and species or cultivar, etc are also investigated.

The project also assesses the role that post harvest technologies have in influencing the life of the cut flowers. This involves in particular pre-cooling techniques and storage conditions so as to maintain the flower life and quality as long as possible. Many pre-cooling methods are depicted in this project but particular attention is paid to vacuum pre-cooling, which is based on the rapid evaporation of water vapour from the surface of the product. The effects of storage conditions such as temperature and humidity are also investigated. With this project, optimum conditions and techniques will be attained so as to maintain the maximum life of the cut flowers in storage, after storage and in use directly from harvest.

Sponsor: UCD Research Demonstratorship

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Development of an Integrated Management Information System for Cattle Farms

Dong Han, Da-Wen Sun and P. O'Kiely*

The main objective of this project is to develop a computerised cattle farm management support system. The system is expected to be used mainly by Teagasc beef production advisors and technically-leading Irish cattle farm owners/managers. It will also serve as a vehicle to transfer and disseminate Teagasc up-to-date research results and new technology to actual farming practice.

The development of the management support system begins with needs assessment and problems identification, then followed by identification and assessment of relevant approaches for addressing user needs, conceptual design, algorithms identification, programming and implementation.

The needs of the potential users are mainly addressed from two aspects-functionality of the system and user-friendliness.

The system is designed to cater for the essential management needs arising from the actual operation of a cattle farm. Cattle farm management can be divided into three basic functions: planning, implementation and control. The systems aims at providing evaluation tool for decisions upon long-term and medium term strategic issues in the cattle farm operation.

The main modules in the system deal with grazing/conservation planning, grass yields and quality estimation, animal performance evaluation, feeding budgeting, planning and selection of beef production system (a 18 month or 24 month been production system) under planned beef production regime, and the risk evaluation under the specific condition.

To integrate Teagasc research results into the system and select appropriate models that suit the Irish situation, models derived from Teagasc research results or recommended by Teagasc are used as much as possible. Best operation practices advocated by Teagasc are taken as beef farm operation guidelines or their applicability will be evaluated first using the system. In the package, the revised Johnstown grass growth model is used to predict grass growth under the specific weather pattern, statistical models derived from Grange Research Centre pasture field experiments are used to evaluate feeding value of the grass and silage.

The effect of fertiliser application level are considered using results from Teagasc field experiment, and animal maintenance and production nutrition required are evaluated using models developed in Ireland, so is the effect of water deficit on grass yield and quality.

The framework of the system can be roughly divided into four parts: models, data and databases (including knowledge base), software system and user interface. Besides the consideration of the ecosystem consisting of animals, grassland, soil, and climate, market condition and regulation regime should also be considered in actual decision making because the beef production industry in Ireland is severely mediated by EU policy.

The end-users' demand for a practical yet easy-to-use system deserves special attention. Many computerised system failed because the models they used require excessive input data or special knowledge from the user. A program may be technically very powerful, but its acceptance will be limited if it is too difficult to use. User-friendliness is a special concern for the management information system under developing.

* Teagasc Grange Research Centre

Sponsor: Teagasc Walsh Fellowship

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Simulation of Cooling Processes in the Food Industry Using Computational Fluid Dynamics

Zehua Hu and Da-Wen Sun

In order to preserve the quality and safety of cooked meats, the cooling process should be commenced as soon as cooking is finished. The meat core temperature should be reduced to 4^oC within a certain period. The cooling time of the cooked meats in an air-blast chillier is mainly affected by the size, shape, weight, specific heat and thermal conductivity of the meats, the structure of the chiller, the operating conditions such as meat initial temperature, cooling air temperature and velocity, and the arrangement of the load. However, in practice predicting the cooling time by considering all the above factors can be complicated and time-consuming without using sophisticated numerical analysis methods such as computational fluid dynamics (CFD).

In this research a CFD model was developed for chilling cooked meat joints within a commercial air-blast chiller and experiments were carried out to verify the model. The model combines modelling of airflow behaviour and heat transfer inside air blast freezers and involve solving equations for mass, momentum and energy conservation. A k-e model of turbulence was used. Velocity profile and temperature distributions were predicted. The CFD results showed good agreement with the measured data. The simulation also showed that its accuracy mainly depended on the turbulence model used, the setting of boundary conditions, and the fineness of the mesh and the iteration steps. Based on the CFD model established, further simulations were conducted to predict the chilling process under different operating conditions so that the cooling parameters can be optimised to meet the guidelines on cook-chill system. The current CFD simulations demonstrates great potential benefits for the applications of CFD technology to the industry for optimising the design of chillers and cold rooms and their operating conditions. Further study will be extended to model vacuum coolers and refrigerated warehouses/coldrooms.

Sponsors: EU Food Research Programme, Teagasc

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Rapid Cooling of Cooked Meats and its Effects on Quality

Karl McDonald and Da-Wen Sun

A research vacuum-cooling unit was especially built to study the effects of a rapid chilling method on cooked meats. The vacuum chilling efficiency is compared with other common methods of chilling including water immersion and air-blast chilling. Experiments to date have involved using large boneless ham and beef joints. Results have indicated significant differences between chilling methods. Vacuum cooling rates are up to 80% quicker than the other methods in cooling samples from 70^oC to 4^oC with cooling rate affected by packaging materials used. However mass losses were highest in vacuum cooled samples.

Quality analysis has involved the use of instrumental techniques to measure such attributes as texture, colour, water-holding capacity and oxidative rancidity with comparisons drawn against trained sensory panels. In general the results to-date indicated that vacuum cooling alters the yield and quality of both cooked beef and ham. Sensory analysis showed that vacuum cooled hams were tougher, drier and slightly darker than the other hams but no differences in overall acceptability were found. Instrumental texture analysis using the Instron Universal Testing Machine also showed a similar trend to sensory analysis with vacuum cooled samples having the highest hardness values. Thiobarbituric acid value, which is a chemical measurement of oxidative rancidity, showed that while vacuum cooling gave the highest values, all samples were satisfactory after storage in vacuum-pack for 9 days at 4^OC. Water-holding capacity results showed no difference between treatments.

Further research is needed to determine whether mass losses in the vacuum cooling system can be reduced. Losses during vacuum cooling are an inherent requirement of this technique and a certain mass must be lost for satisfactory cooling. However it may be possible to offset this by using a higher level of injection provided quality of the final product is not impaired. Texture and colour improvements could be made by incorporation of processing ingredients, use of novel packaging and variation of evacuation pumping speed.

Sponsors: EU Food Research Programme, Teagasc

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Developing a Computer Vision System for Quality inspection of pizzas

Xiaowei Tang and Da-Wen Sun

The objective of this research is to investigate how to build a computer vision system (CVS) for inspecting pizzas. Over the past two decades, tens of CVSs have been developed to inspect a wide range of foods, such as potatoes, apples and fishes. However, very limited work can be found in applying vision technology to pizzas and other prepared customer foods. Therefore, a computer vision system is being developed. The system architecture consists of eleven components: illumination measure, image acquisition, image pre-processing, segmentation, object recognition and description, feature extraction, neural network classification, pizza topping model generator, pizza knowledge base and user interfaces. More than twenty features of pizzas are identified and studied, which mainly include regular local and global histograms; colours, shapes and distributions of toppings; exposure percentages, intended layouts, smooth surfaces and round contours of pizzas.

The difficulties arise due to pizza varieties, inhomogeneous surfaces and different features on changing storage and cooking conditions. Image processing and analysis is usually application-specific, especially in image segmentation. Traditional segmentation techniques were found only suitable for the prepared pizzas with homogeneous appearance. For instance, the images captured from simple pizzas like cheese & tomato sauce pizzas can successfully be segmented by a thresholding technique, which can reach over 95% accuracy on measuring exposure percentage, and the topping distribution can be evaluated well by comparing equally-partitioned wedges. However, for most of pizzas, the RGB scopes of different toppings are heavily overlapping, and some toppings are inhomogeneous by nature or due to overlapping layout, light reflection, noises and shadows.

Therefore, implementing a set of segmentation algorithms that combine multiple techniques such as threshold, edge-detection, region-growing and pattern match has been focused. In particular, a pre-processing module for making pizza images homogeneous and a new efficient region-based algorithm are developed. The module typically runs as a set of procedures that consist of detecting edges, clustering colours, removing small regions and averaging image. Experimental work shows the RGB scopes of individual topping can be reduced by 30% after the pre-processing. Since significant edges are preserved in the pre-processing phrase, the new region-growing algorithm goes smoothly. The algorithm initially divides an image into many horizontal lines according to detected edges, then merges the lines row by row into small homogeneous regions, and finally merges adjacent small regions into significant regions that represent different toppings. The algorithm achieves a good segmentation result with an average accuracy of 90%, high speed and generality. Compared to typical region-growing algorithms, it only needs 2/3 time to segment a complicated pizza image since it has exact growing areas. The certain starting points and stopping conditions make the algorithm generally suitable for many applications. Further research will address the problems caused by overlapping toppings, and investigate the application of neural network technique to pizza classification.

Sponsors: EU Food Research Programme, Teagasc

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Pizza Cheese Evaluation with Computer Vision System

Hai-Hong Wang and Da-Wen Sun

There has been a dramatic increase in pizza market over a world-wide range. The retail sales of frozen pizza have been boosted throughout Europe, while popularity of pizza is still growing in the US. Irish pizza industry is expected to experience an expansion of at least 10% a year. The strong growth of pizza production has led to considerably increasing attention to cheese over the last two decades. It is believed that quality in pizza is mainly about cheese. In the US, where pizza sales have been increasing for 40 years, pizza quality is synonymous with cheese. The unique characteristics of cheese contribute to the distinctive appearance and flavour of pizza. However, quality problems including melting, shredability, blistering, browning and stretchability were complained by many pizza restaurants. As a result of heightened consumer awareness of health, oiling-off property of pizza cheese, which is the tendency to form excessive free oil upon melting, has aroused intensifying concern. Manufacture of products with desirable qualities relies upon a better understanding of the relationship between fundamental characteristics and functional behaviour of pizza cheese. Though some attempts have been made in this field, a major obstacle remains that objective methods to measure cheese properties are lacking.

Image analysis technique using computer vision system has become increasingly popular in assuring quality of a wide-range of products. A typical computer vision system comprises different parts for image acquisition, transferring, transition, display, processing and storage, respectively. It essentially simulates what the eye sees, and through a set of image processing procedures as image enhancement, restoration, segmentation, recognition and so on, allows further measurement and analysis of sample properties available. In the food industry, computer vision systems have been used for quality evaluation and variety classification of various materials such as fruits, vegetables, nuts, cereal and meats. It is evident that image processing technique is capable of discriminating the difference in colour, size, shape, number and even texture. On this basis, information on relevant characteristics has been obtained. Image processing technique has shown great advantage in objective, rapid and automated quality evaluation. Many cheese properties like melting, stretchability, browning and oiling-off are appearance-related, therefore it is reasonable to presume that computer vision system may provide more objective, efficient and reliable information on cheese quality than traditional methods.

Sponsors: EU Food Research Programme, Teagasc

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Modelling of the Food Freezing Process

Xing Zhu and Da-Wen Sun

Freezing and cold storage are important refrigeration processes applied widely in the meat industry. Accurate prediction of freezing time is necessary for optimum design and operation of food freezers.

In this studya finite difference method was used to predict the freezing time of regularly shaped beef.The predicted accuracy was found to be affected by the thermal properties of the materials.A scheme was therefore designed to select an appropriate thermal conductivity (k-value) equation for freezing time prediction. The freezing time of the meat was experimentally determined in a cabinet blast freezer for various air temperatures and moisture contents. Experimental data were used to verify the numerical results. The modified Crank-Nicholson scheme was employed for numerical analyses with various thermal conductivity equations that are functions of temperatures and moisture contents. Based on this, an appropriate method for calculating the k-value to predict the freezing time was obtained.

The effect of heat transfer directivity factor was also analysed in the study. When meat is being frozen, heat can transfer parallel to or perpendicular to muscle fibres at different rates. The emphasis of this work is on increasing the accuracy of freezing time prediction when heat flows perpendicular to the meat muscle fibril direction. A simulation program was written and two thermal conductivity formulas (Levy equation and series equation) were incorporated into the program. The freezing time of beef was also experimentally determined.Finally, simulation was compared with experimental data. The comparison resulted in that a modified series equation was developed which increased the accuracy of the predicted results.

Sponsor: UCD Research Demonstratorship

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Predicting Weight Loss during Food Freezing Processes based on Heat and Mass Transfer Analysis

Adriana Delgado and Da-Wen Sun

The freezing process is frequently considered as a heat conduction problem, and mass transfer due to evaporation or sublimation is generally neglected. However, weight loss, as a result of mass transfer between the product surface and freezing air, is one of the most important factors considered in the food industry, therefore, it must be taken into account for better predicting the freezing time. Sublimation, which is also a

mass transfer process, occurring on product surfaces during freezing is also an important factor, since the dried layer formed will affect the product quality such as tenderness, colour, texture, etc.

Therefore, the current research work focuses on modelling the coupled heat and mass transfer. The model is used to analyse and control product quality under various operating conditions. Experimental work will also be carried out to measure the heat and mass transfer coefficients, thermal properties of foods, and weight loss of products for the model, and to verify the simulation model.

Sponsor: National Council of Technological and Scientific Research of Argentina, EU Food Research Programme

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Development of a Rapid Cooling Technology for the Cooked Meat Industry

Lijun Wang and Da-Wen Sun

A vacuum test rig was built and experiments on cooked meat joints were carried out. The data acquisition system was configured for the experiments. For comparison purpose, experiments on conventional air blast-chiller were also performed. Preliminary experimental results show that vacuum cooling is a very rapid cooling technique for cooked meats. This vacuum cooling method can be used in a wide area in agriculture and food industries.

Sponsors: EU Food Research Programme, Teagasc

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Rapid Vacuum Cooling of Vegetables

Dejun Yangand Da-Wen Sun

Vacuum cooling is traditionally identified as an effective rapid cooling method for leafy vegetables such as lettuce. In order to test its feasibility for more vegetables, experiments are carried out to include non-leafy vegetables such as pepper and cauliflower. For comparison, experiments using air blast chilling are also performed. The effect of spraying water on vegetables before vacuum cooling on controlling weight loss and improving cooling efficiency is also examined in the current study.

Sponsors: EU Food Research Programme

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Modelling the Drying Process of Foods Using Computational Fluid Dynamics

Canchun Jia and Da-Wen Sun

Drying is a common process used in the food industry. Accurate modelling of the process leads to better control of product quality and optimum design of drying equipment. In the current study, a commercial computational fluid dynamics (CFD) package is used to simulate the coupled air flow and heat transfer processes during drying. Experimental data are then used to verify the modelling, and finally drying parameters are optimised.

Sponsor: UCD Research Demonstratorship

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