Da-Wen Sun

Improving quality inspection of food products by computer vision––a review

With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables. Computer vision has been successfully adopted for the quality analysis of meat and fish, pizza, cheese, and bread. Likewise grain quality and characteristics have been examined by this technique. This paper presents the significant elements of a computer vision system and emphasises the important aspects of the image processing technique coupled with a review of the most recent developments throughout the food industry.

Novel methods for rapid freezing and thawing of foods - a review

This paper reviews new developments in methods of freezing (high-pressure freezing, dehydrofreezing and applications of antifreeze protein and ice nucleation protein) and thawing (high-pressure and microwave thawing, ohmic thawing and acoustic thawing) for foods. With a good understanding of the solid‐liquid phase diagram of water, the e! ects of pressure on food freezing‐ thawing cycles are highlighted. High-pressure freezing promotes uniform and rapid ice nucleation and growth through the whole sample. Dehydrofreezing has been successfully used in freezing of vegetables and fruits with the advantage of less damage to plant texture because of partial water removal before freezing. Recently, studies have been carried out for the biotechnological use of antifreeze and ice nucleation proteins because of their uniqueness in directly improving freezing processes. Thawing under pressure can be achieved at lower temperature than that at atmospheric pressures. Finally microwave, ohmic and acoustic thawing are described. It is hoped that this paper will attract more research in novel freezing and thawing processes and methods. ! 2002 Elsevier Science Ltd. All rights reserved.

Inspection and grading of agricultural and food products by computer vision systems—a review

Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy ever-increasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food industry, then identifies areas for further research and wider application the technique.

Solar powered combined ejector-vapour compression cycle for air conditioning and refrigeration

Refrigeration and air conditioning play important roles in providing human comfort, food processing and storage and many other industrial processes. These applications are generally powered by electric energy. On the other hand, solar energy is widely available and has no fuel cost, which makes the running of solar powered refrigeration a viable and economic proposition. Unfortunately, research shows that solar powered conventional refrigeration systems have a relatively low coefficient of performance (COP). This paper describes a new way for harnessing solar energy, using a combined ejector-vapour compression cycle. This combined cycle brings together the advantages and eliminates the disadvantages of the two common conventional cycles. H2O is used as the refrigerant in the ejector sub-cycle and HFC-134a in the vapour compression sub-cycle. The study shows that the combined cycle is: (i) a potentially high performance system with a potential increase in system COP by more than 50% over the conventional cycles; (ii) a “green” system that conforms to all foreseeable ozone preserving regulations and reduces “green house” effects; and (iii) a cost effective system with low maintenance.

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles (semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900-1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination (R(2)) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.

Applications of computational fluid dynamics (cfd) in the food industry: a review

Abstract Computational fluid dynamics ( cfd ) is a simulation tool, which uses powerful computer and applied mathematics to model fluid flow situations for the prediction of heat, mass and momentum transfer and optimal design in industrial processes. It is only in recent years that cfd has been applied in the food processing industry. This paper reviews the application of cfd in food processing industries including drying, sterilisation, refrigeration and mixing. The advantages of using cfd are discussed and the future of cfd applications is also outlined.

Predictive food microbiology for the meat industry : a review

Predictive food microbiology (PFM) is an emerging multidisciplinary area of food microbiology. It encompasses such disciplines as mathematics, microbiology, engineering and chemistry to develop and apply mathematical models to predict the responses of microorganisms to specified environmental variables. This paper provides a critical review on the development of mathematical modelling with emphasis on modelling techniques, descriptions, classifications and their recent advances. It is concluded that the role and accuracy of predictive food microbiology will increase as understanding of the complex interactions between microorganisms and food becomes clearer. However the reliance of food microbiology on laboratory techniques and skilled personnel to determine process and food safety is still necessary.

Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review.

The requirements of reliability, expeditiousness, accuracy, consistency, and simplicity for quality assessment of food products encouraged the development of non-destructive technologies to meet the demands of consumers to obtain superior food qualities. Hyperspectral imaging is one of the most promising techniques currently investigated for quality evaluation purposes in numerous sorts of applications. The main advantage of the hyperspectral imaging system is its aptitude to incorporate both spectroscopy and imaging techniques not only to make a direct assessment of different components simultaneously but also to locate the spatial distribution of such components in the tested products. Associated with multivariate analysis protocols, hyperspectral imaging shows a convinced attitude to be dominated in food authentication and analysis in future. The marvellous potential of the hyperspectral imaging technique as a non-destructive tool has driven the development of more sophisticated hyperspectral imaging systems in food applications. The aim of this review is to give detailed outlines about the theory and principles of hyperspectral imaging and to focus primarily on its applications in the field of quality evaluation of agro-food products as well as its future applicability in modern food industries and research.

Microstructural change of potato tissues frozen by ultrasound-assisted immersion freezing

Abstract Power ultrasound has proved to be very useful in controlling crystallisation processes since sonication can enhance both the nucleation rate and crystal growth rate by producing fresh and/or more nucleation sites. Therefore, in this study, power ultrasound was applied to assist the freezing process. The results showed that the freezing rate of potato sample was improved with the application of ultrasound, compared to that without ultrasound. Higher output power and longer exposure time to ultrasound would help to enhance the freezing rate. However, the heat produced when ultrasound passes through the medium limited the power applied and the exposure time. In this work, the freezing rate was fastest with an output power of 15.85 W and a treatment time of 2 min. The analyses conducted on the microstructure of potato tissue using cryo-scanning electron microscopy technique showed that the plant tissue exhibited a better cellular structure under ultrasonic power of 15.85 W. Much less intercellular void and cell disruption was observed. This was attributed to high freezing rate obtained under high ultrasonic level and thus the domination of intracellular small ice crystals.

Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams.

Due to the high variability and complex colour distribution in meats and meat products, the colour signal calibration of any computer vision system used for colour quality evaluations, represents an essential condition for objective and consistent analyses. This paper compares two methods for CIE colour characterization using a computer vision system (CVS) based on digital photography; namely the polynomial transform procedure and the transform proposed by the sRGB standard. Also, it presents a procedure for evaluating the colour appearance and presence of pores and fat-connective tissue on pre-sliced hams made from pork, turkey and chicken. Our results showed high precision, in colour matching, for device characterization when the polynomial transform was used to match the CIE tristimulus values in comparison with the sRGB standard approach as indicated by their ΔE(ab)(∗) values. The [3×20] polynomial transfer matrix yielded a modelling accuracy averaging below 2.2 ΔE(ab)(∗) units. Using the sRGB transform, high variability was appreciated among the computed ΔE(ab)(∗) (8.8±4.2). The calibrated laboratory CVS, implemented with a low-cost digital camera, exhibited reproducible colour signals in a wide range of colours capable of pinpointing regions-of-interest and allowed the extraction of quantitative information from the overall ham slice surface with high accuracy. The extracted colour and morphological features showed potential for characterizing the appearance of ham slice surfaces. CVS is a tool that can objectively specify colour and appearance properties of non-uniformly coloured commercial ham slices.