Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Laser and optical based methods for detecting and characterising microorganisms

This work investigated novel optical methods of characterizing the activity of microorganisms. Two different systems are studied in detail in this work. The possibility of using line scan speckle systems and imaging systems to understand the microbial behaviour, growth and motility was investigated. Conventionally, the growth and viability of microorganisms are determined by swabbing, plating and incubation, typically at 37degreesC for at least 24 hours. The proposed system allows real-time quantification of morphology and population changes of the microorganisms. An important aspect of the line scan system is the dynamic biospeckle. Dynamic speckle can be obtained from the movement of particles suspended in liquids. The speckle patterns show fluctuations in space and time which may be correlated with the activity of the constituents in the suspension. Initially the speckle parameters were standardized to non-motile and inert specimens such as polystyrene microspheres and suspensions of Staphylococcus aureus. The same optical systems and parameters were later tested on motile, active and live organisms of Escherichia coli. The experimental results that are presented describe the time history of the dynamic speckle pattern. A number of algorithms were used to analyse the intensity data. A 2D-FFT algorithm was used to evaluate the space and time-varying autocorrelation. Analysis of the speckle data in the Fourier domain provided insight into the motility of the organisms in broth. The mathematical analysis also gave further insight into the culture broth evaporation and its particle sedimentation characteristics at 37degreesC. These features correlated with the periodic motions associated with the organism and may therefore provide a signature for the organism and a means of monitoring. These results aided the developemnt of imaging bacterial detection systems which were discussed in the second half of the work. The second experimental system focuses on quantifying the morphology and population dynamics of Euglena gracilis under ambient conditions through image processing. Unlike many other cell systems, Euglena cells change from round to long to round cell shape and these different cell shapes were analyzed over time. In the morphological studies of single Euglena cells, image processing tools and filtering techniques were used and different parameters identified and their efficiency at determining cell shape compared. The best parameter for processing the images and its effectiveness in detecting even the interior motions of constituents within a dead cell was found. The efficiency of the measurement parameters in following sequences of shape changes of the Euglena cell was compared with the visual assessment tests from 12 volunteers and other simple measurement methods including parameters relating to the cells eccentricity, and image processing in the space and frequency domains. One of the major advantages of this system is that living cells can be examined in their natural state without being killed, fixed, and stained. As a result, the dynamics of ongoing biological processes in live cells can be observed and recorded in high contrast and sharp clarity. The population statistics of Euglena gracilis was done in liquid culture. A custom built microscopy system was employed and the laser beam was coupled with a dark field illumination system to enhance the contrast of the images. Different image filters were employed for extracting useful information on the population statistics. Similarly as with the shape study of the Euglena cell, different parameters were identified and the best parameter was selected. The population study of the Euglena cells provided a detection system that indicated the activity of the population

Geospatial Methods for Mapping Domestic Waste Piles and Macro Plastics

There are growing concerns about the threats posed by plastics to human society and natural ecosystems. There is evidence of the harm presented to economies, public health and society. Although plastic pollution is an issue of great concern, low- and middle-income countries lack waste disposal services and this lead to disposal of waste including plastics into the environment. Monitoring presence of waste disposed into the environment is crucial for assessment of remedial measures . Traditional approach for identifying locations with plastic and waste accumulation in the environment involves field surveys, and drone technology is an emerging technology being applied for mapping the presence of plastics and waste in the environment. In this study, I have presented basic requirements for collecting data using Unmanned Aerial Vehicles (UAV) to map plastics and accumulation of domestic waste in the environment. For example, it was observed that a Ground Sampling Distance (GSD) of 2.51 cm is too coarse for mapping plastics of size less than 10 cm. Additionally, the study has also utilized random forest as a machine learning algorithm to classify and identify plastics and waste piles from UAV-derived imagery in a densely populated area of Blantyre, Malawi. The random forest predictions show high performance compared to prior studies for both waste piles (Precision: 0.9048, Recall: 0.95, and F-score: 0.9268) and plastics detection (Precision: 0.8905, Recall: 0.9421, and F-score: 0.9156). With the reported accuracies, UAV imagery can be employed to guide environmental policy implementation by helping in monitoring the effectiveness of policies that have been set to mitigate and address problems such as open waste dumping

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (‘foreign’) objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasive and widely applicable method for foreign object detection. Deep learning has recently emerged as a powerful approach for recognizing patterns in radiographs (i.e., X-ray images), enabling automated X-ray based foreign object detection. However, these methods require a large number of training examples and manual annotation of these examples is a subjective and laborious task. In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labor requirements. In our approach, a few representative objects are CT scanned and reconstructed in 3D. The radiographs that are acquired as part of the CT-scan data serve as input for the machine learning method. High-quality ground truth locations of the foreign objects are obtained through accurate 3D reconstructions and segmentations. Using these segmented volumes, corresponding 2D segmentations are obtained by creating virtual projections. We outline the benefits of objectively and reproducibly generating training data in this way. In addition, we show how the accuracy depends on the number of objects used for the CT reconstructions. The results show that in this workflow generally only a relatively small number of representative objects (i.e., fewer than 10) are needed to achieve adequate detection performance in an industrial setting

Current Developments of Analytical Methodologies for Aflatoxins’ Determination in Food during the Last Decade (2013–2022), with a Particular Focus on Nuts and Nut Products

This review aims to provide a clear overview of the most important analytical development in aflatoxins analysis during the last decade (2013-2022) with a particular focus on nuts and nuts-related products. Aflatoxins (AFs), a group of mycotoxins produced mainly by certain strains of the genus Aspergillus fungi, are known to impose a serious threat to human health. Indeed, AFs are considered carcinogenic to humans, group 1, by the International Agency for Research on Cancer (IARC). Since these toxins can be found in different food commodities, food control organizations worldwide impose maximum levels of AFs for commodities affected by this threat. Thus, they represent a cumbersome issue in terms of quality control, analytical result reliability, and economical losses. It is, therefore, mandatory for food industries to perform analysis on potentially contaminated commodities before the trade. A full perspective of the whole analytical workflow, considering each crucial step during AFs investigation, namely sampling, sample preparation, separation, and detection, will be presented to the reader, focusing on the main challenges related to the topic. A discussion will be primarily held regarding sample preparation methodologies such as partitioning, solid phase extraction (SPE), and immunoaffinity (IA) related methods. This will be followed by an overview of the leading analytical techniques for the detection of aflatoxins, in particular liquid chromatography (LC) coupled to a fluorescence detector (FLD) and/or mass spectrometry (MS). Moreover, the focus on the analytical procedure will not be specific only to traditional methodologies, such as LC, but also to new direct approaches based on imaging and the ability to detect AFs, reducing the need for sample preparation and separative techniques

MACHINE VISION TECHNOLOGY FOR FOOD QUALITY AND SAFETY INSPECTIONS

With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective determination of these characteristics in food products continues to grow. Machine vision as a non-destructive technology, provides an automated and economic way to accomplish these requirements. This research thus explored two applications of using machine vision techniques for food quality and safety inspections. The first application is using a combined X-ray and laser range imaging system to detect bone and other physical contaminants inside poultry meat. For this project, our research focuses on how to calibrate the imaging system. A unique three-step calibration method was developed and results showed that high accuracy has been achieved for the whole system calibration - a root mean square error of 0.20 mm, a standard deviation of 0.20 mm, and a maximum error of 0.48 mm. The second application is separating walnuts' shells and meat. A backlight imaging system was developed based on our finding that the backlit images of walnut shells and meat showed quite different texture patterns due to their different light transmittance properties. The texture patterns were characterized by several rotation invariant texture analysis methods. The uncorrelated and redundant features were further removed by a support vector machine (SVM) based recursive feature elimination method, with the SVM classifier trained concurrently for separations of walnuts' shells and meat. The experimental results showed that the proposed approach was very effective and could achieve an overall 99.2% separation accuracy. This high separation accuracy and low instrument cost make the proposed imaging system a great benefit to the walnut processing industry