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Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species

Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach to identifying species is visual examination by human analysts; this method is rather subjective and time-consuming. Furthermore, confident identification requires extensive experience and training. To aid this inspection process, we have developed in collaboration with FDA analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated pan, zoom, and color analysis capabilities. After species analysis, the results panel allows the user to compare the analyzed images with referenced images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface. Additional issues to address include standardization of image layout, extension of the feature-extraction algorithm, and utilizing image classification to build a central search engine for widespread usage.
ContributorsMartin, Daniel Luis (Author) / Ahn, Gail-Joon (Thesis director) / Doupé, Adam (Committee member) / Xu, Joshua (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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This is a test plan document for Team Aegis' capstone project that has the goal of mitigating single event upsets in NAND flash memory caused by space radiation.

ContributorsForman, Oliver Ethan (Co-author) / Smith, Aiden (Co-author) / Salls, Demetra (Co-author) / Kozicki, Michael (Thesis director) / Hodge, Chris (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the

The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.
ContributorsMin, Eleanor (Author) / Manfredo, Mark (Thesis director) / Kozicki, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor)
Created2022-05
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Description

The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the

The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.

ContributorsMin, Eleanor (Author) / Manfredo, Mark (Thesis director) / Kozicki, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2022-05
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Description

The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the

The purpose of this research is to better understand the potential use environment of a Dendritic Identifier within the current leafy green supply chain, including the exploration of potential costs of implementation as well as non-economic costs. This information was collected through an extensive review of literature and through the engagement in in-depth interviews with professionals that work in the growing, distribution, and processing of leafy greens. Food safety in the leafy green industry is growing in importance in the wake of costly outbreaks that resulted and recalls and lasting market damage. The Dendritic Identifier provides a unique identification tag that is unclonable, scannable, and compatible with blockchain systems. It is a digital trigger that can be implemented throughout the commercial leafy green supply chain to increase visibility from farm to fork for the consumer and a traceability system for government agencies to trace outbreaks. Efforts like the Food Safety Modernization Act, the Leafy Green Marketing Agreement, and other certifications aim at establishing science-based standards regarding soil testing, water, animal feces, imports, and more. The leafy green supply chains are fragmented in terms of tagging methods and data management services used. There are obstacles in implementing Dendritic Identifiers in that all parties must have systems capable of joining blockchain networks. While there is still a lot to take into consideration for implementation, solutions like the IBM Food Trust pose options for a more fluid transfer of information. Dendritic Identifiers beat out competing tagging technologies in that they work with cellphones, are low cost, and are blockchain compatible. Growers and processors are excited by the opportunity to showcase their extensive food safety measures. The next step in understanding the use environment is to focus on the retail distribution and the retailer specifically.

ContributorsMin, Eleanor (Author) / Manfredo, Mark (Thesis director) / Kozicki, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2022-05