Team:UC Davis

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<p class="centertext"><a href="https://2014.igem.org/Team:UC_Davis/Project_Overview">
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Project Overview</a>
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Project Description
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<a href="https://2014.igem.org/Team:UC_Davis/Protein_Engineering"><div class="ProEng"></div></a/>
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More than 65% of imported extra virgin olive oil is defective due to poor handling or deliberate adulteration with extraneous, non-beneficial oils. The most prevalent and identifying defect in olive oil is rancidity, indicating the absence of expected health benefits such as antioxidants and polyunsaturated fats. This summer, we are assembling a biosensor capable of quickly and cheaply evaluating rancidity defects in the chemical profile of olive oil, providing both consumers and retailers with a means of ensuring product quality.
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OliView: An Enzyme Based Electrochemical Biosensor Developed for Olive Oil Quality Control
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Current methods for detecting defects in olive oil are complicated and time-consuming. The current gold standard, GC-MS, is slow and expensive. In addition, most biosensors are limited to detecting one analyte at a time, restricting the scope of their utility in food science. Our team is developing a biosensor which couples enzyme engineering with fundamental principles of electrochemistry to engineer an instrument capable of identifying multiple analytes in a sample and their corresponding concentrations. We have identified straight-chain, aliphatic aldehydes of varying chain lengths as indicators of rancidity in olive oil. Just as a glucose meter utilizes enzymes to quantify the amount of glucose in a sample, we are using the same principles to quantify the concentration of different aldehyde species in olive oil. Where a glucose meter utilizes one type of enzyme to identify one analyte, we are engineering a panel of enzymes to identify a myriad of aldehyde species. In separate reactions, each enzyme will have a narrow substrate specificity profile, only reacting with aldehydes of a specific chain length to produce a detectable byproduct. The byproduct created by each enzyme may be effectively quantified through our electrochemical platform. By coupling and multiplexing these enzymes with several electrochemical cells, our electrochemical biosensor will simultaneously identify and quantify multiple indicators of rancidity in olive oil.
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In a report conducted by the UC Davis Olive Oil Center, it was found that more than 65% of the extra virgin olive oil on shelves around the US is defective due to poor handling or deliberate adulteration with extraneous, non-beneficial oils. The most prevalent and identifying defect in olive oil is rancidity, indicating the absence of expected health benefits such as antioxidants and polyunsaturated fats. This summer, we engineered a biosensor capable of quickly and cheaply evaluating rancidity defects in the chemical profile of olive oil, providing both consumers and retailers with a means of ensuring product quality.<br><br>
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The project consisted of four components: Protein Engineering, Electrochemistry, Development of a user friendly potentiostat and signal processing. We were able to provide proof of concept for each component  of our device. With each component combined, we had a fully functional electrochemical biosensor that could distinguish rancid olive oil from fresh. <br><br>
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Our modular electrochemical biosensor may identify the chemical profile of compounds in a sample of olive oil. This will provide a rapid, accurate assessment of oil quality, allowing consumers and retailers to cheaply and effectively distinguish between healthy and defective olive oil. Producers, packagers, and retailers may pre-screen oil with our device to confirm oil quality. Given the lack of certified quality control entities in the U.S., the development of a widely accessible device will enable the olive oil industry to effectively regulate, monitor, and safeguard its reputation.
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<a href="https://static.igem.org/mediawiki/2014/0/07/UC_Davis_iGEM_2014_Practical_Implications_for_the_Development_and_Deployment_of_Engineered_Biosensors_in_Olive_Oil_Production.pdf" class="brightlink">Read full version of our practice and policy report</a><br>
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          <a href="https://2014.igem.org/Team:UC_Davis/Protein_Engineering">
 
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          <a href="https://2014.igem.org/Team:UC_Davis/Protein_Engineering"><h3>Protein Engineering</h3></a>
 
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          <p>Learn about how we engineered substrate specificity of Aldehyde Dehydrogenases and how we characterized specificity profiles.
 
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          <h3>Electrochemistry</h3>
 
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          <p>Take a look at how we developed a robust, coupleable electrochemical system to enable detection of various aldehydes in solution.         
 
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          <p>Take a look at the potentiostat and corresponding software we developed to acquire and process our data.
 
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          <h3>Results</h3>
 
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          <p>Here's the criteria that we met for this year's team.
 
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Latest revision as of 05:45, 24 November 2014

UC Davis iGEM 2014

Project Overview

OliView: An Enzyme Based Electrochemical Biosensor Developed for Olive Oil Quality Control

In a report conducted by the UC Davis Olive Oil Center, it was found that more than 65% of the extra virgin olive oil on shelves around the US is defective due to poor handling or deliberate adulteration with extraneous, non-beneficial oils. The most prevalent and identifying defect in olive oil is rancidity, indicating the absence of expected health benefits such as antioxidants and polyunsaturated fats. This summer, we engineered a biosensor capable of quickly and cheaply evaluating rancidity defects in the chemical profile of olive oil, providing both consumers and retailers with a means of ensuring product quality.

The project consisted of four components: Protein Engineering, Electrochemistry, Development of a user friendly potentiostat and signal processing. We were able to provide proof of concept for each component of our device. With each component combined, we had a fully functional electrochemical biosensor that could distinguish rancid olive oil from fresh.

Read full version of our practice and policy report

Retrieved from "http://2014.igem.org/Team:UC_Davis"