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Efficacy Testing of Food Industry Cl...

Wood, Elizabeth Alene.

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Efficacy Testing of Food Industry Cleaning Agents on the Removal of Engineered Nanoparticles from the Skins of Grape Tomatoes (Solanum lycopersicum) Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and Support Vector Machine (SVM) Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Efficacy Testing of Food Industry Cleaning Agents on the Removal of Engineered Nanoparticles from the Skins of Grape Tomatoes (Solanum lycopersicum) Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and Support Vector Machine (SVM) Learning./
作者:	Wood, Elizabeth Alene.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	189 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Food science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10786862
ISBN:	9780438104570

Efficacy Testing of Food Industry Cleaning Agents on the Removal of Engineered Nanoparticles from the Skins of Grape Tomatoes (Solanum lycopersicum) Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and Support Vector Machine (SVM) Learning.
Wood, Elizabeth Alene.

Efficacy Testing of Food Industry Cleaning Agents on the Removal of Engineered Nanoparticles from the Skins of Grape Tomatoes (Solanum lycopersicum) Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and Support Vector Machine (SVM) Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 189 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--Washington State University, 2018.

This study used regulatory-approved food industry cleaning agents to test removal efficiency of engineered nanoparticles from the surfaces of ready-to-eat produce. Grape tomatoes (Solanum lycopersicum) provided the food surface used for testing and titania, alumina, and silica were the engineered nanoparticles tested for removal. Twelve different industrial cleaning agents (nanopure water, peroxyacetic acid, hypochlorous acid, ammonium hydroxide/hydrogen peroxide mix, ethanol, hydrogen peroxide, peroxyacetic acid/hydrogen peroxide mix, sodium dodecylbenzene-sulfonate, potassium hydroxide, electrolyzed water, sodium chloride, and calcium chloride) were used to treat the tomato surfaces, with results being analyzed using Fourier Transform Infrared Spectroscopy and Support Vector Machine Learning algorithms. Method verification was performed using Inductively-Coupled Plasma Mass Spectrometry.

ISBN: 9780438104570Subjects--Topical Terms:

3173303
Food science.

Efficacy Testing of Food Industry Cleaning Agents on the Removal of Engineered Nanoparticles from the Skins of Grape Tomatoes (Solanum lycopersicum) Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FTIR) and Support Vector Machine (SVM) Learning.
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520 $a This study used regulatory-approved food industry cleaning agents to test removal efficiency of engineered nanoparticles from the surfaces of ready-to-eat produce. Grape tomatoes (Solanum lycopersicum) provided the food surface used for testing and titania, alumina, and silica were the engineered nanoparticles tested for removal. Twelve different industrial cleaning agents (nanopure water, peroxyacetic acid, hypochlorous acid, ammonium hydroxide/hydrogen peroxide mix, ethanol, hydrogen peroxide, peroxyacetic acid/hydrogen peroxide mix, sodium dodecylbenzene-sulfonate, potassium hydroxide, electrolyzed water, sodium chloride, and calcium chloride) were used to treat the tomato surfaces, with results being analyzed using Fourier Transform Infrared Spectroscopy and Support Vector Machine Learning algorithms. Method verification was performed using Inductively-Coupled Plasma Mass Spectrometry.
520 $a The results indicate that the combination of Fourier Transform Infrared Spectroscopy and Support Vector Machine Learning algorithms has a strong potential for use in industry as an effective means to predict and monitor nanoparticle removal from food surfaces. Two possible spectral detection mechanisms are proposed based on these findings. It was also discovered in this study that electrostatic and intermolecular forces between the nanoparticles, the food surface, and the cleaning agent used have the strongest influence on the removal success rates. This study proposes two possible mechanisms for these three competing interactions and their role in removal.
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