LDR | | 02018nmm uu200433 4500 |
001 | | 000000332465 |
005 | | 20240805170835 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438104570 |
035 | |
▼a (MiAaPQ)AAI10786862 |
035 | |
▼a (MiAaPQ)wsu:12377 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 641 |
100 | 1 |
▼a Wood, Elizabeth Alene. |
245 | 10 |
▼a 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 |
260 | |
▼a [S.l.] :
▼b Washington State University.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 189 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
500 | |
▼a Adviser: Barbara A. Rasco. |
502 | 1 |
▼a Thesis (Ph.D.)--Washington State University, 2018. |
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 |
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 |
590 | |
▼a School code: 0251. |
650 | 4 |
▼a Food science. |
650 | 4 |
▼a Nanotechnology. |
650 | 4 |
▼a Agriculture. |
690 | |
▼a 0359 |
690 | |
▼a 0652 |
690 | |
▼a 0473 |
710 | 20 |
▼a Washington State University.
▼b Food Science. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0251 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997371
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |