Discrimination of Flavonoids by a Hypothesis Free Sensor Array
Source abstract
Abstract
Flavonoids are the most abundant polyphenols in the human diet and are ascribed a potentially beneficial effect against cancers, diabetes, and oxidative stress. The broad range of bioactivities and number of different flavonoids in a multitude of commercial products dictate a need to discriminate among different flavonoids as a means of quality control and testing for commercial flavonoid tablets, capsules, powders, etc. A sensor array has been constructed and optimized using five different fluorescent polymers, including poly(para-phenyleneethynylene)s (PPE) and poly(tetraphenylethene)s (PTPEs), employed at different pH values (4, 7, and 10). The selected six element array discriminates 11 different sample flavonoids from six different subgroups with 100% accuracy. Six commercial nutrition products were also successfully investigated to demonstrate the practical application of the sensor array. This array allows to inspect the quality of nutrition products with respect to content and identity.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies · Analytical Chemistry and Chromatography · Biochemical Analysis and Sensing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Organisch-Chemisches Institut, Ruprecht-Karls-Universitat Heidelberg, Im Neuenheimer Feld 270, 69120 Heidelberg, Germany
CAM, Centre for Advanced Materials, Ruprecht-Karls-Universitat Heidelberg, Im Neuenheimer Feld 225, 69120 Heidelberg, Germany
Funding
China Scholarship Council
References (34)
Show source-provided references
1. 10.1016/0140-6736(92)91277-F10.1016/0140-6736(92)91277-F ↗
2. 10.1080/10610278.2012.69149910.1080/10610278.2012.691499 ↗
3. 10.1039/C0SC00487A10.1039/C0SC00487A ↗
4. 10.1017/jns.2016.4110.1017/jns.2016.41 ↗
5. 10.1016/j.nutres.2004.07.00510.1016/j.nutres.2004.07.005 ↗
6. 10.1016/S0031-9422(00)00235-110.1016/S0031-9422(00)00235-1 ↗
7. 10.1016/S0308-8146(03)00272-310.1016/S0308-8146(03)00272-3 ↗
8. 10.5897/JMPR11.140410.5897/JMPR11.1404 ↗
9. 10.1159/00039940710.1159/000399407 ↗
10. 10.4314/tjpr.v7i3.1469310.4314/tjpr.v7i3.14693 ↗
11. 10.1007/s11130-006-0014-810.1007/s11130-006-0014-8 ↗
12. Middleton E. · Pharmacol. Rev. · 2000 · 52 · 673
13. 10.1111/j.1468-2494.2010.00522.x10.1111/j.1468-2494.2010.00522.x ↗
14. 10.3390/cosmetics203025910.3390/cosmetics2030259 ↗
15. 10.1002/jssc.20150128710.1002/jssc.201501287 ↗
16. 10.1002/elan.20150044910.1002/elan.201500449 ↗
17. 10.1016/j.talanta.2017.08.10210.1016/j.talanta.2017.08.102 ↗
18. 10.1039/C7AY01480E10.1039/C7AY01480E ↗
19. 10.1039/B505518K10.1039/B505518K ↗
20. 10.1002/1521-3773(20010903)40:17<3118::AID-ANIE3118>3.0.CO;2-Y10.1002/1521-3773(20010903)40:17<3118::AID-ANIE3118>3.0.CO;2-Y ↗
21. 10.1039/c3cs60179j10.1039/c3cs60179j ↗
22. 10.1016/j.chempr.2017.04.00810.1016/j.chempr.2017.04.008 ↗
23. 10.1002/anie.20170610110.1002/anie.201706101 ↗
24. 10.1021/acsami.6b0646210.1021/acsami.6b06462 ↗
25. 10.1002/anie.20160238510.1002/anie.201602385 ↗
26. 10.1021/acssensors.7b0094310.1021/acssensors.7b00943 ↗
27. 10.1021/acssensors.8b0037110.1021/acssensors.8b00371 ↗
28. 10.1021/acsami.6b1241910.1021/acsami.6b12419 ↗
29. 10.1038/nnano.2007.9910.1038/nnano.2007.99 ↗
30. 10.1002/anie.20090692810.1002/anie.200906928 ↗
31. 10.1038/3502102810.1038/35021028 ↗
32. 10.1371/journal.pone.014345410.1371/journal.pone.0143454 ↗
33. 10.1002/marc.20180077410.1002/marc.201800774 ↗
34. 10.1039/C4CS00267A10.1039/C4CS00267A ↗