Skip to Main content Skip to Navigation
Journal articles

Reliable chiral recognition with an optoelectronic nose

Abstract : Chiral discrimination is a key problem in analytical chemistry. It is generally performed using expensive instruments or highly-specific miniaturized sensors. An electronic nose is a bio-inspired instrument capable after training of discriminating a wide variety of analytes. However, generality is achieved at the cost of specificity which makes chiral recognition a challenging task for this kind of device. Recently, a peptide-based optoelectronic nose which can board up to hundreds of different sensing materials has shown promising results, especially in terms of specificity. In line with these results, we describe here its use for chiral recognition. This challenging task requires care, especially in terms of statistical and experimental bias. For these reasons , we set up an automatic gas sampling system and recorded data over two long sessions, taking care to exclude possible confounds. Two couples of chiral molecules, namely (R) and (S) limonene and (R) and (S) carvone, were tested and several statistical analyses indicate the almost perfect discrimination of their two enantiomers. A method to highlight discriminative sensing materials is also proposed and shows that successful discrimination is likely achieved using just a few peptides.
Complete list of metadatas

Cited literature [32 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02534216
Contributor : Pierre Maho <>
Submitted on : Thursday, April 9, 2020 - 5:03:09 PM
Last modification on : Friday, July 3, 2020 - 4:52:28 PM

File

Maho_2020_Reliable_chiral_reco...
Files produced by the author(s)

Identifiers

Collections

Citation

Pierre Maho, Cyril Herrier, Thierry Livache, Guillaume Rolland, Pierre Comon, et al.. Reliable chiral recognition with an optoelectronic nose. Biosensors and Bioelectronics, Elsevier, 2020, ⟨10.1016/j.bios.2020.112183⟩. ⟨hal-02534216v2⟩

Share

Metrics

Record views

36

Files downloads

52