An enzyme response-regulated colorimetric assay for pattern recognition sensing application using biomimetic inorganic-protein hybrid nanoflowers

Numerous detection strategies have been undertaken for quantitative analysis of single metal ion in the field of water-quality monitoring, whereas the complexity in the types and concentrations of potential contaminant leads us to should focus on groups of contaminants than to individual contaminants. To the best of our knowledge, there are few strategies to explore cross response-based semiselective sensor array in the area of water-quality monitoring. In this study, a colorimetric sensor array based on enzyme response analysis was innovatively developed for pattern recognition of various metal ions. Three types of metal phosphates-acetylcholinesterase nanoflowers (MP-AChE NFs) were prepared by using a green, facile, cost-efficient enzyme immobilization technology to construct the sensor array. With the help of a multivariate statistical analysis that can concentrate the most significant characteristics (variance) of the data into a lower dimensional space, principal component analysis (PCA) successfully identifies and distinguishes 11 species of metal ions, and gives unique fingerprint information for each analyte. Moreover, the sensor array can distinguish different concentrations of single model analyte, as well as a mixture of different contaminants in tap water.

Automated summary

A colorimetric sensor array based on enzyme response analysis was developed for pattern recognition of various metal ions in water quality monitoring. The sensor array successfully identifies and distinguishes 11 species of metal ions using principal component analysis (PCA), and can also distinguish different concentrations of single model analyte and mixtures of contaminants in tap water.