A mobile laboratory for rapid on-site analysis of catechols from water samples with real-time results production. Issue 84 (26th August 2016)

• Record Type: Journal Article
• Authors: Wang, Yun; Li, Yuanyuan; Liu, Yan; Han, Juan; Xia, Jinchen; Bao, Xu; Ni, Liang; Tang, Xu
• Is Part Of: RSC advances. Volume 6:Issue 84(2016)
• Page Start: 80885
• Page End: 80895
• Publication Date: 2016-08-26
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c6ra12052k
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : A smartphone was coupled with a remote server to analyze catechols from water samples on-site with real-time results production. Abstract : The search for a practical method to analyze cis-diol-containing compounds outside laboratory settings remains a substantially scientific challenge. Herein, we used a "mobile laboratory", wherein a filter paper-based colorimetric sensor array, a smartphone, and a remote server were combined together, for rapid on-site analysis of catechols from water samples with real-time results production. A smallest-scale filter paper-based 2 × 2 colorimetric sensor array composed of pH indicators and phenylboronic acid was configured. The array was able to distinguish 7 water-soluble catechols at 7 serial concentrations, through simultaneous treatment via principal component analysis, hierarchical cluster analysis, and linear discriminant analysis. After both the discriminatory power of the array and the prediction ability of the partial least squares quantitative models were proven to be predominant, the smartphone was coupled to the remote server. All the ΔRGB data were uploaded to the remote server wherein linear discriminant analysis and partial least squares processing modules were established to provide qualitative discrimination and quantitative calculation, respectively, of the analytes in real time. The applicability of this novel method to a real-life scenario was confirmed by the on-site analysis of various catechols from a water sample of the Yangtze River; the feedback result in the smartphone showed that the method was able to identify catechols with 100% accuracy and predict the concentrations to within 0.484–4.08 standard deviation.