TY - JOUR
T1 - High-performance estimation of lead ion concentration using smartphone-based colorimetric analysis and a machine learning approach
AU - Sajed, Samira
AU - Kolahdouz, Mohammadreza
AU - Sadeghi, Mohammad Amin
AU - Razavi, Seyedeh Fatemeh
N1 - Publisher Copyright:
© 2020 American Chemical Society. All rights reserved.
PY - 2020/10/27
Y1 - 2020/10/27
N2 - Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb2+ in the concentration range of 0.5−2000 ppb with a detection limit of 0.5 ppb.
AB - Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb2+ in the concentration range of 0.5−2000 ppb with a detection limit of 0.5 ppb.
UR - http://www.scopus.com/inward/record.url?scp=85096133699&partnerID=8YFLogxK
U2 - 10.1021/acsomega.0c04255
DO - 10.1021/acsomega.0c04255
M3 - Article
AN - SCOPUS:85096133699
SN - 2470-1343
VL - 5
SP - 27675
EP - 27684
JO - ACS Omega
JF - ACS Omega
IS - 42
ER -