High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

Abstract

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 Pb 2+ 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 Pb 2+ 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 Pb 2+ 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 Pb 2+ in the concentration range of 0.5–2000 ppb with a detection limit of 0.5 ppb.

Industrial Buyer Innovation Adoption Model: A Focus on a Smartphone-Based Electrochemical Analytical Device for Toxic Heavy Metal Detection

“…For example, Li et al [15] successfully utilized SEAD to analyze iron, nickel, chromium, copper and zinc in Hubin Lake. Moreover, SEADs were demonstrated to quantify mercury in Pearl river samples (Guangzhou, China) [16] and lead in Iranian water sources [17]. Their SEAD detection ranges achieved US EPA and WHO permissible safety standards for contaminants in water.…”

New Smartphone based Colorimetric Method Development and validation for the Drugs containing Nitrogen, Sulphur and Phosphorus in Bulk and Tablet Dosage Form

“…[89] CODEN (USA): JDDTAO visual data, which is then translated into RGB histograms (red, green and blue). [23][24][25] The RGB colour model is based on the colour perception theory, which states that the human eye has different sensitivity peaks located around red, green, and blue. Multivariate analysis could be employed in this software to increase the RGB colour system applicability of colorimetry.…”

Diethylenetriaminepentaacetic Acid-Functionalized Gold Nanoparticles for the Detection of Toxic Chromium Assisted by a Machine-Learning Approach

“…37 A similar technique was also used to detect Pb II using oligonucleotide-modified GNP and linear regression on the mobile platform. 30 They used complex mechanical hardware to eliminate variation in the illumination of the acquired images. Although these groups have used a regression-based learning model to detect lead and mercury, the system needs an optoelectronic apparatus to maintain the similar picture quality, suggesting a lack of robustness in the detection system.…”

“…Recently, the high-definition camera of smartphones has offered various innovative ideas for detecting environmental toxins such as chromium. The smartphone-based colorimetric sensing has demonstrated its potential as a lab-on-phone device which is cost-effective, portable, and highly accurate. − The detection techniques introduced so far rely on RGB (red, green, and blue) intensities of the sensor color. For instance, Chen et al developed a smartphone-integrated colorimetric sensor using meso-2,3-dimercaptosuccinic acid (DMSA)-functionalized GNP to detect both the oxidation states of chromium ions (III and VI).…”

“…In the field of color sensing, ML is employed as a tool for data processing to extract features such as color intensity and utilize these features to predict the concentration of analytes and toxic ions directly. , Sajed et al reported a detection method for Hg II using a linear regression approach on a smartphone based on the color change of aptamer-modified GNP . A similar technique was also used to detect Pb II using oligonucleotide-modified GNP and linear regression on the mobile platform . They used complex mechanical hardware to eliminate variation in the illumination of the acquired images.…”