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

Sajed, Samira; Kolahdouz, Mohammadreza; Sadeghi, Maryam; Razavi, Seyedeh Fatemeh

doi:10.1021/acsomega.0c04255

Cited by 40 publications

(22 citation statements)

References 52 publications

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“…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.…”

Section: Theoretical Background 21 Smartphone-based Electrochemical Analytical Devices For Heavy Metal Detection (Sead)mentioning

confidence: 90%

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

et al. 2021

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Smartphone-Based Electrochemical Analytical Devices (SEAD) enable sophisticated toxic heavy metal quantification experiments to be conducted anywhere with high precision, selectivity, and sensitivity. However, a very limited number of such technologies are able to make the transition from the lab to the competitive B2B marketplace. The purpose of this paper is to examine the factors influencing SEAD adoption in the manufacturing industry using the Industrial Buyer Innovation Adoption (IBIA) model, which integrates B2B consumer behavior theories and technology acceptance models from the past 50 years. Analysis of data collected from 400 corporations in Thailand revealed that sellers, buyers, internal organization and invented technology advantage variables predicted industrial innovation adoption based on binary logistic regression. This research contributes to the understanding of SEAD transition from scientific knowledge to sustainable technology and diffusible innovation. The ambitious IBIA framework is potentially an alternative managerial tool for holistically assessing and creating adoptable innovative business models for new technologies.

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Section: Theoretical Background 21 Smartphone-based Electrochemical Analytical Devices For Heavy Metal Detection (Sead)mentioning

confidence: 90%

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

et al. 2021

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“…[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.…”

Section: Introduction To Colorimetric Analysis Based On Smartphone Ap...mentioning

confidence: 99%

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

Choudhary

Choksi

Mashru

2022

J. Drug Delivery Ther.

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A method for determining the concentration of coloured compounds in a solution is colorimetry. The intensity of the colour is related to the chemical concentration being measured. Because of their low cost and ability to collect, store, and interpret data all in one device, smartphone-based colorimetry has increased in popularity as an analytical tool. The camera on the phone is used as a detector in smartphone colorimetry. Both the smartphone colorimetric method and the UV method relied on the detection of colour intensity as concentration rose. Distinct oxidation states of ammonium metavanadate generate different colours depending on the oxidation state. The +5-oxidation state appears yellow, the +4-oxidation state appears blue, the +3-oxidation state appears green, and the +2-oxidation state appears purple. The ammonium metavanadate reagent is orange red in colour, but when it combines with pharmaceuticals that contain nitrogen, phosphorus, or sulphur in their structure, it turns green. The developed approach for all of the drugs in this article is linear. The colour intensity increases as the concentration of API increases. All of the photos were captured on a smartphone and analysed with photometrix PRO software. The photometrix PRO application turns an image to an RGB histogram, and it also includes regression models. The percent RSD for all three drugs was less than 2 employing Photometrix PRO and UV method. Using a statistical method called a two-paired test, the results reveal that both procedures are equally significant for all three drugs. Keywords: Uv spectrophotometry, Photometrix PRO, RGB Histogram, Sumatriptan Succinate, Gemifloxacin, Tenofovir disoproxil fumarate

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“…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.…”

Section: ■ Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Priyadarshni

Dutta

Chanda

2021

ACS Appl. Nano Mater.

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We present a machine-learning approach in developing a smartphone-based rapid and point-of-use (PoU) trace-level detection of chromium (III and VI) ions using a nanosensor comprising dithiolated diethylenetriaminepentaacetic acid-functionalized gold nanoparticles (DTDTPA-GNP). The bright red color of DTDTPA-GNP turned blue with Cr III/VI ions in an aqueous medium. The nanosensor exhibited good linearity for Cr III/VI detection in a concentration range of 0.001−10 ppm with a detection limit of 0.001 ppm. A smartphone was used to acquire images of the distinct color change due to the DTDTPA-GNP−Cr III/VI interactions. Two features, that is, saturation and chroma component of red color difference were extracted from the color information of the acquired images. The extracted features from training samples were utilized to build a support vector machine-based regression (SVR) model to predict the accurate concentration of chromium (III and VI) in water and an aqueous extract of ferrochrome industrial waste (collected from the alloy industry as waste slag). The accurate prediction of 0.027 ppm Cr III/VI concentration in the industrial waste sample with a 100% recovery rate confirms the colorimetric-based SVR model as an efficient sensing platform for Cr III/VI ion detection. The mean squared error for Cr III/VI prediction was 0.00039 with 404 support vectors and a bias value of 0.1473. This highly selective and sensitive smartphone-based approach thus established the use of DTDTPA-GNP for PoU detection of Cr III/VI , specifically in day-today monitoring of water quality.

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

Cited by 40 publications

References 52 publications

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

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

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

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

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