Smartphone-based spectrometer with high spectral accuracy for mHealth application

Ding, Hui; Chen, Chen; Qi, Sheng; Han, Chunyang; Chunxia, Yue

doi:10.1016/j.sna.2018.03.008

Cited by 38 publications

(18 citation statements)

References 20 publications

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“…The use of fluorescence-based methods for toxicant detection can avoid many of the practical challenges associated with mass spectral detection [15]. In particular, fluorescence-based detection in complex environments can occur using a hand-held device while maintaining high sensitivity [16], and can also be performed rapidly to enable high-throughput sample screening [17]. Fluorescence-based detection methods have been reported for a broad variety of analytes using a range of fluorescent sensors, including explosive detection via fluorescent conjugated polymers [18] and nanoparticles [19], anion detection via supramolecular fluorescent sensors [20], and cation detection via interactions of cationic analytes with small-molecule, high-quantum-yield fluorophores [21].…”

Section: Of 16mentioning

confidence: 99%

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

et al. 2019

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Reported herein is the sensitive and selective cyclodextrin-promoted fluorescence detection of benzene, toluene, ethylbenzene, xylene, and cumene (BTEXC) fuel components in contaminated snow samples collected from several locations in the state of Rhode Island. This detection method uses cyclodextrin as a supramolecular scaffold to promote analyte-specific, proximity-induced fluorescence modulation of a high-quantum-yield fluorophore, which leads to unique fluorescence responses for each cyclodextrin-analyte-fluorophore combination investigated and enables unique pattern identifiers for each analyte using linear discriminant analysis (LDA). This detection method operates with high levels of sensitivity (sub-micromolar detection limits), selectivity (100% differentiation between structurally similar compounds, such as ortho-, meta-, and para-xylene isomers), and broad applicability (for different snow samples with varying chemical composition, pH, and electrical conductivity). The high selectivity, sensitivity, and broad applicability of this method indicate significant potential in the development of practical detection devices for aromatic toxicants in complex environments.

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Section: Of 16mentioning

confidence: 99%

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

et al. 2019

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“…The use of fluorescence-based methods for toxicant detection can avoid many of the practical challenges associated with mass spectral detection [15]. In particular, fluorescence-based detection in complex environments can occur using a hand-held device while maintaining high sensitivity [16], and can also be performed rapidly to enable high-throughput sample screening [17]. Fluorescence-based detection methods have been reported for a broad variety of analytes using a range of fluorescent sensors, including explosive detection via fluorescent conjugated polymers [18] and nanoparticles [19], anion detection via supramolecular fluorescent sensors [20], and cation detection via www.videleaf.com interactions of cationic analytes with small-molecule, highquantum-yield fluorophores [21].…”

Section: Introductionmentioning

confidence: 99%

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

DiScenza¹,

Intravaia²,

Healy³

et al. 2020

Prime Archives in Sensors

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“…For mobile health applications, Ding et al (2018) developed a highly accurate smartphone spectrophotometer that analysed absorption of light in the visible part of the electromagnetic spectrum for the detection of creatinine, an excess of which can indicate kidney problems. They improved the accuracy of the smartphone camera's CMOS sensor (the semiconductor that converts light to electronic signals, similar to the human retina) for spectroscopy by creating a spectral intensity correction algorithm.…”

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confidence: 99%

“…Wilkes et al (2017) developed an inexpensive smartphone spectrophotometer specifically for the analysis of sulphur dioxide at 310 nm. Unlike Hossain et al (2016) and Ding et al (2018), they used an external camera rather than the phone's internal camera. They modified the Raspberry Pi microcomputer's external camera to enable it to detect UV light at a wavelength of 310 nm.…”

mentioning

confidence: 99%

“…They modified the Raspberry Pi microcomputer's external camera to enable it to detect UV light at a wavelength of 310 nm. While the devices developed by Hossain et al (2016) and Ding et al (2018) required additional optical components, Wilkes et al 's device needed only a diffraction grating. Their device was able to measure sulphur dioxide at concentration levels similar to those detected by costly counterparts, and they reported that the spectral detection resolution was 1nm.…”

mentioning

confidence: 99%

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Water quality assessment using a portable UV optical absorbance nitrate sensor with a scintillator and smartphone camera

Ingles

Louw

Booysen

2021

WSA

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Nitrate contamination of water sources is a global environmental concern. A major source of pollution is agricultural runoff, which can contain decomposed organic matter, fertilizer, and animal or human waste. Nitrate adversely affects the stability of water systems such as dams and rivers and thus also public health. Regulation is essential but difficult to implement, given that measuring nitrates is laborious, and normally done using chemical assays in laboratories. We present a novel portable nitrate sensor that uses a smartphone camera fitted with low-cost optics. The sensor uses ultraviolet absorbance analysis to detect nitrates in water samples and quantify the concentration. The sensor’s absorptivity when a bandpass filter was used was 0.0681 L∙mg−1∙cm−1 compared to 0.0934 L∙mg−1∙cm−1 measured with a spectrophotometer in a laboratory. Measurements by the sensor of the concentration of nitrates in two environmental samples differed from those taken by the spectrophotometer by 19% and 7%. The sensor achieved a nitrate concentration measurement resolution of 0.2 mg∙L−1, and a detection range of 0–5 mg∙L−1, with higher concentrations requiring dilution to quantify. Our tests showed that the smartphone-based nitrate sensor is sufficiently accurate to be used as an inexpensive instrument for nitrate analysis in the field.

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Smartphone-based spectrometer with high spectral accuracy for mHealth application

Cited by 38 publications

References 20 publications

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

Fluorescence-Based Detection of Benzene, Toluene, Ethylbenzene, Xylene, and Cumene (BTEXC) Compounds in Fuel-Contaminated Snow Environments

Water quality assessment using a portable UV optical absorbance nitrate sensor with a scintillator and smartphone camera

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