Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study
Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sreekanth, R. [verfasserIn] Sampson, S. Abraham [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Optical and quantum electronics - Springer US, 1969, 56(2024), 10 vom: 28. Sept. |
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| Übergeordnetes Werk: |
volume:56 ; year:2024 ; number:10 ; day:28 ; month:09 |
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| DOI / URN: |
10.1007/s11082-024-07579-x |
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| Katalog-ID: |
SPR057511446 |
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| 520 | |a Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. | ||
| 650 | 4 | |a Cadmium sulfide |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ethanol adulteration |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sensitivity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SPR fiber optic sensors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Zinc sulfide |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sampson, S. Abraham |e verfasserin |4 aut | |
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10.1007/s11082-024-07579-x doi (DE-627)SPR057511446 (SPR)s11082-024-07579-x-e DE-627 ger DE-627 rakwb eng 500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Sreekanth, R. verfasserin aut Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 Sampson, S. Abraham verfasserin aut Enthalten in Optical and quantum electronics Springer US, 1969 56(2024), 10 vom: 28. Sept. (DE-627)312693869 (DE-600)2000642-1 1572-817X nnns volume:56 year:2024 number:10 day:28 month:09 https://dx.doi.org/10.1007/s11082-024-07579-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.38 VZ 33.18 VZ 33.23 VZ 53.54 VZ 52.88 VZ 33.72 VZ AR 56 2024 10 28 09 |
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10.1007/s11082-024-07579-x doi (DE-627)SPR057511446 (SPR)s11082-024-07579-x-e DE-627 ger DE-627 rakwb eng 500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Sreekanth, R. verfasserin aut Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 Sampson, S. Abraham verfasserin aut Enthalten in Optical and quantum electronics Springer US, 1969 56(2024), 10 vom: 28. Sept. (DE-627)312693869 (DE-600)2000642-1 1572-817X nnns volume:56 year:2024 number:10 day:28 month:09 https://dx.doi.org/10.1007/s11082-024-07579-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.38 VZ 33.18 VZ 33.23 VZ 53.54 VZ 52.88 VZ 33.72 VZ AR 56 2024 10 28 09 |
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10.1007/s11082-024-07579-x doi (DE-627)SPR057511446 (SPR)s11082-024-07579-x-e DE-627 ger DE-627 rakwb eng 500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Sreekanth, R. verfasserin aut Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 Sampson, S. Abraham verfasserin aut Enthalten in Optical and quantum electronics Springer US, 1969 56(2024), 10 vom: 28. Sept. (DE-627)312693869 (DE-600)2000642-1 1572-817X nnns volume:56 year:2024 number:10 day:28 month:09 https://dx.doi.org/10.1007/s11082-024-07579-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.38 VZ 33.18 VZ 33.23 VZ 53.54 VZ 52.88 VZ 33.72 VZ AR 56 2024 10 28 09 |
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10.1007/s11082-024-07579-x doi (DE-627)SPR057511446 (SPR)s11082-024-07579-x-e DE-627 ger DE-627 rakwb eng 500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Sreekanth, R. verfasserin aut Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 Sampson, S. Abraham verfasserin aut Enthalten in Optical and quantum electronics Springer US, 1969 56(2024), 10 vom: 28. Sept. (DE-627)312693869 (DE-600)2000642-1 1572-817X nnns volume:56 year:2024 number:10 day:28 month:09 https://dx.doi.org/10.1007/s11082-024-07579-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.38 VZ 33.18 VZ 33.23 VZ 53.54 VZ 52.88 VZ 33.72 VZ AR 56 2024 10 28 09 |
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10.1007/s11082-024-07579-x doi (DE-627)SPR057511446 (SPR)s11082-024-07579-x-e DE-627 ger DE-627 rakwb eng 500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Sreekanth, R. verfasserin aut Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 Sampson, S. Abraham verfasserin aut Enthalten in Optical and quantum electronics Springer US, 1969 56(2024), 10 vom: 28. Sept. (DE-627)312693869 (DE-600)2000642-1 1572-817X nnns volume:56 year:2024 number:10 day:28 month:09 https://dx.doi.org/10.1007/s11082-024-07579-x X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 33.38 VZ 33.18 VZ 33.23 VZ 53.54 VZ 52.88 VZ 33.72 VZ AR 56 2024 10 28 09 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cadmium sulfide</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ethanol adulteration</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitivity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SPR fiber optic sensors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Zinc sulfide</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sampson, S. 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| author |
Sreekanth, R. |
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Sreekanth, R. ddc 500 bkl 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 misc Cadmium sulfide misc Ethanol adulteration misc Machine learning misc Sensitivity misc SPR fiber optic sensors misc Zinc sulfide Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study |
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Sreekanth, R. |
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electronic Article |
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500 - Natural sciences & mathematics 620 - Engineering & allied operations |
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1572-817X |
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500 620 VZ 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 bkl Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study Cadmium sulfide (dpeaa)DE-He213 Ethanol adulteration (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Sensitivity (dpeaa)DE-He213 SPR fiber optic sensors (dpeaa)DE-He213 Zinc sulfide (dpeaa)DE-He213 |
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ddc 500 bkl 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 misc Cadmium sulfide misc Ethanol adulteration misc Machine learning misc Sensitivity misc SPR fiber optic sensors misc Zinc sulfide |
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ddc 500 bkl 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 misc Cadmium sulfide misc Ethanol adulteration misc Machine learning misc Sensitivity misc SPR fiber optic sensors misc Zinc sulfide |
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ddc 500 bkl 33.38 bkl 33.18 bkl 33.23 bkl 53.54 bkl 52.88 bkl 33.72 misc Cadmium sulfide misc Ethanol adulteration misc Machine learning misc Sensitivity misc SPR fiber optic sensors misc Zinc sulfide |
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500 - Science 620 - Engineering |
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metal–semiconductor sulfide bilayer based spr fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study |
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Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study |
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Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The study explores the use of surface plasmon resonance (SPR)-based fiber optic sensors, coated with alternating layers of metal and semiconductor sulfide materials, for detecting methanol adulteration of ethanol. Gold, silver, and copper are chosen as the metal layers for the excitation of SPR, with cadmium sulfide (CdS)/zinc sulfide (ZnS) as the semiconductor sulfide materials. Sensors with varying thicknesses of metal layers, i.e. 40 nm, 45 nm and 50 nm, are considered while the thickness of the material varies from 0nm (only the metal layer) to 3 nm in 0.5 nm increments. The sensor with a 40 nm gold layer and 3 nm CdS layer showed a maximum sensitivity of 1.47 nm/vol.% of methanol adulteration in ethanol. Apart from sensitivity, the figure of merit (FOM) and detection accuracy (DA) of all the sensors are computed and compared. Multiple machine learning approaches, such as logistic regression (LR), K-nearest neighbours (KNN), support vector machine (SVM), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), with the stratified K-fold cross-validation method of train/test split, are used for the detection of methanol adulteration of ethanol using the sensor outputs. The CV splits and the hyperparameters are tuned to achieve maximum performance for the models. DT achieves an accuracy of 91% of prediction with 87.1% of F1_score at max_depth=5 and CV=7, when trained with the outputs of all the sensors, and KNN shows 92% accuracy of prediction with 86.6% F1_Score at n_neighbours= 15 and CV=10 for the selected sensors. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.4007587 |