Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation
In spite of the wide use of Raman spectroscopy for chemical analysis in different fields, not any automated identification of Raman spectra is universally adopted. However, the interest in this field is witnessed by the large number of papers published in the last decades. The problem of Raman-spect...
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MDPI AG
2022-07-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/10/8/295 |
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| author | Federico Angelini Simone Santoro Francesco Colao |
| author_facet | Federico Angelini Simone Santoro Francesco Colao |
| author_sort | Federico Angelini |
| collection | DOAJ |
| description | In spite of the wide use of Raman spectroscopy for chemical analysis in different fields, not any automated identification of Raman spectra is universally adopted. However, the interest in this field is witnessed by the large number of papers published in the last decades. The problem of Raman-spectra classification becomes particularly challenging when low irradiation is requested, either for safety reasons or to avoid target photodegradation. This often leads to spectra characterized by a low signal-to-noise ratio, where methods based on correlation usually fail. For this reason, a method based on peak identification through FMFs is presented, discussed and validated over a large set of samples. In particular, a Monte Carlo simulation has been employed to determine the best parameters of the fuzzy membership functions based on the analysis of performances of the classification procedure. The ROC curves have been analyzed, and AUC and best accuracy are employed as key parameters to evaluate the classification performances on different amounts of ammonium nitrate (from 300 to 1500 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>g) and different laser exposure levels (from 3.1 to 250 mJ/cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>). |
| first_indexed | 2024-03-09T09:59:43Z |
| format | Article |
| id | doaj.art-99a35c40358d4331ba2e4638e19e5ff9 |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T09:59:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
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| series | Chemosensors |
| spelling | doaj.art-99a35c40358d4331ba2e4638e19e5ff92023-12-01T23:33:52ZengMDPI AGChemosensors2227-90402022-07-0110829510.3390/chemosensors10080295Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo SimulationFederico Angelini0Simone Santoro1Francesco Colao2Diagnostic and Metrology Laboratory, Department of Fusion and Technology for Nuclear Safety and Security, ENEA via Enrico Fermi 45, 00044 Frascati, ItalyDiagnostic and Metrology Laboratory, Department of Fusion and Technology for Nuclear Safety and Security, ENEA via Enrico Fermi 45, 00044 Frascati, ItalyDiagnostic and Metrology Laboratory, Department of Fusion and Technology for Nuclear Safety and Security, ENEA via Enrico Fermi 45, 00044 Frascati, ItalyIn spite of the wide use of Raman spectroscopy for chemical analysis in different fields, not any automated identification of Raman spectra is universally adopted. However, the interest in this field is witnessed by the large number of papers published in the last decades. The problem of Raman-spectra classification becomes particularly challenging when low irradiation is requested, either for safety reasons or to avoid target photodegradation. This often leads to spectra characterized by a low signal-to-noise ratio, where methods based on correlation usually fail. For this reason, a method based on peak identification through FMFs is presented, discussed and validated over a large set of samples. In particular, a Monte Carlo simulation has been employed to determine the best parameters of the fuzzy membership functions based on the analysis of performances of the classification procedure. The ROC curves have been analyzed, and AUC and best accuracy are employed as key parameters to evaluate the classification performances on different amounts of ammonium nitrate (from 300 to 1500 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>g) and different laser exposure levels (from 3.1 to 250 mJ/cm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>).https://www.mdpi.com/2227-9040/10/8/295Raman spectroscopyfuzzy logicMonte Carlo simulationROC curves |
| spellingShingle | Federico Angelini Simone Santoro Francesco Colao Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation Chemosensors Raman spectroscopy fuzzy logic Monte Carlo simulation ROC curves |
| title | Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation |
| title_full | Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation |
| title_fullStr | Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation |
| title_full_unstemmed | Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation |
| title_short | Chemical Identification from Raman Peak Classification Using Fuzzy Logic and Monte Carlo Simulation |
| title_sort | chemical identification from raman peak classification using fuzzy logic and monte carlo simulation |
| topic | Raman spectroscopy fuzzy logic Monte Carlo simulation ROC curves |
| url | https://www.mdpi.com/2227-9040/10/8/295 |
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