{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:02:48Z","timestamp":1784610168661,"version":"3.55.0"},"reference-count":16,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study investigates how Raman peak features influence the accuracy of pollutant concentration quantification in Raman spectroscopy using feature-based machine learning. A Raman peak feature-based approach is applied to data acquired from laboratory Raman equipment for inorganic anion mixtures, with the objective of identifying processing strategies suitable for low-resolution Raman instruments. A dataset comprising nitrate, nitrite, and sulphate dissolved in water at varying concentrations was used to develop a pre-processing pipeline, train and validate models, and perform linear regression using peak- and area-based features extracted from analyte-specific spectral fingerprint regions. Signal downsampling was employed to simulate reduced spectral resolution and evaluate feature robustness under peak broadening conditions. Results indicate that peak-based regression is more sensitive to reduced peak resolution for nitrate and sulphate, whereas area-based regression produces more stable prediction errors across downsampling factors. Conversely, for nitrite, peak-based regression is less affected by downsampling, while area-based regression exhibits degraded performance. Area-based regression achieved mean absolute percentage errors below 7% and 16% for nitrate concentrations above 3833 mg\/L and 1916 mg\/L, respectively, and generally below 16% for sulphate concentrations above 1916 mg\/L. For nitrite, peak-based regression yielded errors within 4%.<\/jats:p>","DOI":"10.3390\/s26144552","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:09:09Z","timestamp":1784527749000},"page":"4552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Raman Peaks Feature-Based Machine Learning for Raman Spectroscopy Quantification of Inorganic Pollutants"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3983-8268","authenticated-orcid":false,"given":"Antonio","family":"Nocera","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michela","family":"Raimondi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9000-7454","authenticated-orcid":false,"given":"Lorenzo","family":"Luciani","sequence":"additional","affiliation":[{"name":"Scuola di Scienze e Tecnologie, Chemistry Division, Universit\u00e0 di Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9474-7046","authenticated-orcid":false,"given":"Laura","family":"Burattini","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3183-7682","authenticated-orcid":false,"given":"Laura","family":"Falaschetti","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria dell\u2019Informazione, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8025-9615","authenticated-orcid":false,"given":"Rossana","family":"Galassi","sequence":"additional","affiliation":[{"name":"Scuola di Scienze e Tecnologie, Chemistry Division, Universit\u00e0 di Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pooja, D., Kumar, P., Singh, P., and Patil, S. (2020). Water Pollutants: Sources and Impact on the Environment and Human Health. Sensors in Water Pollutants Monitoring: Role of Material, Springer.","DOI":"10.1007\/978-981-15-0671-0"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mancuso, G., Bencresciuto, G.F., Lavrni\u0107, S., and Toscano, A. (2021). Diffuse Water Pollution from Agriculture: A Review of Nature-Based Solutions for Nitrogen Removal and Recovery. Water, 13.","DOI":"10.3390\/w13141893"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1586\/14737159.2015.1036744","article-title":"Advantages and limitations of Raman spectroscopy for molecular diagnostics: An update","volume":"15","author":"Eberhardt","year":"2015","journal-title":"Expert Rev. Mol. 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