{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T10:35:33Z","timestamp":1780396533134,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["51805177"],"award-info":[{"award-number":["51805177"]}]},{"name":"the National Natural Science Foundation of China","award":["11BS413"],"award-info":[{"award-number":["11BS413"]}]},{"name":"the Scientific Research Fund Project of Huaqiao University","award":["51805177"],"award-info":[{"award-number":["51805177"]}]},{"name":"the Scientific Research Fund Project of Huaqiao University","award":["11BS413"],"award-info":[{"award-number":["11BS413"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction (Rp2) was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the Rp2 was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.<\/jats:p>","DOI":"10.3390\/s22155809","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"5809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6277-3103","authenticated-orcid":false,"given":"Dengshan","family":"Li","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11235-013-9689-y","article-title":"Design of WSN node for water pollution remote monitoring","volume":"53","author":"Chen","year":"2013","journal-title":"Telecommun. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"810","DOI":"10.4319\/lom.2014.12.810","article-title":"The determination of pH in hypersaline lakes with a conventional combination glass electrode","volume":"12","author":"Golan","year":"2014","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117815","DOI":"10.1016\/j.saa.2019.117815","article-title":"Rapid analysis of soluble solid content in navel orange based on visible-near infrared spectroscopy combined with a swarm intelligence optimization method","volume":"228","author":"Song","year":"2020","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103045","DOI":"10.1016\/j.infrared.2019.103045","article-title":"Rapid determination of moisture content in compound fertilizer using visible and near infrared spectroscopy combined with chemometrics","volume":"102","author":"Wang","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"120138","DOI":"10.1016\/j.saa.2021.120138","article-title":"Rapid determination of hemoglobin concentration by a novel ensemble extreme learning machine method combined with near-infrared spectroscopy","volume":"263","author":"Wang","year":"2021","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jfoodeng.2009.02.023","article-title":"Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics","volume":"94","author":"Xie","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, L., and Li, D. (2021, January 11\u201313). A Hybrid Multivariate Calibration Optimization Method for Visible Near Infrared Spectral Analysis. Proceedings of the 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO), Guangzhou, China.","DOI":"10.1109\/CMMNO53328.2021.9467659"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"136765","DOI":"10.1016\/j.scitotenv.2020.136765","article-title":"Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy","volume":"714","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.infrared.2019.01.005","article-title":"Detection method of TFe content of iron ore based on visible-infrared spectroscopy and IPSO-TELM neural network","volume":"97","author":"Xiao","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103003","DOI":"10.1016\/j.infrared.2019.103003","article-title":"WSPXY combined with BP-ANN method for hemoglobin determination based on near-infrared spectroscopy","volume":"102","author":"Tian","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106655","DOI":"10.1016\/j.compag.2021.106655","article-title":"Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models","volume":"193","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.trac.2019.01.018","article-title":"An overview of variable selection methods in multivariate analysis of near-infrared spectra","volume":"113","author":"Yun","year":"2019","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.aca.2020.03.055","article-title":"Understanding the learning mechanism of convolutional neural networks in spectral analysis","volume":"1119","author":"Zhang","year":"2020","journal-title":"Anal. Chim. Acta"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.chemolab.2018.07.008","article-title":"Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration","volume":"182","author":"Cui","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111741","DOI":"10.1016\/j.postharvbio.2021.111741","article-title":"Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy","volume":"183","author":"Mishra","year":"2022","journal-title":"Postharvest Biol. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.aca.2019.08.064","article-title":"Feature visualization of Raman spectrum analysis with deep convolutional neural network","volume":"1087","author":"Fukuhara","year":"2019","journal-title":"Anal. Chim. Acta"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"134723","DOI":"10.1016\/j.scitotenv.2019.134723","article-title":"Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy","volume":"702","author":"Ng","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106638","DOI":"10.1016\/j.compag.2021.106638","article-title":"Early detection of freezing damage in oranges by online Vis\/NIR transmission coupled with diameter correction method and deep 1D-CNN","volume":"193","author":"Tian","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104287","DOI":"10.1016\/j.chemolab.2021.104287","article-title":"A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit","volume":"212","author":"Mishra","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s11694-007-9012-2","article-title":"Influence of temperature on the measurement of NaCl content of aqueous solution by short-wavelength near infrared spectroscopy (SW-NIR)","volume":"1","author":"Huang","year":"2007","journal-title":"Sens. Instrum. Food Qual."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116045","DOI":"10.1016\/j.trac.2020.116045","article-title":"New data preprocessing trends based on ensemble of multiple preprocessing techniques","volume":"132","author":"Mishra","year":"2020","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wiedemair, V., Langore, D., Garsleitner, R., Dillinger, K., and Huck, C. (2019). Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis. Molecules, 24.","DOI":"10.3390\/molecules24030428"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116150","DOI":"10.1016\/j.fuel.2019.116150","article-title":"Feasibility study of gross calorific value, carbon content, volatile matter content and ash content of solid biomass fuel using laser-induced breakdown spectroscopy","volume":"258","author":"Lu","year":"2019","journal-title":"Fuel"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least square regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11947-013-1065-0","article-title":"Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS\/NIR Spectroscopy and Least Squares Support Vector Machine","volume":"7","author":"Bao","year":"2014","journal-title":"Food Bioprocess Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.aca.2008.01.039","article-title":"Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy","volume":"610","author":"Liu","year":"2008","journal-title":"Anal. Chim. Acta"},{"key":"ref_27","first-page":"557","article-title":"Particle swarm optimization algorithm based on parameter improvements","volume":"17","author":"Wu","year":"2017","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.aca.2014.12.033","article-title":"A consensus successive projections algorithm--multiple linear regression method for analyzing near infrared spectra","volume":"858","author":"Liu","year":"2015","journal-title":"Anal. Chim Acta"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Saldanha","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_30","unstructured":"Bjerrum, E.J., Glahder, M., and Skov, T. (2017). Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118994","DOI":"10.1016\/j.saa.2020.118994","article-title":"Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering","volume":"246","author":"Zhu","year":"2021","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e2977","DOI":"10.1002\/cem.2977","article-title":"One-dimensional convolutional neural networks for spectroscopic signal regression","volume":"32","author":"Malek","year":"2018","journal-title":"J. Chemom."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120633","DOI":"10.1016\/j.saa.2021.120633","article-title":"Quantitative detection of Aflatoxin B1 by subpixel CNN regression","volume":"268","author":"Zhu","year":"2022","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s11426-008-0080-x","article-title":"Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation","volume":"51","author":"Liu","year":"2008","journal-title":"Sci. China Ser. B: Chem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.renene.2020.07.117","article-title":"Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting","volume":"161","author":"Sewdien","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"121808","DOI":"10.1016\/j.energy.2021.121808","article-title":"A novel loss function of deep learning in wind speed forecasting","volume":"238","author":"Chen","year":"2021","journal-title":"Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"319","DOI":"10.2116\/analsci.17.319","article-title":"Elimination of the Uninformative Calibration Sample Subset in the Modified UVE (Uninformative Variable Elimination)\u2013PLS (Partial Least Squares) Method","volume":"17","author":"Koshoubu","year":"2001","journal-title":"Anal. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108405","DOI":"10.1016\/j.meatsci.2020.108405","article-title":"A global calibration model for prediction of intramuscular fat and pH in red meat using hyperspectral imaging","volume":"181","author":"Dixit","year":"2021","journal-title":"Meat Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.aca.2013.11.032","article-title":"A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration","volume":"807","author":"Yun","year":"2014","journal-title":"Anal. Chim. Acta"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:03:46Z","timestamp":1760141026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,3]]},"references-count":39,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155809"],"URL":"https:\/\/doi.org\/10.3390\/s22155809","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,3]]}}}