Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
Source abstract
Abstract
To address the limitations of a traditional Fourier-transform infrared (FTIR) spectrometer, including its bulky size, high cost, and unsuitability for on-site industrial detection, this study developed a Fourier-transform near-infrared (FT-NIR) absorption testing system utilizing Micro-Electro-Mechanical System (MEMS) technology for detecting trace water content in ethylene glycol. The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. To enhance predictive accuracy of water trace, spectral data were preprocessed using smoothing combined with first-derivative processing, and optimal selection of absorption wavelength feature was performed using interval Partial Least Squares (iPLS). Cross-batch external validation demonstrated a Limit of Detection (LOD) of 0.026% with 95% confidence which satisfies the rapid screening requirements for water exceedances (>0.1%) in industrial applications. These findings provide a robust technical foundation for developing handheld, in situ water detection devices.
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Research context
OpenAlex topics: Spectroscopy and Chemometric Analyses · Advanced Chemical Sensor Technologies · Water Quality Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Optoelectronic Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Guangxi Key Laboratory of Optoelectronic Information Processing, Guilin 541004, China
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
Funding
National Natural Science Foundation of China · Guangxi Science and Technology Program Project · Natural Science Foundation of Guangxi Province · Innovation Project of GUET Graduate Education · Funding of Guangxi Key Laboratory of Opto-electronic Information Processing · Middle-aged and Young Teachers’ Basic Ability Promotion Project of Guangxi
References (40)
Show source-provided references
1. Technoeconomic and environmental analysis of ethylene glycol production from coal and natural gas compared with oil-based production10.1016/j.jclepro.2020.123120 ↗
2. Determination and Exploration of Water Content in the EG Products Process
3. Experimental Methods in Chemical Engineering: Karl Fischer Titration10.1002/cjce.25295 ↗
4. Vaporisation coulometric Karl Fischer titration: A perfect tool for water content determination of difficult matrix reference materials10.1016/j.foodchem.2007.01.079 ↗
5. Liu, J.X. (2008). Practical Near-Infrared Spectroscopy Analysis Technology, Science Press.
6. The determination of moisture in solids: A Selected Review10.1016/S0003-2670(00)81740-7 ↗
7. A Review: Integration of NIRS and Chemometric Methods for Tea Quality Control—Principles, Spectral Preprocessing Methods, Machine Learning Algorithms, Research Progress, and Future Directions10.1016/j.foodres.2025.115870 ↗
8. Lu, W.Z. (2007). Modern Near-Infrared Spectroscopy Analysis Technology, China Petrochemical Press. [2nd ed.].
9. Quality evaluation of frozen guava and yellow passion fruit pulps by NIR spectroscopy and chemometrics10.1016/j.foodres.2016.04.027 ↗
10. Prediction of Dissolution Profiles by Non-Destructive NIR Spectroscopy in Bilayer Tablets10.1016/j.ijpharm.2019.05.022 ↗
11. NIR Spectroscopy of Actaea racemosa L. Rhizome—En Route to Fast and Low-Cost Quality Assessment10.1055/s-0043-108122 ↗
12. Qualitative and quantitative analysis of counterfeit fluconazole capsules: A non-invasive approach using NIR spectroscopy and chemometrics10.1016/j.talanta.2018.11.088 ↗
13. Liang, J., Yu, X., Hong, W., and Cai, Y. (2024). Information Extraction of UV-NIR Spectral Data in Waste Water Based on Large Language Model. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 318.10.1016/j.saa.2024.124475 ↗
14. Monitoring of Salinity of Water on the THA CHIN River Basin Using Portable Vis-NIR Spectrometer Combined with Machine Learning Algorithms10.1016/j.molstruc.2023.135720 ↗
15. Czarnecki, M.A., Beć, K.B., Grabska, J., Huck, C.W., Mazurek, S., and Orzechowski, K. (2023). State of Water in Various Environments: Aliphatic Ketones. MIR/NIR Spectroscopic, Dielectric and Theoretical Studies. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 302.10.1016/j.saa.2023.123057 ↗
16. Machine Learning Based Technique to Predict the Water Adulterant in Milk Using Portable Near Infrared Spectroscopy10.1016/j.jfca.2024.106270 ↗
17. Fast and Robust NIRS-Based Characterization of Raw Organic Waste: Using Non-Linear Methods to Handle Water Effects10.1016/j.watres.2022.119308 ↗
18. Simple and Effective Mathematical Models for Cellulose Water Content Calculation from Absorbance/Wavenumber Shifts in NIR Spectrum10.1016/j.polymertesting.2022.107874 ↗
19. Data Fusion Strategy for Rapid Prediction of Moisture Content During Drying of Black Tea Based on Micro-NIR Spectroscopy and Machine Vision10.1016/j.ijleo.2023.170645 ↗
20. Near-infrared (NIR) spectrometry as a fast and reliable tool for fat and moisture analyses in olives10.1021/acsomega.8b02491 ↗
21. Determination of water in olive oil: Rapid FT-NIR spectroscopic procedure based on the Karl-Fischer reference method10.5650/jos.ess20078 ↗
22. Yue, J., Zhang, H., Gao, L., Tian, W., Luo, J., Nie, L., Li, L., Wu, A., and Zang, H. (2025). Benchtop and Different Miniaturized Near-Infrared Spectrometers Application Study: Calibration Transfer and 2D-COS for In-Situ Analysis of Moisture Content in HPMC. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 333.10.1016/j.saa.2025.125889 ↗
23. Rapid On-Site Analysis of Soil Microplastics Using Miniaturized NIR Spectrometers: Key Aspect of Instrumental Variation10.1016/j.jhazmat.2024.135967 ↗
24. Characterization of Skin Moisture and Evaluation of Cosmetic Moisturizing Properties Using Miniature Near-Infrared Spectrometer10.1016/j.infrared.2023.104759 ↗
25. Process Monitoring of Moisture Content and Mass Transfer Rate in a Fluidised Bed with a Low Cost Inline MEMS NIR Sensor10.1007/s11095-020-02787-y ↗
26. Rapid method for the determination of Moisture content in biodiesel using FTIR spectroscopy10.1007/s11746-011-1866-0 ↗
27. Constructing In-Situ and Real-Time Monitoring Methods During Soy Sauce Production by Miniature Fiber NIR Spectrometers10.1016/j.foodchem.2024.140788 ↗
28. Process Understanding and Monitoring: A Glimpse into Data Strategies for Miniaturized NIR Spectrometers10.1016/j.aca.2023.341902 ↗
29. Predicting the Nutrition Deficiency of Fresh Pear Leaves with a Miniature Near-Infrared Spectrometer in the Laboratory10.1016/j.measurement.2021.110553 ↗
30. Non-Destructive Authentication of Melon Seed (Cucumeropsis mannii) Powder Using a Pocket-Sized Near-Infrared (NIR) Spectrophotometer with Multiple Spectral Preprocessing10.1016/j.jfca.2024.106425 ↗
31. Fast Determination of Oxides Content in Cement Raw Meal Using NIR Spectroscopy Combined with Synergy Interval Partial Least Square and Different Preprocessing Methods10.1016/j.measurement.2019.106990 ↗
32. Measurement Errors and Implications for Preprocessing in Miniaturised Near-Infrared Spectrometers: Classification of Sweet and Bitter Almonds as a Case of Study10.1016/j.talanta.2024.126271 ↗
33. Metabolic engineering of a pentose metabolism pathway in ethanologenic Zymomonas mobilis10.1126/science.267.5195.240 ↗
34. Clua-Palau, G., Jo, E., Nikolic, S., Coello, J., and Maspoch, S. (2020). Finding a Reliable Limit of Detection in the NIR Determination of Residual Moisture in a Freeze-Dried Drug Product. J. Pharm. Biomed. Anal., 183.10.1016/j.jpba.2020.113163 ↗
35. Multivariate Detection Limits of On-Line NIR Model for Extraction Process of Chlorogenic Acid from Lonicera japonica10.1016/j.jpba.2012.12.026 ↗
36. Absorption and quantitative characteristics of C-H bond and O-H bond of NIR10.1134/S0030400X1411023X ↗
37. Optical Constants of Ethylene Glycol Over an Extremely Wide Spectral Range10.1016/j.optmat.2014.04.035 ↗
38. NIR Spectrometric Determination of Quality Parameters in Vegetable Oils Using iPLS and Variable Selection10.1016/j.foodres.2007.12.013 ↗
39. Combination of Near Infrared Spectroscopy with Characteristic Interval Selection for Rapid Detection of Rice Protein Content10.1016/j.jfca.2024.106995 ↗
40. Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy10.1366/0003702001949500 ↗