# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 4 Conclusions  
This study demonstrates the successful integration of a MEMS-based NIR spectrometer with chemometric algorithms for rapid water quantification in ethylene glycol. The optimized PLSR model achieved a detection limit (LOD) of 0.026%, four times lower than the industrial control threshold (0.1%), ensuring reliable identification of non-compliant batches. By employing interval Partial Least Squares (iPLS), critical absorption bands at 1450 nm and 1940 nm--corresponding to O-H stretching overtones--were prioritized, enhancing model interpretability compared to conventional \"black-box\" approaches. Despite these advancements, limitations exist; predictions for ultra-low concentrations (<0.026%, e.g., sample 13 with 0.003% H\({}_{2}\)O) exhibited amplified errors (375.63%) due to spectral noise dominance, and validation under controlled laboratory conditions may not fully represent industrial field environments with temperature fluctuations or particulate interference.  
To address these challenges and advance the technology toward industrial adoption, future efforts will focus on three interconnected objectives. First, algorithmic enhancements will integrate hybrid CNN-PLSR architectures to capture nonlinear spectral relationships in expanded datasets, targeting an LOD reduction to 0.01%. Concurrently, multi-matrix adaptability will be prioritized via transfer learning frameworks, enabling rapid adaptation to related glycol-based solvents (e.g., propylene glycol) with minimal target-domain samples (<50). Critically, the current model supports qualitative screening of non-compliant samples (H\({}_{2}\)O \(\geq\) 0.026%) and provides reliable quantitative analysis within 0.05-0.7% (RMSEP = 0.0115%, R\({}^{2}\) = 0.997). For trace-level detection (<0.026%), cross-validation via GC-MS or sample pre-concentration is strongly recommended to ensure data credibility. These innovations aim to bridge the gap between laboratory validation and field deployment, ensuring scalability across diverse industrial scenarios.  
Conceptualization, Q.L. and Y.R.; methodology, Q.L. and Z.G.; validation, Q.L., Y.R. and Z.G.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, Y.R., D.L., B.C. and Q.L.; visualization, Z.G.; supervision, Y.R. and D.L.; project administration, Y.R. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant no. 62275058), Guangxi Science and Technology Program Project (No. AD25069073), Natural Science Foundation of Guangxi Province (No. 2023 GXNSFAA026259). Innovation Project of GUET Graduate Education (2023 YCXS228), Funding of Guangxi Key Laboratory of Opto-electronic InformationProcessing (No. GD24105), Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi (2024 KY0802, 2025 KY0240).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.