Near-Infrared Spectroscopy for Monitoring Ionic Liquids in Pharmaceutical Production

Fangfang Chen; Ben Li; Fei Wang; Zansheng Zheng; Yibo Zou; Yiting Yu

doi:10.3788/AOSOL250484

Acta Optica Sinica (Online), Volume. 2, Issue 15, 1514003(2025)

Near-Infrared Spectroscopy for Monitoring Ionic Liquids in Pharmaceutical Production

Fangfang Chen¹, Ben Li^1,2, Fei Wang^1,2, Zansheng Zheng³, Yibo Zou⁴, and Yiting Yu^1,2、*

¹Key Laboratory of Scale Manufacturing Technologies for High-Performance MEMS Chips of Zhejiang Province, Key Laboratory of Optical Microsystems and Application Technologies of Ningbo City, Ningbo Institute of Northwestern Polytechnical University, Ningbo 315103, Zhejiang , China

²Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education), Key Laboratory of Micro and Nano Electro-Mechanical Systems of Shaanxi Province, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, Shaanxi , China

³Ningbo Chemgoo Pharma Tech Co., Ltd., Ningbo 315103, Zhejiang , China

⁴Ningbo Smartflow Co., Ltd., Ningbo 315103, Zhejiang , China

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Figures & Tables(8)

Fig. 1. Near-infrared spectra of N-methylimidazole, bromoethane, and ionic liquid. (a) Spectral curves in the 1350‒1650 nm band; (b) spectral curves in the 1550‒1850 nm band; (c) spectral curves in the 1750‒2150 nm band

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Fig. 2. Near-infrared spectra of acetone and dichloromethane. (a) Solvent spectral curves in the 1350‒1650 nm band; (b) solvent spectral curves in the 1550‒1850 nm band; (c) solvent spectral curves in the 1750‒2150 nm band

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Fig. 3. Near infrared spectra of different pretreatment methods. (a) Raw spectra; (b) 1^st-der; (c) MSC; (d) SG smoothing; (e) SNV

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Fig. 4. Feature wavelength distribution obtained by different feature selection algorithms. (a) SPA; (b) CARS; (c) GA

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Fig. 5. Scatter plots of predicted and actual values for PLSR models built by different feature selection algorithms. (a) SPA-PLSR; (b) CARS-PLSR; (c) GA-PLSR

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$Plots of predicted and actual mass fraction for the glucose validation model. (a) Calibration set; (b) prediction set$

Fig. 6. Plots of predicted and actual mass fraction for the glucose validation model. (a) Calibration set; (b) prediction set

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Table 1. Influence of pretreatment methods on PLSR model parameters

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Table 1. Influence of pretreatment methods on PLSR model parameters

Pretreatment method	Principal factor number	η_RMSEC	$R_{c}^{2}$	η_RMSECV	$R_{c v}^{2}$	η_RMSEP	$R_{p}^{2}$
None	6	0.1368	0.9364	0.2067	0.8635	0.1921	0.8719
1^st-der	5	0.1107	0.9585	0.1756	0.8957	0.1752	0.9082
MSC	4	0.1348	0.9432	0.1868	0.8695	0.1842	0.9179
SG smoothing	4	0.1052	0.9649	0.2451	0.8096	0.2001	0.8522
SNV	4	0.1147	0.9625	0.1845	0.9031	0.1384	0.9271

Table 2. PLSR model parameters under different feature selection algorithms
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Table 2. PLSR model parameters under different feature selection algorithms
Model Number of wavelengths η_RMSEC $R_{c}^{2}$ η_RMSEP $R_{p}^{2}$
SPA-PLSR 10 0.1137 0.9588 0.1119 0.9576
CARS-PLSR 93 0.0689 0.9836 0.1008 0.9666
GA-PLSR 152 0.1127 0.9601 0.1145 0.9516

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Fangfang Chen, Ben Li, Fei Wang, Zansheng Zheng, Yibo Zou, Yiting Yu. Near-Infrared Spectroscopy for Monitoring Ionic Liquids in Pharmaceutical Production[J]. Acta Optica Sinica (Online), 2025, 2(15): 1514003

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