Quantitative Analysis of Solutions Using a High Resolution Portable Raman Spectrometer

Significance of the topic

Raman spectroscopy offers
Its high spectral resolution enables both structural elucidation and quantification
The ability to analyze aqueous solutions without extensive preparation increases its applicability in food, pharmaceutical, and process industries

Objectives and overview

The study aimed to develop a quantitative calibration model to measure concentrations of glucose, fructose, and sucrose in a tertiary aqueous mixture
A portable Raman spectrometer (i-Raman Plus) coupled with BWIQ chemometric software was employed to build and validate multivariate regression models

Methodology

A set of 31 standard solutions containing known amounts of glucose, fructose, and sucrose was prepared in distilled water with total sugar concentration fixed at 0.4 M
Spectral acquisition was conducted over 176–3200 cm-1 using 785 nm excitation at approximately 300 mW and 50 s integration time
Duplicate measurements yielded 62 spectra, with 50 spectra (25 samples) used for calibration and 12 spectra reserved for external validation
Partial Least Squares regression (PLS1) was applied without preprocessing over the 250–1500 cm-1 region to construct calibration models

Instrumentation

• i-Raman Plus portable Raman spectrometer (B&W Tek)
• 785 nm diode laser with approximately 300 mW power output
• Fiber-optic probe for noninvasive sampling through glass vials
• BWIQ chemometric software incorporating PLSR and PCA algorithms

Main results and discussion

Three-factor PLS models were developed separately for glucose, fructose, and sucrose
Calibration and prediction results showed R2 values exceeding 0.999 for glucose, with RMSEC and RMSEP below 0.008 M
External validation samples demonstrated accurate concentration predictions within experimental error

Benefits and practical applications

• Rapid, noninvasive quantification of individual sugars in multicomponent aqueous systems
• Portable setup facilitates in-field or at-line measurements without extensive sample preparation
• High accuracy and precision suitable for food quality control, fermentation monitoring, and pharmaceutical analysis

Future trends and potential applications

• Extension of multivariate models to other analytes and complex matrices
• Integration of advanced machine learning techniques for improved baseline correction and classification
• Miniaturization and automation for real-time process analytics
• Combination with complementary spectroscopic methods for comprehensive molecular profiling

Conclusion

The portable i-Raman Plus system combined with BWIQ software delivered robust quantitative models for sugar analysis in aqueous mixtures, achieving high spectral resolution and low prediction error while maintaining user-friendly operation across diverse analytical contexts

Reference

• Pelletier, M.J. Applied Spectroscopy 57, 20A–42A (2003)