Monitoring the production of stearic acid and its salts with NIR spectroscopy

Significance of the topic

Near-infrared (NIR) spectroscopy offers rapid, nondestructive, and cost-effective analysis for key quality attributes during stearic acid and stearate production. Reliable monitoring of acid number, iodine value, moisture, ash, melting point and granulometry across process stages ensures product consistency and regulatory compliance in industries such as pharmaceuticals, cosmetics, food, plastics and lubricants.

Objectives and overview of the study

This application note evaluates the OMNIS NIR Analyzer (Liquid/Solid) for quantitative monitoring of critical quality parameters throughout stearic acid production and conversion to magnesium, calcium and zinc stearates. The goals were to demonstrate feasibility for both liquid (process intermediates) and solid (final stearates) samples, to develop calibration models for routine quality control, and to report figures of merit (FOMs) for each parameter.

Methodology

The study used representative samples from different production stages:

• Pre-entry (raw material): acid number and iodine value — 44 and 40 samples respectively
• Hydrogenation: acid number — 17 samples
• Cleavage: acid number — 99 samples
• Final Mg/Ca/Zn stearates: ash, granulometry, melting point, moisture — 69 samples

Sample handling and spectral acquisition:

• Liquid/viscous samples (early stages) measured in transmission using 8 mm disposable vials at 75 °C (vessel temperature control mode) to ensure consistent optical path and to melt high‑melting components
• Solid final products measured in reflectance using 28 mm disposable vials with automated multi‑position sampling to address sample heterogeneity
• Spectra and model development were performed using OMNIS Software with the OMNIS Model Developer (OMD) workflow

Used instrumentation

The hardware and software configuration included:

• OMNIS NIR Analyzer Liquid/Solid (combined liquid and solid modules)
• Disposable vials: 8 mm (transmission) and 28 mm (reflection)
• Vial holders and a flexible holder for variable diameters
• OMNIS Stand‑Alone license and Quant Development software license for calibration/model development

Main results and discussion

Quantitative NIR models were developed for multiple parameters. Correlation plots between NIR predictions and laboratory reference values showed good agreement. Key figures of merit for calibration/validation are summarized below:

• Acid number — pre-entry: R2 = 0.994; SEC = 0.16 mg KOH/g; SECV = 0.21 mg KOH/g; SEP = 0.12 mg KOH/g. Excellent correlation for raw material screening.
• Iodine value — pre-entry: R2 = 0.962; SEC = 0.40 g I2/100 g; SECV = 0.46; SEP = 0.52. Strong predictive power for unsaturation level in feedstock.
• Acid number — hydrogenation: R2 = 0.921; SEC = 0.11 mg KOH/g; SECV = 0.40; SEP = 0.17. Good model performance though slightly reduced compared with pre‑entry, likely due to matrix variability in intermediate mixtures.
• Ash content — final stearates: R2 = 0.971; SEC = 0.29%; SECV = 0.33%; SEP = 0.36%. Reliable estimation of inorganic residue in stearates.
• Moisture — final stearates: R2 = 0.992; SEC = 0.14%; SECV = 0.14%; SEP = 0.15%. High accuracy for low‑level water content determination.
• Melting point — final stearates: R2 = 0.996; SEC = 1.05 °C; SECV = 1.40 °C; SEP = 1.25 °C. Very precise prediction of thermal properties relevant to handling and formulation.
• Granulometry — final stearates: R2 = 0.862; SEC = 0.09 mm; SECV = 0.12 mm; SEP = 0.15 mm. Predictive but with lower correlation, reflecting challenges in characterizing particle size by bulk NIR reflectance.

The results show that the selected NIR spectral information and chemometric models are sufficient for reliable quality control for most parameters. The instrument's temperature control (up to 80 °C) and automated multi‑position solid sampling improved repeatability and model robustness. Granulometry and some intermediate process models exhibited lower correlation, indicating areas where larger calibration sets or complementary techniques may be required.

Benefits and practical applications

NIR analysis delivered several operational advantages:

• Rapid measurements (<10 seconds per sample) and minimal sample preparation
• Single instrument able to measure both liquids and solids, enabling seamless transition between process monitoring and final product QC
• Precise temperature control to ensure representative spectra for viscous or high‑melting samples
• Automated multi‑position solid sampling for improved reproducibility with heterogeneous samples
• Potential for integration into automation and process analytical technology (PAT) workflows and for linkage with other analytical tools (e.g., titration)

These advantages support at‑line or lab‑based quality control to accelerate decision making, reduce turnaround time, and decrease reliance on slower, destructive reference methods.

Future trends and potential applications

Opportunities to extend and improve NIR deployment in stearic acid production include:

• Expanding calibration sets to cover more raw material and process variability to improve model robustness, especially for granulometry and intermediate matrices
• Implementing model transfer and standardization strategies for multi‑site or multi‑instrument use
• Integrating NIR data into real‑time process control loops (PAT) and enterprise systems for automated corrective actions
• Combining NIR with complementary sensors (e.g., lasers for particle sizing, DSC for thermal analysis) in hybrid analytical platforms
• Using advanced chemometrics and machine learning to handle complex, nonlinear matrix effects and to detect outliers or process drifts

Conclusion

The OMNIS NIR Analyzer Liquid/Solid demonstrated strong applicability for routine quantitative monitoring of critical quality attributes during stearic acid production and conversion to stearates. Most models (acid number, iodine value, ash, moisture, melting point) showed high correlation with laboratory references and acceptable error metrics for QC use. Granulometry and some intermediate process predictions were less robust and may benefit from expanded datasets or complementary methods. Overall, NIR delivers fast, nondestructive, and operationally efficient analytics that can be integrated into modern quality systems and PAT strategies.

References

The application note describes experimental sample sets, figures of merit and instrument configuration as provided by the OMNIS NIR Analyzer Liquid/Solid documentation and methodology included in the study.