Simultaneous determination of Total Fatty Matter, Iodine Value, and C8–C14 in soap noodles by Vis-NIRS

Importance of the Topic

Vis-NIR spectroscopy offers a rapid, non‐destructive, and simultaneous approach for monitoring critical quality parameters in soap noodles, significantly enhancing throughput and reducing costs in industrial quality control workflows.

Objectives and Study Overview

The study focused on developing and validating Vis-NIR calibration models for key quality attributes of soap noodles:

• Total Fatty Matter (TFM) content
• Iodine Value (IV)
• C8–C14 fatty acid fraction

Methodology

A total of 46 soap noodle samples were measured using a Metrohm XDS SmartProbe Analyzer in reflection mode over the 400–2500 nm range. Spectra were collected directly through sealed sample bags. Data acquisition, management, and model development were performed with Vision Air 2.0 software. Spectral pretreatment involved a second derivative transformation followed by Partial Least Squares (PLS) regression on selected spectral regions.

Used Instrumentation

• Metrohm NIRS XDS SmartProbe Analyzer with 2 m Fiber (Order No. 2.921.1610)
• Vision Air 2.0 Complete software (Order No. 6.6072.208)

Main Results and Discussion

The developed calibration models demonstrated strong predictive performance:

• Total Fatty Matter (78.5–80%): 5 PLS factors; SEC = 0.10%; SECV = 0.13%.
• Iodine Value (36–40 g I2/100 g): 6 PLS factors; SEC = 0.39; SECV = 0.45.
• C8–C14 content (1–17%): 5 PLS factors; SEC = 0.17%; SECV = 0.21%.

The low calibration and cross‐validation errors, along with high correlation to reference methods (titration and chromatography), confirm the method’s reliability for simultaneous quality assessment.

Benefits and Practical Applications

The Vis-NIR approach enables sub‐minute determination of multiple quality parameters without sample preparation or reagents, reducing analysis time, consumable usage, and operational costs. Its intuitive software interface allows routine use by non‐specialized operators, supporting in‐line or at‐line quality control during soap production.

Future Trends and Applications

Extensions of this Vis-NIR platform may include monitoring of moisture content, acid value, and active detergent levels. Integration with process analytical technology (PAT) and automated process control systems could facilitate real‐time quality monitoring and adaptive manufacturing in continuous soap processing environments.

Conclusion

The validated Vis-NIR models provide accurate, fast, and cost‐effective measurement of TFM, IV, and C8–C14 in soap noodles, streamlining industrial quality control and enabling efficient, high‐throughput analysis in soap manufacturing.