Inline detection of wheat flour adulteration by NIR spectroscopy

Importance of the Topic

The authenticity of wheat flour is critical for consumer safety and product consistency. Adulteration with substances such as potato starch can reduce nutritional value, introduce allergens, and compromise quality. Inline monitoring of flour composition ensures rapid detection of contaminants, minimizing health risks and economic losses in large-scale production.

Objectives and Study Overview

This application note describes how near-infrared (NIR) spectroscopy can be integrated directly into a wheat flour production line to detect potato starch adulteration. The study’s goal was to develop a real-time analytical framework using Metrohm’s 2060 The NIR Analyzer, enabling continuous quality control without manual sampling delays.

Methodology and Instrumentation

NIR measurements were performed inline using a micro interactance reflectance probe with purge vents, directly sampling material in a feeder or blender. Spectra ranging from 1100 to 2000 nm were collected in under a minute. Multivariate chemometric modeling correlated spectral data to known potato/wheat flour ratios, covering the full range from 0% to 100% adulteration. Instrumentation:

• Metrohm 2060 The NIR Analyzer
• Fiber optic micro interactance reflectance probe with purge tip
• Embedded software for multichannel spectral acquisition

Key Results and Discussion

The NIR model demonstrated a robust calibration across the entire adulteration range. Spectral features corresponding to starch, moisture, protein, and other components enabled accurate quantification of potato starch in wheat flour. Inline analysis provided updated compositional data every 10 seconds, reducing the time between sampling and decision-making from hours to minutes. This real-time feedback improved process control and prevented significant off-spec material from progressing through the production line.

Advantages and Practical Applications

• Rapid, reagent-free, nondestructive analysis
• Continuous monitoring without manual sample handling
• Real-time data supports immediate process adjustments
• Cost savings through reduced waste and chemical usage
• Scalability for multichannel configurations (up to five probes)

Future Trends and Applications

Advancements in machine learning could further refine chemometric models, enhancing sensitivity and selectivity for minor adulterants. Integration with plant control systems and predictive analytics will drive autonomous process optimization. The same approach can extend to other cereal flours and complex mixtures, broadening inline NIR applications across the food industry.

Conclusion

Inline NIR spectroscopy, using Metrohm’s 2060 The NIR Analyzer, provides an efficient, accurate, and cost-effective solution for detecting potato starch adulteration in wheat flour. By delivering real-time compositional insights, this method strengthens quality control, ensures regulatory compliance, and safeguards consumer health.

Reference

1. Rohman, A., & Che Man, Y. B. (2011). The use of Fourier transform mid-infrared (FT-MIR) spectroscopy for detection and quantification of adulteration in virgin coconut oil. Food Chemistry, 129(2), 583-588. https://doi.org/10.1016/j.foodchem.2011.04.070
2. Shahbandeh, M. (2023). Wheat - statistics & facts. Statista. Retrieved July 12, 2023, from https://www.statista.com/topics/1668/wheat/
3. Tao, C., Wang, K., Liu, X., et al. (2020). Effects of potato starch on the properties of wheat dough and the quality of fresh noodles. CyTA - Journal of Food, 18(1), 427-434. https://doi.org/10.1080/19476337.2020.1768152
4. Yánez, E., Ballester, D., Wuth, H., et al. (1981). Potato flour as partial replacement of wheat flour in bread: Baking studies and nutritional value of bread containing graded levels of potato flour. International Journal of Food Science & Technology, 16(3), 291-298. https://doi.org/10.1111/j.1365-2621.1981.tb01017.x
5. Rady, A. M., & Guyer, D. E. (2015). Rapid and/or nondestructive quality evaluation methods for potatoes: A review. Computers and Electronics in Agriculture, 117, 31-48. https://doi.org/10.1016/j.compag.2015.07.002