Flour analysis by near-infrared spectroscopy (NIRS)

Significance of the Topic

Near-infrared spectroscopy (NIRS) enables rapid, non-destructive assessment of critical flour quality parameters. By measuring moisture, protein, ash, and rheological properties within seconds, NIRS reduces sample preparation time and eliminates the need for chemicals. This capability supports higher milling yield, consistent product quality and efficient process control in the flour industry.

Objectives and Study Overview

This study aimed to develop and validate quantitative NIR models for key compositional and functional attributes of flour. A diverse set of 237 samples, comprising wheat, spelt and other flour types, was analyzed to ensure robust calibration. Primary reference methods (AOAC protocols) provided moisture, protein and ash values for model training and validation.

Methodology and Instrumentation

The samples were measured using a Metrohm OMNIS NIR Analyzer Solid in diffuse reflectance mode. A rotating 100 mm cup ensured representative spectra collection. OMNIS software facilitated automatic averaging of spectra and model development. Moisture was determined by AOAC 953.10, protein by AOAC 2001.11 and ash by AOAC 923.03.

Instrumentation Used

• OMNIS NIR Analyzer Solid
• Large holder OMNIS NIR (100 mm)
• Large cup OMNIS NIR (100 mm)
• OMNIS Stand-Alone software license
• Quant Development software license

Main Results and Discussion

Calibration models demonstrated strong correlations between NIR predictions and reference data: moisture (R2 = 0.827; SEC = 0.47%; SECV = 0.48%), protein (R2 = 0.789; SEC = 1.89%; SECV = 2.68%) and ash (R2 = 0.929; SEC = 0.221%; SECV = 0.231%). Additional flour attributes achieved high cross-validation performance: gluten (R2CV = 0.918), baking strength (R2CV = 0.919), elasticity index (R2CV = 0.906) and tenacity (R2CV = 0.910). Starch prediction was moderate (R2CV = 0.755).

Benefits and Practical Applications

• Chemical-free, minimal sample preparation
• Analysis time below 10 seconds per sample
• At-line and laboratory implementation with minimal training
• Continuous quality control of compositional and rheological parameters

Future Trends and Potential Applications

Advancements may include inline process monitoring, integration with automation systems and real-time control using chemometric software. The expansion of predictive models to cover novel flour varieties and end-use properties, coupled with artificial intelligence, will further enhance process optimization and product consistency.

Conclusion

This work demonstrates that NIR spectroscopy with the Metrohm OMNIS platform offers fast, accurate and comprehensive flour quality analysis. The approach supports improved milling efficiency and reliable prediction of dough performance, underscoring its value for industrial and laboratory settings.