A mobile phone digital image method designed for efficient durum wheat flour characterization.

Area of Science:

• Food Science and Technology
• Analytical Chemistry
• Image Processing

Background:

• Accurate characterization of foreign bodies and impurities in wheat flour and semolina is crucial for food safety and quality.
• Existing international standards, like UNI 10941:2001, may involve subjective interpretations.
• Regulatory and quality norms mandate the recognition and counting of these contaminants.

Purpose of the Study:

• To propose and assess a novel, semi-automatic analytical method for foreign body and impurity detection in flour and semolina.
• To develop a cost-effective and efficient alternative to current standard methods.
• To validate the proposed method against established standards and diverse sample types.

Main Methods:

• Utilized smartphone-captured digital images under controlled illumination.
• Employed a macro-script with free Fiji-ImageJ software for image analysis.
• Applied image processing techniques including grayscale conversion, contrast adjustment, and intensity thresholding to identify and count particles.
• Recognized four component fractions to characterize product types.

Main Results:

• The method demonstrated an estimated processing time of less than 60 seconds per sample.
• Validation against 14 reference samples showed relative differences of less than ±20% bias compared to reference values.
• Measurement reproducibility was found to be better than 30-40% RSD across different regions of interest (ROIs) and within threshold selections.

Conclusions:

• The developed semi-automatic digital image analysis method offers an efficient and low-cost solution for foreign body and impurity quantification in wheat-based products.
• The method's performance metrics are consistent with industry standards, providing a reliable alternative for quality control.
• This approach enhances the objectivity and efficiency of safety and quality assessments in the cereal sector.