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This pilot study aims to rapidly and non-destructively screen flours based on key nutraceutical indicators such as fats, carbohydrates, and proteins. It has industrial relevance for both high-throughput online screening and occasional at-line sampling and testing. The study analyzed 47 commercially available flours from cereals, legumes, tubers, and others, each with varying nutraceutical values. Using the SpectraPod™ pocket-sized near-infrared spectral sensor, we measured the flour reflectance spectra. Chemometric processing of the spectroscopic data allowed us to classify flours into two fat classes with 98% accuracy and three classes for carbohydrates and proteins, each with 89% accuracy.

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Rapid Profiling of Flour with Near-Infrared Spectral Sensing and Chemometrics

  • 会议论文

  • 发表日期：

    2025年

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  • Leonardo Ciaccheri,
  • Anna G. Mignani,
  • Andrea A. Mencaglia,
  • Lien Smeesters

摘要

This pilot study aims to rapidly and non-destructively screen flours based on key nutraceutical indicators such as fats, carbohydrates, and proteins. It has industrial relevance for both high-throughput online screening and occasional at-line sampling and testing. The study analyzed 47 commercially available flours from cereals, legumes, tubers, and others, each with varying nutraceutical values. Using the SpectraPod™ pocket-sized near-infrared spectral sensor, we measured the flour reflectance spectra. Chemometric processing of the spectroscopic data allowed us to classify flours into two fat classes with 98% accuracy and three classes for carbohydrates and proteins, each with 89% accuracy.