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<p>Foxtail millet (<i>Setaria italica</i> L.) is a nutrient-dense, climate-resilient grain, but its utilization is limited due to poor digestibility and the presence of antinutrients. The present study investigates the effect of ultrasound treatment on the nutritional and antinutritional properties of foxtail millet by optimizing three processing parameters: power level (PL), treatment duration (TD), and water-to-grain ratio (W: G). Response Surface Methodology (RSM) and Artificial Neural Network–Genetic Algorithm (ANN–GA) models were employed to predict and optimize process conditions. The RSM-derived optimal parameters were 400&#xa0;W, 6.2&#xa0;min, and 2.85&#xa0;W: G, whereas the ANN-GA optimization yielded 280&#xa0;W, 4&#xa0;min, and 2.4&#xa0;W: G. Ultrasound treatment significantly reduced total condensed tannins (82.17%) and phytates (42%) compared with the untreated control sample, while preserving the major nutrients, including protein, carbohydrate, and ash content. Multivariate analyses (PCA and HCA) revealed strong correlations among nutritional components and confirmed distinct clustering of antinutrients and fiber content, showing their similar effects under the processing conditions. The study demonstrates that ultrasound is an effective, non-thermal, and environmentally sustainable approach to enhance the nutritional bioavailability of foxtail millet, supporting its application in the development of value-added, health-promoting foods.</p>

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Ultrasound-assisted optimization of foxtail millet processing: enhancing nutritional quality and reducing antinutrients through RSM and ANN–GA modeling

  • 期刊论文

  • 发表日期：

    2026年5月14日

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  • Payal Jaysingh,
  • Shagolshem Mukta Singh,
  • Sanjana Gautam,
  • P. Srinivasa Rao

摘要

Foxtail millet (Setaria italica L.) is a nutrient-dense, climate-resilient grain, but its utilization is limited due to poor digestibility and the presence of antinutrients. The present study investigates the effect of ultrasound treatment on the nutritional and antinutritional properties of foxtail millet by optimizing three processing parameters: power level (PL), treatment duration (TD), and water-to-grain ratio (W: G). Response Surface Methodology (RSM) and Artificial Neural Network–Genetic Algorithm (ANN–GA) models were employed to predict and optimize process conditions. The RSM-derived optimal parameters were 400 W, 6.2 min, and 2.85 W: G, whereas the ANN-GA optimization yielded 280 W, 4 min, and 2.4 W: G. Ultrasound treatment significantly reduced total condensed tannins (82.17%) and phytates (42%) compared with the untreated control sample, while preserving the major nutrients, including protein, carbohydrate, and ash content. Multivariate analyses (PCA and HCA) revealed strong correlations among nutritional components and confirmed distinct clustering of antinutrients and fiber content, showing their similar effects under the processing conditions. The study demonstrates that ultrasound is an effective, non-thermal, and environmentally sustainable approach to enhance the nutritional bioavailability of foxtail millet, supporting its application in the development of value-added, health-promoting foods.