Zalidis Achilleas Panagiotis, Tsakiridis Nikolaos, Zalidis George, Mourtzinos Ioannis, Gkatzionis Konstantinos
Laboratory of Consumer and Sensory Perception of Food & Drinks, Department of Food Science and Nutrition, University of the Aegean, Metropolite Ioakeim 2, 81400 Myrina, Greece.
Laboratory of Remote Sensing, Spectroscopy and Geographic Information Systems (GIS), School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Foods. 2025 Jul 29;14(15):2663. doi: 10.3390/foods14152663.
Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis-NIR-SWIR) spectroscopy (350-2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000-2500 nm and 1400-2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control.
功能性面粉富含生物活性化合物，在消费者对替代成分的需求以及小麦粉营养局限性的推动下，受到了越来越多的关注。本研究利用可见、近红外和短波红外（Vis-NIR-SWIR）光谱（350-2500nm）并结合机器学习（ML）算法，探索了各种功能性面粉中酚类化合物的热稳定性。采用随机森林模型根据面粉类型、烘焙温度和酚类浓度对样品进行分类。整个光谱范围产生了较高的分类准确率（分别为0.98、0.98和0.99），一个可解释性框架揭示了与每个类别最相关的波长。为了解决将颜色作为一个混杂因素的问题，通过依次排除可见区域实施了有针对性的光谱细化。在1000-2500nm和1400-2500nm范围内训练的模型准确率略有下降，这表明分类并非完全由可见特征驱动。结果表明，豆类和小麦粉在温和的热条件下保留了较高的总酚含量（TPC），而葡萄籽粉（GSF）和橄榄石粉（OSF）即使在高温下也表现出显著的TPC热稳定性。这些初步发现表明，所提出的无损光谱方法能够对功能性面粉进行快速分类和质量评估，为未来在精准食品配方和质量控制中的应用提供支持。
