Amoriello Tiziana, Mellara Francesco, Amoriello Monica, Ciccoritti Roberto
CREA Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy.
CREA Central Administration, Via Archimede 59, 00197 Rome, Italy.
Foods. 2024 Jul 19;13(14):2277. doi: 10.3390/foods13142277.
In recent years, the growing demand for algae in Western countries is due to their richness in nutrients and bioactive compounds, and their use as ingredients for foods, cosmetics, nutraceuticals, fertilizers, biofuels,, etc. Evaluation of the qualitative characteristics of algae involves assessing their physicochemical and nutritional components to determine their suitability for specific end uses, but this assessment is generally performed using destructive, expensive, and time-consuming traditional chemical analyses, and requires sample preparation. The hyperspectral imaging (HSI) technique has been successfully applied in food quality assessment and control and has the potential to overcome the limitations of traditional biochemical methods. In this study, the nutritional profile (proteins, lipids, and fibers) of seventeen edible macro- and microalgae species widely grown throughout the world were investigated using traditional methods. Moreover, a shortwave infrared (SWIR) hyperspectral imaging device and artificial neural network (ANN) algorithms were used to develop multi-species models for proteins, lipids, and fibers. The predictive power of the models was characterized by different metrics, which showed very high predictive performances for all nutritional parameters (for example, R = 0.9952, 0.9767, 0.9828 for proteins, lipids, and fibers, respectively). Our results demonstrated the ability of SWIR hyperspectral imaging coupled with ANN algorithms in quantifying biomolecules in algal species in a fast and sustainable way.
近年来，西方国家对藻类的需求不断增长，这是因为藻类富含营养物质和生物活性化合物，且可作为食品、化妆品、营养保健品、肥料、生物燃料等的原料。对藻类定性特征的评估包括对其物理化学和营养成分进行评估，以确定其是否适合特定的最终用途，但这种评估通常采用具有破坏性、成本高且耗时的传统化学分析方法，并且需要进行样品制备。高光谱成像（HSI）技术已成功应用于食品质量评估与控制，并且有潜力克服传统生化方法的局限性。在本研究中，使用传统方法对全世界广泛种植的17种可食用大型和微型藻类的营养成分（蛋白质、脂质和纤维）进行了研究。此外，使用短波红外（SWIR）高光谱成像设备和人工神经网络（ANN）算法建立了蛋白质、脂质和纤维的多物种模型。通过不同指标对模型的预测能力进行了表征，结果表明这些模型对所有营养参数均具有非常高的预测性能（例如，蛋白质、脂质和纤维的R分别为0.9952、0.9767和0.9828）。我们的结果证明了短波红外高光谱成像结合人工神经网络算法能够快速且可持续地定量分析藻类中的生物分子。
