PDF(Pubmed)
Abstract：
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, R2 = 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.
摘要：
近年来,西方国家对藻类日益增长的需求是由于它们富含营养物质和生物活性化合物，以及它们作为食物成分的用途，化妆品,营养食品，肥料,生物燃料,,等。评估藻类的定性特征包括评估其理化和营养成分，以确定其对特定最终用途的适用性，但是这种评估通常是使用破坏性的，贵,和耗时的传统化学分析，并且需要样品制备。高光谱成像（HSI）技术已成功应用于食品质量评估和控制，并有可能克服传统生化方法的局限性。在这项研究中,营养成分(蛋白质，脂质，和纤维）使用传统方法研究了全世界广泛生长的17种可食用大型和微藻物种。此外，短波红外（SWIR）高光谱成像设备和人工神经网络（ANN）算法用于开发蛋白质的多物种模型，脂质，和纤维。模型的预测能力由不同的指标来表征，对所有营养参数显示出非常高的预测性能(例如，蛋白质的R2=0.9952，0.9767，0.9828，脂质，和纤维，分别)。我们的结果表明，SWIR高光谱成像与ANN算法相结合，能够以快速，可持续的方式量化藻类中的生物分子。
