Manzoor Muhammad Faisal, Hussain Abid, Naumovski Nenad, Ranjha Muhammad Modassar Ali Nawaz, Ahmad Nazir, Karrar Emad, Xu Bin, Ibrahim Salam A
School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.
Department of Agriculture and Food Technology, Faculty of Life Science, Karakoram International University, Gilgit-Baltistan, Pakistan.
Front Nutr. 2022 Jul 19;9:901342. doi: 10.3389/fnut.2022.901342. eCollection 2022.
Anthocyanins (ACNs) are plant polyphenols that have received increased attention recently mainly due to their potential health benefits and applications as functional food ingredients. This has also created an interest in the development and validation of several non-destructive techniques of ACN assessments in several food samples. Non-destructive and conventional techniques play an important role in the assessment of ACNs in agricultural and food products. Although conventional methods appear to be more accurate and specific in their analysis, they are also associated with higher costs, the destruction of samples, time-consuming, and require specialized laboratory equipment. In this review article, we present the latest findings relating to the use of several spectroscopic techniques (fluorescence, Raman, Nuclear magnetic resonance spectroscopy, Fourier-transform infrared spectroscopy, and near-infrared spectroscopy), hyperspectral imaging, chemometric-based machine learning, and artificial intelligence applications for assessing the ACN content in agricultural and food products. Furthermore, we also propose technical and future advancements of the established techniques with the need for further developments and technique amalgamations.
花青素（ACNs）是一类植物多酚，近年来受到越来越多的关注，主要是因其潜在的健康益处以及作为功能性食品成分的应用。这也引发了人们对开发和验证多种食品样品中ACN无损评估技术的兴趣。无损技术和传统技术在农产品和食品中ACN的评估中发挥着重要作用。尽管传统方法在分析中似乎更准确、更具特异性，但它们也存在成本较高、会破坏样品、耗时且需要专业实验室设备等问题。在这篇综述文章中，我们介绍了有关使用多种光谱技术（荧光、拉曼、核磁共振光谱、傅里叶变换红外光谱和近红外光谱）、高光谱成像、基于化学计量学的机器学习以及人工智能应用来评估农产品和食品中ACN含量的最新研究结果。此外，我们还提出了现有技术的技术改进和未来发展方向，以及进一步发展和技术融合的必要性。
