Viejo Claudia Gonzalez, Torrico Damir D, Dunshea Frank R, Fuentes Sigfredo
School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.
Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand.
Foods. 2019 Nov 20;8(12):596. doi: 10.3390/foods8120596.
Quality control, mainly focused on the assessment of bubble and foam-related parameters, is critical in carbonated beverages, due to their relationship with the chemical components as well as their influence on sensory characteristics such as aroma release, mouthfeel, and perception of tastes and aromas. Consumer assessment and acceptability of carbonated beverages are mainly based on carbonation, foam, and bubbles, as a flat carbonated beverage is usually perceived as low quality. This review focuses on three beverages: beer, sparkling water, and sparkling wine. It explains the characteristics of foam and bubble formation, and the traditional methods, as well as emerging technologies based on robotics and computer vision, to assess bubble and foam-related parameters. Furthermore, it explores the most common methods and the use of advanced techniques using an artificial intelligence approach to assess sensory descriptors both for descriptive analysis and consumers' acceptability. Emerging technologies, based on the combination of robotics, computer vision, and machine learning as an approach to artificial intelligence, have been developed and applied for the assessment of beer and, to a lesser extent, sparkling wine. This, has the objective of assessing the final products quality using more reliable, accurate, affordable, and less time-consuming methods. However, despite carbonated water being an important product, due to its increasing consumption, more research needs to focus on exploring more efficient, repeatable, and accurate methods to assess carbonation and bubble size, distribution and dynamics.
质量控制主要侧重于对与气泡和泡沫相关参数的评估，在碳酸饮料中至关重要，因为它们与化学成分相关，以及对诸如香气释放、口感和味道及香气感知等感官特性有影响。消费者对碳酸饮料的评估和接受度主要基于碳酸化、泡沫和气泡，因为平淡的碳酸饮料通常被认为质量较低。本综述聚焦于三种饮料：啤酒、苏打水和起泡葡萄酒。它解释了泡沫和气泡形成的特征，以及评估与气泡和泡沫相关参数的传统方法，以及基于机器人技术和计算机视觉的新兴技术。此外，它探讨了最常见的方法以及使用人工智能方法的先进技术的应用，以评估用于描述性分析和消费者接受度的感官描述词。基于机器人技术、计算机视觉和机器学习相结合作为人工智能方法的新兴技术已被开发并应用于啤酒评估，在较小程度上也用于起泡葡萄酒评估。其目的是使用更可靠、准确、经济且耗时更少的方法来评估最终产品质量。然而，尽管苏打水是一种重要产品，且其消费量不断增加，但更多研究需要聚焦于探索更高效、可重复且准确的方法来评估碳酸化以及气泡大小、分布和动态。
