Unlocking the potential of odor-induced sugar reduction: An updated review of the underlying mechanisms, substance selections, and technical methodologies

Lin Zhu,Fei Pan, Felix Stöppelmann,Jiaqi Liang,Dan Qin, Can Xiang,Marina Rigling, Lea Hannemann,Tim Wagner,Youfeng Zhang,Yanyan Zhang

Trends in Food Science & Technology(2024)

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摘要
Background In the context of increasing global health risks, including obesity, diabetes, and cardiovascular diseases, odor-induced taste enhancement (OITE) has emerged as an important supplement to other sugar reduction strategies, offering a promising way to reduce sugar intake. OITE can be used in conjunction with other sugar reduction methods, and unlike some artificial sweeteners, it does not cause unpleasant aftertastes of bitterness and metallic flavors. However, compared to other approaches, the efficiency of OITE in sugar reduction appears relatively modest. Scope and approach This review aimed to discuss OITE with a focus on the underlying mechanisms, material selection, and innovative techniques. Current explanations of OITE, such as associative learning and natural association, indicate that it can be either acquired or inherent. We also summarized previous strategies for material selection and provided extensive databases of odorants and sweeteners for alternative selections. Finally, we reviewed some technical methodologies used in OITE studies, including experimental and data mining techniques. Key findings and conclusions Broadening the range of potential odorants and sweeteners is essential for improving the sweetening potency of odorants. Caution should be exercised with the use of olfactoscan, as this technique does not actually involve tasting. Emerging technologies, such as machine learning, have significant potential to further increase the efficacy of OITE. As the sweetening efficiency of OITE increases, this strategy will substantially benefit the food industry and human health in the foreseeable future.
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关键词
Odor-induced taste enhancement (OITE),Sugar reduction,Associative learning,Natural association,Olfactoscan,Machine learning
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