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Zhao Q, Ye Z, Deng Y, Chen J, Chen J, Liu D, Ye X, Huan C. An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception. Compr Rev Food Sci Food Saf 2024; 23:e13327. [PMID: 38517017 DOI: 10.1111/1541-4337.13327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/19/2024] [Accepted: 03/01/2024] [Indexed: 03/23/2024]
Abstract
Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.
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Affiliation(s)
- Qian Zhao
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Zhiyue Ye
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Yong Deng
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
| | - Jin Chen
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
| | - Jianle Chen
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Donghong Liu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Xingqian Ye
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Cheng Huan
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Research Center of Intelligent Food Technology and Equipment, Fuli Institute of Food Science, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Zhejiang University, Hangzhou, China
- Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
- Zhongyuan Institute, Zhejiang University, Zhengzhou, China
- Ningbo Innovation Center, Zhejiang University, Ningbo, China
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Dai C, Peng Z, Wang L, Song T, Xu L, Xu M, Shao Y. Total sleep deprivation reduces the table tennis anticipation performance of young men: A functional magnetic resonance imaging study. iScience 2023; 26:107973. [PMID: 37822501 PMCID: PMC10562798 DOI: 10.1016/j.isci.2023.107973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/31/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023] Open
Abstract
This study explored whether and how sleep deprivation (SD) affects sport-related anticipation. Twenty table tennis players and 28 non-athletes completed a table tennis anticipation task before and after 36 h SD. Functional magnetic resonance imaging (fMRI) data were acquired simultaneously. The results showed that, compared with the non-athletes, table tennis players had higher neural efficiency, manifested by their higher anticipation accuracy and lower frontal lobe activation. SD impaired anticipation performance, accompanied by decreased activation of the occipital and temporal lobes. Compensatory activation occurred in the left hippocampus and orbital part of the right inferior frontal gyrus (IFG) after SD in the table tennis player group, but not in the non-athlete group. The decreased accuracy of non-athletes was positively correlated with decreased activation of orbital part of the right IFG. This study's findings improve the understanding of the cognitive neuroscience mechanisms by which SD affects sport-related anticipation.
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Affiliation(s)
- Cimin Dai
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Letong Wang
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Tao Song
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Lin Xu
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Mengmeng Xu
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing 100084, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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