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基于云模型的电子舌对白酒味觉信息的模糊评价输出。

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model.

机构信息

College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.

Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30602, USA.

出版信息

Sensors (Basel). 2020 Jan 27;20(3):686. doi: 10.3390/s20030686.

Abstract

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

摘要

作为一种味觉仿生系统,电子舌可以用于获取不同类型食物的味觉信息。在此基础上,我们进行了拓展延伸,使其在准确区分样本的能力之外,还能够通过人类般的评价性语言来进行更具表现力的表达。因此,本文展示了味觉仿生系统的定性数字输出与符合人类感知模式的模糊评价语言之间的相关性。首先,通过主成分分析(PCA)、后向云发生器和前向云发生器,利用电子舌采集的酒类味觉数据,建立了不同风味信息的二维云滴群。其次,通过对人工感官评价实验中出现的数据进行计数和分析,得到了不同酒类风味的评价词的频率和顺序。根据词的频率和顺序,计算了云滴群中每个词对应的云滴范围。最后,将源于 8 组具有不同风味的酒类数据的模糊评价与人工感知进行比较,结果表明,本工作所建立的模型能够输出符合人类感知的模糊评价,而不仅仅是数字输出。总之,该方法使电子舌系统能够生成符合人类描述性语言的输出,使食品检测技术更接近人类感知。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2489/7038490/b310b0dfe0ab/sensors-20-00686-g0A1.jpg

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