Research method of establishing intelligent smell judging models for honey quality differences

Abstract

The invention discloses a research method of establishing intelligent smell judging models for honey quality differences, and is characterized in that the intelligent smell judging model for five kinds of honey quality differences are established by adopting a linear mode identification method and a nonlinear mode identification method, and the intelligent smell judging models consist of (1) a nectar source difference model, (2) a production place difference model, (3) a different storage period model, (4) an adulteration judging model and (5) a fake identification model. Through comparing and finding a best intelligent smell judging model for the different honey class differences, and identifying an effect by mining differentiated information, intelligent smell representation of honey odor characteristics is realized.

Description

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AI Technical Summary

Benefits of technology

This patented describes an algorithm called LFEC which helps identify changes made by smoking certain chemical compounds like caffeine or nicotianamic acid. By analyzing these modifications over multiple times, we aimed at finding specific ones associated with each odour change. These identified patterns were then compared against existing databases stored previously to see if they could have any impact on human taste perception. Based upon their similarity, it was found how well the algorithms worked together to accurately detect similarities across various flavors. Overall, our study provides technical means towards understanding and improving the accuracy of identifying harmful chemicals within sensory data collected through experiments conducted on animal behavior models.

Problems solved by technology

This patents describes how sensors like electronics may help identify harmful chemical compounds in consumer goods during manufacturing processes. These devices measure odour differences based upon their specific composition but they often require complex signal processings and data manipulation techniques before identifying any potentially harmful molecules. To address this problem, we propose developing smart atomoscope systems with improved accuracy and efficiency over existing technologies.

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