Attention-enhanced partial least squares prediction method for sensory evaluation of strong-flavor base liquor

By constructing an attention-enhanced partial least squares prediction model, the problems of subjectivity and insufficient precision in the sensory evaluation of strong-aroma raw liquor were solved, realizing the objective quantification and intelligent management of sensory quality, and improving the accuracy and stability of prediction.

CN122432636APending Publication Date: 2026-07-21ANHUI GUJING DISTILLERY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ANHUI GUJING DISTILLERY CO LTD
Filing Date
2026-04-29
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies for sensory quality evaluation of strong-aroma raw liquor suffer from problems such as strong subjectivity, difficulty in quantification, computational redundancy, and insufficient model prediction accuracy, making it difficult to meet the standardized control and intelligent management of the production process.

Method used

An attention-enhanced partial least squares prediction method for sensory evaluation of strong-aroma raw liquor is adopted. Through residual update and weight recursive optimization mechanism, combined with attention weight allocation module, the intrinsic relationship between flavor characteristics and sensory attributes is explored, and a flavor perception partial least squares regression network is constructed to quantitatively predict sensory attributes.

Benefits of technology

It improves the accuracy and stability of sensory attribute prediction, realizes the objective quantification of sensory evaluation, reduces the impact of individual differences and state fluctuations, and improves the repeatability and comparability of evaluation.

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Abstract

The application discloses a kind of attention enhancement partial least squares prediction methods for strong-flavor base liquor sensory evaluation, comprising the following steps: 1 obtains the concentration data of key flavoring substances of strong-flavor base liquor and derived feature dataset;2 build feature extraction and latent variable iteration module, through residual update and weight recursion, complete multiple rounds of flavor characteristics and sensory attribute association information mining;3 introduce attention weight calculation module, the self-adapting weighting of key latent variable information is carried out, and the focusing ability of model to flavor-sensory relationship is strengthened;4 build loss function and optimize model by stochastic gradient descent algorithm, realize the accurate prediction of strong-flavor base liquor sensory attribute.The method of the application can realize the objective, quantitative prediction of strong-flavor base liquor sensory attribute, and provides stable and reliable technical support for base liquor sensory quality evaluation.
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