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乌玛米转换器:用于高精度预测和实验验证乌玛米的深度学习框架

Baifeng Fu¹, Min Fan², Junjie Yi²

¹Key Laboratory of Food Nutrition and Health of Liaoning Province, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China; SKL of Marine Food Processing & Safety Control, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China.

|August 23, 2025

概括

一个新的人工智能模型,Umami-Transformer,可以准确地识别umami. 合成的像DDE和DDED表现出优越的度和度,在食品中提供成本效益优化的味道.

科学领域:

食品科学
计算化学
人工智能

背景情况:

鉴定和评估乌玛米的传统方法是低效和主观的.
开发客观有效的方法对于食品科学和味道优化至关重要.

研究的目的:

开发一个先进的计算模型来预测和识别乌玛米.
通过感官和仪器分析合成和验证预测的乌玛米.
阐明中乌玛米味觉的结构基础.

主要方法:

将变压器架构与八个物理化学描述符集成,以创建Umami-变压器模型.
在中检查二到五以预测乌玛米.
预测得分最高的的合成和感官评估 (DD,DDE,DDED,DDEDD).
使用电子舌头分析和分子对接研究进行验证.

主要成果:

乌玛米转换器实现了高预测准确度 (0.965),F1得分 (0.903) 和马修斯相关系数 (0.889).
合成的DDE和DDED (1 mg/mL) 显示出显著的和盐度,超过单谷氨酸 (3 mg/mL).
分子对接显示,末的Asp/Glu残留增强了乌玛米受体相互作用.

结论:

乌玛米转换器提供了一种高效准确的乌玛米识别方法.
这项研究成功地将理论预测与实际食品应用相结合,从而实现了成本效益优化的味道.
了解特定氨基酸残留的作用有助于设计新型风味化合物.

关键词:

深度学习,变压器机器学习分子对接乌玛米

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