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感官评分预测和关键芳香化合物的表征在发酵的切碎胡

Yuan Liu¹, Lingyan Zhao¹, Chunya Yang¹

¹College of Food Science and Technology, Hunan Agricultural University, Changsha, Hunan 410128, PR China.

Food chemistry: X

|July 21, 2025

概括

这项研究使用电子鼻子 (E-nose) 和机器学习来预测发酵切碎胡 (FCP) 的感觉得分. 随机森林模型准确地识别了FCP中的关键芳香化合物,提高了质量控制.

科学领域:

食品科学食品科学食品科学
分析化学分析化学
感官科学感官科学

背景情况:

发酵切碎胡 (FCP) 具有复杂的香味特征,使得感官评分预测和关键香味化合物识别变得困难.
精确的感官评估和对香气的理解有助于FCP质量控制和产品开发.

研究的目的:

应用电子鼻子 (E-nose) 结合机器学习来预测FCP感官分数.
确定有助于高质量的FCP的感觉特征的关键芳香化合物.

主要方法:

使用电子鼻子 (E-nose) 与机器学习算法 (随机森林,SVM,MLR,BPNN) 结合,用于感官得分预测.
采用GC×GC-O-Q-TOF-MS来分析挥发性化合物和识别FCP中的气味活性化合物.
进行了芳香重组和省略实验,以验证关键的芳香贡献者.

主要成果:

随机森林 (RF) 在测试的机器学习模型中实现了FCP感觉得分的最高预测准确度.
GC×GC-O-Q-TOF-MS在表现最好的FCP样本 (FCP-1) 中发现了97种挥发性化合物和19种气味活性化合物.
34种化合物的气味活性值 (OAV) 大于1,表明有显著的感官影响.

结论:

电子鼻子和射频的组合提供了一个有效的方法来预测FCP感官得分.
关键的芳香化合物,包括林纳醇,乙醇和β-damascenone,被确定为FCP-1的芳香特征的关键贡献者.
这项研究为控制FCP香味质量和开发新发酵产品提供了宝贵的见解.

关键词:

3 - 异布-2-甲氧化皮拉 (PubChem) 是一种 4 - 乙基 (PubChem) 是一种 (CID: 18635) 这是一个很好的例子.CID: 24020) 的情况.(CID: 31242) 这是一个很好的例子.(CID: 32594) 这是一个很好的例子.(CID: 5362583) 这是一个非常重要的数字.(CID: 6054) 这是一个很好的例子.(CID: 61953) 这是一个很好的例子.(CID: 638014) 已经开始使用.(CID:17100) 在美国.乙基2-甲基酸盐 (PubChem) 是一种乙基转-4-脱酸盐 (PubChem) 是一种发酵的切碎的胡.关键的芳香化合物关键的芳香化合物林纳 (PubChem CID: 6549) 的使用情况机器学习方法机器学习方法甲醇 (PubChem) 的使用情况气味活动值的气味活动值.乙醇 (PubChem) 是一种预测预测预测螺旋氧化物 (PubChem) 是一种 α-Terpineol (PubChem公司提供)乙-离子体 (PubChem) 是一种

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