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采用增量关联马尔可夫毯选择样本描述性成分的顶空气相色谱-质谱法鉴定豆酱的产地。

Identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry by selecting sample-descriptive components with an Incremental Association Markov Blanket.

机构信息

Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul 04763, Republic of Korea.

Department of Chemistry, Chungnam National University, 99 Daehak-Ro, Yuseong-Gu, Daejeon 34134, Republic of Korea.

出版信息

Food Res Int. 2023 Dec;174(Pt 1):113492. doi: 10.1016/j.foodres.2023.113492. Epub 2023 Sep 23.

Abstract

The identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry was attempted in this study. Since soybean paste was odor-rich, 36 components were identified in the imported and domestic soybean samples. t-Test, variable importance in projection (VIP), and Incremental Association Markov Blanket (IAMB) were employed to select proper components that could effectively discriminate the two sample groups. The discrimination accuracies were below 87.3 % when all 36 components were fed for either LDA, k-NN, or SVM. When the five t-test-selected components or six VIP score-selected components were employed, the accuracies improved to 95.2-96.2 %. The IAMB selected three different components were 3-methylbutanal, 4-methylnonane, and 2,3-pentanedione, and the correlations among their peak areas were not significant. This suggests that these three components were independently relevant for the discrimination. The accuracy obtained using these three components was superior, 97.7 %, as undescriptive and/or redundant components for the discrimination were excluded.

摘要

本研究尝试使用顶空气相色谱-质谱法鉴定豆瓣酱的产地。由于豆瓣酱气味浓郁,在进口和国产豆瓣酱样品中鉴定出 36 种成分。t 检验、变量重要性投影(VIP)和增量关联马尔可夫毯(IAMB)用于选择可以有效区分两组样品的合适成分。当将所有 36 种成分输入 LDA、k-NN 或 SVM 时,判别准确率均低于 87.3%。当使用 5 种 t 检验选择的成分或 6 种 VIP 得分选择的成分时,准确率提高到 95.2-96.2%。IAMB 选择了三个不同的成分,分别是 3-甲基丁醛、4-甲基壬烷和 2,3-戊二酮,它们的峰面积之间没有显著相关性。这表明这三个成分是独立相关的。使用这三个成分的准确率更高,为 97.7%,因为排除了对判别无描述性和/或冗余的成分。

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