多种气味及滋味化合物共同决定了酵母抽提物（YE）独特的风味特性,进一步决定了不同 YE 的应用特性。利用GC-O-MS、HPLC技术,对市场上的6款商品化 YE 中的挥发性化合物及不挥发性化合物（糖、有机酸、氨基酸、呈味核苷酸、肽）进行了全...多种气味及滋味化合物共同决定了酵母抽提物（YE）独特的风味特性,进一步决定了不同 YE 的应用特性。利用GC-O-MS、HPLC技术,对市场上的6款商品化 YE 中的挥发性化合物及不挥发性化合物（糖、有机酸、氨基酸、呈味核苷酸、肽）进行了全定量分析；随后,结合气味及滋味感官鉴评,评价不同种类风味化合物对YE感官知觉的影响。最后,对不同商品化酵母抽提物的应用特性进行评价和研究。结果发现：挥发性化合物方面,不同厂家生产的YE在气味活性化合物方面具有较大的差异。吡嗪类主要贡献了YE中对肉味有贡献的烤香香韵。而3-甲基丁醛、乙酸和酮类分别提供了 YE 的焦苦味、酸味和奶油味。不同香韵的分布还表明酵香可能是青香/焦苦香、烤香/坚果香/甜香之间的复合风味。不挥发化合物方面,糖类、大部分氨基酸类、核苷酸类、除琥珀酸、乳酸的有机酸类在1% YE 用量下TAV值均小于1,未单独对YE溶液滋味做出贡献。对不挥发物的相关性分析表明,300～2000 Da 分布肽段对浓厚滋味具有贡献,而Glu主要贡献了YE鲜味,且鲜味与浓厚味有正相关作用。Ala可以贡献YE甜味,而一系列的苦味氨基酸与苦味具有很强烈的正相关作用。另外,苦味主要与鲜味、浓厚味、酸味有负相关关系,表明了苦味与这些滋味具有此消彼长的关系。展开更多
High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional dat...High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.展开更多
文摘多种气味及滋味化合物共同决定了酵母抽提物（YE）独特的风味特性,进一步决定了不同 YE 的应用特性。利用GC-O-MS、HPLC技术,对市场上的6款商品化 YE 中的挥发性化合物及不挥发性化合物（糖、有机酸、氨基酸、呈味核苷酸、肽）进行了全定量分析；随后,结合气味及滋味感官鉴评,评价不同种类风味化合物对YE感官知觉的影响。最后,对不同商品化酵母抽提物的应用特性进行评价和研究。结果发现：挥发性化合物方面,不同厂家生产的YE在气味活性化合物方面具有较大的差异。吡嗪类主要贡献了YE中对肉味有贡献的烤香香韵。而3-甲基丁醛、乙酸和酮类分别提供了 YE 的焦苦味、酸味和奶油味。不同香韵的分布还表明酵香可能是青香/焦苦香、烤香/坚果香/甜香之间的复合风味。不挥发化合物方面,糖类、大部分氨基酸类、核苷酸类、除琥珀酸、乳酸的有机酸类在1% YE 用量下TAV值均小于1,未单独对YE溶液滋味做出贡献。对不挥发物的相关性分析表明,300～2000 Da 分布肽段对浓厚滋味具有贡献,而Glu主要贡献了YE鲜味,且鲜味与浓厚味有正相关作用。Ala可以贡献YE甜味,而一系列的苦味氨基酸与苦味具有很强烈的正相关作用。另外,苦味主要与鲜味、浓厚味、酸味有负相关关系,表明了苦味与这些滋味具有此消彼长的关系。
基金supported by National Natural Science Foundation of China(Grant Nos.11401497 and 11301435)the Fundamental Research Funds for the Central Universities(Grant No.T2013221043)+3 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,the Fundamental Research Funds for the Central Universities(Grant No.20720140034)National Institute on Drug Abuse,National Institutes of Health(Grant Nos.P50 DA036107 and P50 DA039838)National Science Foundation(Grant No.DMS1512422)The content is solely the responsibility of the authors and does not necessarily represent the official views of National Institute on Drug Abuse, National Institutes of Health, National Science Foundation or National Natural Science Foundation of China
文摘High-dimensional data have frequently been collected in many scientific areas including genomewide association study, biomedical imaging, tomography, tumor classifications, and finance. Analysis of highdimensional data poses many challenges for statisticians. Feature selection and variable selection are fundamental for high-dimensional data analysis. The sparsity principle, which assumes that only a small number of predictors contribute to the response, is frequently adopted and deemed useful in the analysis of high-dimensional data.Following this general principle, a large number of variable selection approaches via penalized least squares or likelihood have been developed in the recent literature to estimate a sparse model and select significant variables simultaneously. While the penalized variable selection methods have been successfully applied in many highdimensional analyses, modern applications in areas such as genomics and proteomics push the dimensionality of data to an even larger scale, where the dimension of data may grow exponentially with the sample size. This has been called ultrahigh-dimensional data in the literature. This work aims to present a selective overview of feature screening procedures for ultrahigh-dimensional data. We focus on insights into how to construct marginal utilities for feature screening on specific models and motivation for the need of model-free feature screening procedures.
