Differentiation of Aroma Characteristics of Pine-Mushrooms (Tricholoma matsutake Sing.) of Different Grades Using Gas Chromatography−Olfactometry and Sensory Analysis

Two independent approaches, gas chromatography−olfactometry and sensory analysis, were used to evaluate and compare the aroma characteristics of pine-mushrooms (Tricholoma matsutake Sing.) of four different grades. The aroma-active compounds responsible for the sensory attributes of pine- mushrooms...

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Published inJournal of agricultural and food chemistry Vol. 55; no. 6; pp. 2323 - 2328
Main Authors Cho, In Hee, Lee, Soh Min, Kim, Se Young, Choi, Hyung-Kyoon, Kim, Kwang-Ok, Kim, Young-Suk
Format Journal Article
LanguageEnglish
Published Washington, DC American Chemical Society 21.03.2007
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Summary:Two independent approaches, gas chromatography−olfactometry and sensory analysis, were used to evaluate and compare the aroma characteristics of pine-mushrooms (Tricholoma matsutake Sing.) of four different grades. The aroma-active compounds responsible for the sensory attributes of pine- mushrooms were investigated based on the correlation between instrumental and sensory analyses through partial least-square regression. Piny, meaty, and floral attributes were strongly correlated with each other and were the most important descriptors for defining the pine-mushrooms of the highest grade, and they decreased as the grade decreased. Among 23 aroma-active compounds, (E)-2-decenal, α-terpineol, phenylethyl alcohol, and 2-methylbutanoic acid ethyl ester contributed most to these attributes. On the other hand, the major aroma characteristics of the pine-mushrooms of the lowest grade were wet soil-like, alcohol, metallic, moldy, and fermented, and they decreased as the grade increased. These aroma characteristics were strongly associated with 1-octen-3-one, 1-octen-3-ol, 3-octanol, 3-octanone, (E)-2-octen-1-ol, and methional. Keywords: Pine-mushroom (Tricholoma matsutake Sing.); instrument-sensory correlations; GC-O; quantitative descriptive analysis (QDA); partial least-square regression (PLSR)
Bibliography:http://dx.doi.org/10.1021/jf062702z
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ISSN:0021-8561
1520-5118
DOI:10.1021/jf062702z