Literature DB >> 30488614

Comparison between quantitative descriptive analysis and flash profile in profiling the sensory properties of commercial red sufu (Chinese fermented soybean curd).

Wenmeng He1, Hau Yin Chung1.   

Abstract

BACKGROUND: Red sufu is a type of sufu produced by solid-state fermentation of soybean curd and coloration with red mold rice. The purposes of this study were: (i) to characterize commercial red sufu samples using the quantitative descriptive analysis (QDA) and flash profile (FP) by ten trained and ten untrained panelists, respectively; (ii) to compare the differences in panel performance, descriptive abilities and sensory maps between the two methodologies; and (iii) to compare the efficiency between QDA and FP using red sufu as the matrix. Techniques in multivariate analysis were utilized to explore the data.
RESULTS: Results from generalized procrustes analysis (GPA) showed that panel performance by QDA was more repeatable and reached higher homogeneity than that by FP. Despite the confidence ellipse results of the 12 red sufus being better discriminated by QDA, the RV coefficient was high (RV = 0.852) between the configurations of the two-dimensional model (F1 and F2) of the two methodologies, indicating that the two methods are similar and closely related. Overall, QDA provided more accurate and detailed information, while FP provided a similar sensory map on product location and descriptive results.
CONCLUSION: The FP technique appeared to be an efficient alternative approach to quickly evaluate sensory properties, including appearance, flavor, aroma and textural properties of an array of red sufu products.
© 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Entities:  

Keywords:  RV coefficient; confidence ellipse; flash profile; generalized procrustes analysis; principal component analysis; quantitative descriptive analysis

Mesh:

Year:  2019        PMID: 30488614     DOI: 10.1002/jsfa.9516

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  2 in total

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Journal:  J Food Sci Technol       Date:  2022-05-06       Impact factor: 3.117

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Journal:  Foods       Date:  2021-05-28
  2 in total

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