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International Journal of Agriculture and Food Science

Vol. 5, Issue 2, Part A (2023)

Physicochemical and sensory quality evaluation of dried noodles from Chinese market based on principal component analysis and cluster analysis

Author(s):

Siran Zhang

Abstract:

In order to explore the quality diversity of different dried noodle products in Chinese market and construct an evaluation model, this study investigated physicochemical and sensory properties of nine commercially dried noodles, after which principal component analysis (PCA) and cluster analysis were applied to investigate interrelations and differences of various noodle samples, and sensory parameters of noodles were evaluated in addition. Results of physicochemical parameters of each sample, including moisture content (64.08~70.20%), protein content (0.139~0.607 g/100 g), elongation (13.78~37.49%), tension (18.33~38.67 g), chewiness (32.2~50.2 times), firmness (0.247~0.663 N), L*(from 87.013~92.290), a* (-2.820~-0.503), and b*(11.837~25.713) were calculated through factor analysis, where 4 main components were extracted with a cumulative contribution rate of 94.048%, reflecting comprehensive quality of the dried noodles. Quality scores Y of nine dried noodles obtained through a series of operations based on PCA were compared with those of sensory evaluations, which presented an appreciable consistent trend and to some extent reflects the reliability and effectiveness of the experiment. Cluster analysis divided nine brands of noodle into four categories, with similar quality within each category. This study shows that using general physicochemical parameters analysis and PCA to establish a quality evaluation model can effectively reflect the quality of dry noodles, and has certain application and reference value.

Pages: 28-36  |  350 Views  101 Downloads


International Journal of Agriculture and Food Science
How to cite this article:
Siran Zhang. Physicochemical and sensory quality evaluation of dried noodles from Chinese market based on principal component analysis and cluster analysis. Int. J. Agric. Food Sci. 2023;5(2):28-36. DOI: 10.33545/2664844X.2023.v5.i2a.143
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