LEI Jing, WAN Zhiwei, JU Min. Recognition and Identification of Coarse Grain Starch Based on Morphometric and Canonical Correspondence Analysis[J]. Science and Technology of Food Industry, 2021, 42(14): 289−295. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021020059.
Citation: LEI Jing, WAN Zhiwei, JU Min. Recognition and Identification of Coarse Grain Starch Based on Morphometric and Canonical Correspondence Analysis[J]. Science and Technology of Food Industry, 2021, 42(14): 289−295. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021020059.

Recognition and Identification of Coarse Grain Starch Based on Morphometric and Canonical Correspondence Analysis

  • In this paper, morphometric parameters and multivariate statistical analysis had been used to study the starch grain morphology of 8 common coarse grain starch in China. The results showed that different types of coarse grain starch granules exhibit certain differences in overall morphology and particle size. The starch graunles of sorghum, barley, millet and buckwheat were polygonal, with average diameters of 18.931, 13.499, 13.482 and 7.194 μm. Starch grains of pea, mung bean and lentil were irregularly elliptical, with average particle sizes of 24.008, 23.895 and 21.001 μm. Oat starch grains were semi-elliptical, with an average particle size of 4.861 μm. The contour curve of the polygonal starch grains of cereals had obvious broken lines and sawtooth, and the closed area of wavelet spectrum was more complicated. The contour line of bean starch grains had the characteristic of fluctuating curve, and the closed area of wavelet spectrum was more regular. The canonical correspondence analysis results showed that there were certain differences between different influencing factors. The factors representing the average particle size, standard deviation of particle size, Hu invariant moment 3, extrusion surface and Hu invariant moment 1 had the longest arrow length maybe the main factors leading to the difference of starch granule morphology. CCA could provide selection criteria of influencing factors for the identification of different starch granules.
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