Cluster Analysis and Principal Component Analysis of Stickiness Texture of Waxy Corns Based on Multivariate Analysis
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Abstract
Waxy corn has unique eating characterized by its sticky texture when cooked. To classify waxy corn with varying degrees of stickiness, multivariate analysis is needed. This research aimed to apply cluster analysis and principal component analysis (PCA) methods to classify 30 waxy corn lines/varieties/hybrids based on their chemical properties, pasting properties, and
isoamylase activities. The principal component analysis results revealed that the total variance captured was 68.8%, explained by two principal components (PC1 and PC2). PC1 was associated
with gelatinization, while PC2 was correlated with retrogradation. K-means clustering was also used for classification. The waxy corn inbred lines PWHB01 and WTNGB003 and the waxy corn hybrids CNW1602 and CNW1627 were found to display mean values that were close to those of the commercial waxy corn hybrids. Based on cluster analysis, waxy corn lines/varieties/hybrids were categorized into two groups. The first group comprised 13 waxy corn lines/varieties/hybrids that shared similarities in the gelatinization component. The second group included 17 waxy corn lines/varieties/hybrids that were common in retrogradation components or setbacks. The flour from PWHB01, WTNGB003, CNW1602, and CNW1627 had high peak viscosity and breakdown but low setback, with the stickiness texture characters comparable to the check entries. The results obtained in this study should provide important information for further studies on the selection of waxy corn lines/varieties of good eating quality and high consumer acceptance in the future.
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References
American Association of Cereal Chemists. 2000. Approve Methods of the American Association of Cereal Chemists. 10th edition. The American Association of Cereal Chemists, Minnesota, USA.
Azanza, F., B.P. Klein and J.A. Juvik. 1996. Sensory characterization of sweet corn lines differing in physical and chemical composition. J. Food Sci. 61(1): 253–257.
Boonlertnirun, K. and C. Jompuk. 2013. Waxy corn hybrid classification by multivariate analysis. RMUTSB Acad. J. 1(1): 41–50. (in Thai)
Hung, P.V., T. Maeda and N. Morita. 2007. Study on physicochemical characteristics of waxy and high amylose wheat starches in comparison with normal wheat starch. Starch 59(3–4): 125–131.
Inpik, N. and B. Kongsamai. 2019. Genetic diversity of some morphological characteristic of Amorphophallus muelleri Blume using multivariate analysis. JSTKU 8(2): 21–31. (in Thai)
Jane, J., Y.Y. Chen, L.F. Lee, A.E. McPherson, K.S. Wong, M. Radosavljevic and T. Kasemsuwan. 1999. Effects of amylopectin branch length and amylose content on the gelatinization and pasting properties of starch. Cereal Chem. 76(5): 629–637.
Ketthaisong, D., B. Suriharn, R. Tangwongchai, J.L. Jane and K. Lertrat. 2015. Physicochemical and morphological properties of starch from fresh waxy corn kernels. J. Food Sci. Technol. 52(10): 6529–6537.
Kim, W.S. and P.A. Seib. 1993. Apparent restriction of starch swelling in cooked noodles by lipids in some commercial wheat flours. Cereal Chem. 70(4): 367–372.
Li, L., H. Jiang, M. Campbell, M. Blanco and J.L. Jane. 2008. Characterization of maize amyloseextender (ae) mutant starches. Part I: Relationship between resistant starch contents and molecular structures. Carbohydr. Polym. 74(3): 396–404.
Liaotrakoon, W. and V. Liaotrakoon. 2020. Effect of acid modification and pre-gelatinization methods on physicochemical properties of water chestnut flour. JFTSU 15(2): 82–95. (in Thai)
Pérez, S. and E. Bertoft. 2010. The molecular structure of starch component and their contribution to the architecture of starch granules. Starch. 62: 389–420.
Phapumma, A., T. Monkham, J. Sanitchon and S. Chankaew. 2020. Evaluation of amylose content, textural properties and cooking qualities of selected glutinous rice. Khon Kaen Agr. J. 48(3): 597–606. (in Thai)
R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Shittu, T.A., L.O. Sanni, S.O. Awonorin, B. Maziya-Dixon and A. Dixon. 2007. Use of multivariate techniques in studying the flour making properties of some CMD resistant cassava clones. Food Chem. 101(4): 1606–1615.
Simla, S. 2009. Eating Quality Investigation and Improvement in Waxy Corn. PhD Thesis, Khon Kaen University, Khon Kaen.
Simla, S. 2013. Eating quality in vegetable corns. J. Sci. Technol. MSU. 32(3): 337–342.
Simla, S., K. Lertrat and B. Suriharn. 2010. Carbohydrate characters of six vegetable waxy corn varieties as affected by harvest time and storage duration. Asian J. Plant Sci. 9(8): 463–470.
Singh, R.P. and B.A. Anderson. 2004. The major types of food spoilage: an overview, pp. 3–23. In R. Steele, ed. Understanding and Measuring the Shelf-life of Food. Woodhead Publishing, Cambridge, UK.
Syahariza, Z.A., S. Sar, J. Hasjim, M.J. Tizzotti and R.G. Gilbert. 2013. The importance of amylose and amylopectin fine structures for starch digestibility in cooked rice grains. Food Chem. 136(2): 742–749.
Valach, A., G. Moore and E.B. Perry. 2020. Impacts of dietary profile on starch content and gelatinization in canine diets. J. Anim. Sci. 98(Suppl. 4): 56.
Vanichbuncha, K. 2008. Statistics Analysis: Statistics for Management and Research. Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand. 589 pp. (in Thai)
Zhu, L., C. Jones, Q. Guo, L. Lewis, C.R. Stark and S. Alavi. 2016. An evaluation of total starch and starch gelatinization methodologies in pelleted animal feed. J. Anim. Sci. 94(4): 1501–1507.