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基于几何形态测量学的5种食物淀粉粒分析
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  • 英文篇名:Analysis of five food starch granules based on geometric morphometrics
  • 作者:万智巍 ; 李明启 ; 贾玉连 ; 蒋梅鑫
  • 英文作者:WAN Zhiwei;LI Mingqi;JIA Yulian;JIANG Meixin;Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education,School of Geography and Environment,Jiangxi Normal University;Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences;
  • 关键词:淀粉粒分析 ; 几何形态测量 ; 轮廓线 ; 小波分析
  • 英文关键词:starch granule analysis;;geometric morphometrics;;contour curve;;wavelet analysis
  • 中文刊名:SPFX
  • 英文刊名:Food and Fermentation Industries
  • 机构:江西师范大学地理与环境学院鄱阳湖湿地与流域研究教育部重点实验室;中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室;
  • 出版日期:2019-02-11 14:15
  • 出版单位:食品与发酵工业
  • 年:2019
  • 期:v.45;No.384
  • 基金:国家自然科学基金(41761045);; 江西省自然科学基金(20161BAB213075);; 江西省教育厅科学技术研究项目(GJJ150305);; 江西师范大学博士启动基金(6902)
  • 语种:中文;
  • 页:SPFX201912033
  • 页数:5
  • CN:12
  • ISSN:11-1802/TS
  • 分类号:226-230
摘要
淀粉粒特征是食品鉴定和质量分析的重要依据,利用几何形态测量学方法对小麦、水稻、玉米、土豆和山药这5种主粮和经济作物进行淀粉粒形态分析,测得其形态分别为近圆形、多边形、多边形、椭圆形和椭圆形,平均粒径分别为(19. 90±7. 19)、(5. 08±1. 08)、(11. 40±3. 50)、(11. 94±8. 56)和(21. 86±3. 50)μm。水稻、玉米淀粉粒的轮廓曲线较为曲折,小麦、土豆和山药淀粉粒的较为平滑。小波分析结果显示,除水稻外其余4种食物的淀粉粒均在6~8个角步距尺度表现出显著周期变化。5种食物淀粉粒显示出不同类型的小波谱,分别具有3、5、2、4、3个完整谱值闭合区。主成分分析结果表明2个主成分累积方差为83. 07%,5种淀粉粒在主成分散点图中区分明显。基于以上结果构建出了食物淀粉粒形态鉴定的判别方程。几何形态测量方法可以为不同食物淀粉粒的判别提供定量化的鉴定依据,在食品质量检验方面具有一定的应用潜力。
        The characteristics of starch granules are important for product identification and quality analysis. This study used geometrical morphometrics to analyze wheat,rice,corn,potato,and yam starch granules. The results showed that wheat starch granules had circular shapes with(19. 90 ± 7. 19) μm diameter. Both rice and corn starches had polygonal shapes,and their diameters were(5. 08 ± 1. 08) μm and(11. 40 ± 3. 50) μm,respectively. In addition,the shapes of potato and yam starch granules were elliptical and their diameters were(11. 94 ± 8. 56) μm and(21. 86 ± 3. 50) μm,respectively. The contour curves of rice and corn starch granules were tortuous while other starch granules were smoother. The wavelet analysis showed that except for rice starch granules,other granules all displayed significant periodic variations at 6-8 angular step scales. Moreover,the five food starches showed significantly different wavelet spectra and possessed 3,5,2,4,and 3 complete spectral value closures,respectively. Principal component analysis showed that the cumulative variance of the two main components was 83. 07%,and the five food starch granules clearly distinguished from each other,and a discriminant equation for food starch granule identification was established. In conclusion,geometric morphometrics offers quantitative identification to discriminate different food starch granules,and therefore it has application potential in food quality inspection.
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