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Confocal Laser Scanning Microscopy and Image Analysis for Elucidating Crumb and Crust Microstructure of Bran-Enriched South African Fried Dough and Batter

机译:共焦激光扫描显微镜和图像分析以阐明富含麸皮的南非油炸面团和面糊的面包屑和硬皮微观结构

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摘要

A double staining protocol for image acquisition using confocal microscopy (CLSM) coupled with image analysis was employed to elucidate the crust and cross-sectional properties of fried dough. Penetrated oil by image analysis (POia), porosity and pore features were quantified from the cross-section micrographs. Crust surface roughness was measured using fractal metrics and fat content was determined by solvent extraction using the American Association of Cereal Chemists method. Crumb porosity ranged between 54.94%–81.84% and reduced ( < 0.05) with bran addition. Crumb pore sizes ranged from 0–475 µm with <1 circularity, indicating elliptical shape. POia values were notably higher ( < 0.05) than PO by Soxhlet extraction (POsox), except for wheat bran (WB) fried dough where the values of POia and POsox were closely ranked. The linear effect of initial moisture content and bran concentration showed a significant impact on the image properties. The mean fractal dimension (FD) decreased as initial moisture increased. The addition of WB caused a significant reduction in the FD of fried dough, while the opposite effect was noted for its oat bran counterpart. Due to non-collinearity of image properties (FD, POia and porosity), data were fitted to cubic polynomial regression with R values > 0.70. CLSM and image analysis were effective in measuring oil absorption and interpreting crumb properties of fried dough. The protocol used in this study can be applied to other thick deep-fried foods for qualitative observation and quantitative measurement of a specific physical or chemical property.
机译:使用共聚焦显微镜(CLSM)与图像分析相结合的双重染色方案来获取图像,以阐明油炸面团的结皮和横截面特性。通过图像分析(POia)分析渗透油,从横截面显微照片定量孔隙率和孔特征。使用分形度量标准来测量外壳表面粗糙度,并使用美国谷物化学家协会方法通过溶剂萃取来确定脂肪含量。面包糠的孔隙率在54.94%–81.84%之间,并随着麸皮的添加而降低(<0.05)。碎屑的孔径范围为0–475 µm,圆形度小于1,表示椭圆形。除小麦麸(WB)油炸面团中POia和POsox的值排名靠外,POia值显着高于(<0.05)的索氏提取法(POsox)。初始水分含量和麸皮浓度的线性效应显示出对图像性能的显着影响。随着初始水分的增加,平均分形维数(FD)降低。 WB的添加导致油炸面团的FD显着降低,而其燕麦麸对应物的效果却相反。由于图像属性(FD,POia和孔隙率)的非共线性,因此将数据拟合为R值> 0.70的三次多项式回归。 CLSM和图像分析可有效地测量吸油率并解释油炸面团的面包屑特性。这项研究中使用的协议可以应用于其他浓稠油炸食品,以进行定性观察和定量测量特定物理或化学性质。

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