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食品研究与开发:2021,42(16):29-36
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物料加水量对重组米品质的影响及其评价模型的研究
(华南农业大学食品学院,广东省食品质量安全重点实验室,广东 广州 510642)
Establishment of A Model to Evaluate the Effects of Water Quantity on Recombinant Rice Quality
(College of Food Science,South China Agricultural University,Guangdong Provincial Key Laboratory of Food Quality and Safety,Guangzhou 510642,Guangdong,China)
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投稿时间:2020-10-23    
中文摘要: 以大米粉为原料,采用螺杆挤压技术,研究不同物料加水量(15%、20%、25%、30%和35%)对重组米食用品质的影响,并通过主成分分析构建重组米的品质评价模型。结果表明,随着物料加水量的升高,重组米的千粒重、硬度和咀嚼性呈先上升后下降趋势,在加水量为30%时均取得最大值;糊化度呈先下降后上升的趋势,在加水量为30%时取得最小值;重组米的径向膨化率、糊化时间及蒸煮损失率显著升高(P<0.05),内聚性显著降低(P<0.05)。通过主成分分析从15个品质指标中提取出3个主成分,累计贡献率达96.61%,并以此构建综合评价模型。经验证,综合得分与感官得分顺序一致,排序为30%>35%>25%>15%>20%,且二者相关系数达0.937 3,表明该评价模型可用于评价重组米的食用品质。
Abstract:This study investigated the mechanism through which different water quantities (15%,20%,25%,30% and 35%)could affect recombinant rice quality,using screw extrusion technology.Moreover,a quality evaluation model was established using principal component analysis.The results showed that the 1 000-grain weight,hardness,and chewiness of recombinant rice first increased and then decreased in parallel with increasing water quantity,reaching the maximum value at a water quantity of 30%.The gelatinization degree first decreased and then increased,reaching the minimum value at a water quantity of 30%.The radial expansion rate,gelatinization time,and cooking loss rate of recombinant rice increased significantly(P<0.05),while the cohesiveness decreased significantly(P<0.05).Three principal components were extracted from 15 quality indexes using principal component analysis,and the cumulative contribution rate was determined as 96.61%.Based on this result,a comprehensive evaluation model was established.After verification,the order of the comprehensive and sensory scores was found to be as follows:30%>35%>25%>15%>20%.The correlation coefficient was 0.937 3,indicating that this evaluation model could be used to examine the quality of edible recombinant rice.
文章编号:202116005     中图分类号:    文献标志码:
基金项目:国家自然科学青年基金(31801540);广东省自然科学基金(2018A0303130147)
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