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食品研究与开发:2020,41(19):94-98
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均匀试验结合模糊数学评价优化马铃薯泥营养餐的配方
(1.贵州省农业科学院生物技术研究所,贵州贵阳550006;2.贵州省农业科学院食品加工研究所,贵州贵阳550006;3.贵州省农业生物技术重点实验室,贵州贵阳550006)
Optimization Mashed Potato Nutritious Meal by Uniform Experiment and Fuzzy Mathematics Evaluation
(1.Institute of Biotechnology,Guizhou Academy of Agricultural Science,Guiyang 550006,Guizhou,China;2.Institute of Food Processing Technology,Guizhou Academy of Agricultural Science,Guiyang 550006,Guizhou,China;3.Guizhou Key Laboratory of Agricultural Biotechnology,Guiyang 550006,Guizhou,China)
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投稿时间:2019-12-11    
中文摘要: 以新鲜马铃薯为原材料,经过蒸煮、调配和炒制制成马铃薯营养餐。采用均匀试验设计并结合模糊数学感官评价,优化马铃薯泥营养餐的配方。对均匀试验结果进行二次多项式逐步回归处理分析,结果表明:回归方程决定系数R2为0.974 0、相关系数R为0.986 9、F值为31.27、p值为0.000 8、标准差S为1.86;回归方程拟合最大值为92.41分,最佳配方为马铃薯泥1.77 kg、卤牛肉0.11 kg、青豆0.10 kg、土豆淀粉0.02 kg、菜籽油39.60 mL和食盐1.90 g。按照最佳配方进行验证试验,结果表明,验证样品模糊数学感官评分为89.20分,与回归方程拟合值相比较,误差百分比为3.47%,回归方程拟合准确度高、最佳配方结果准确可靠。
中文关键词: 马铃薯  营养餐  配方  均匀试验  模糊数学
Abstract:Fresh potatoes were used as raw materials,which were steamed,blended and fried to made potato nutritious meal.The uniform experimental design combined with fuzzy mathematical sensory evaluation was used to optimize the formula of mashed potato nutrition meal.The uniform test results were quadratic polynomial stepwise regression analysis,the results indicated that the equations coefficients of regression was 0.974 0,coefficient of association was 0.986 9,F value was 31.27,p value was 0.000 8,the standard deviation S was 1.86.The equations fit maximum value was 92.41,the optimum formula were mashed potatoes 1.77 kg,marinated beef 0.11 kg,petits pois 0.10 kg,potato starch 0.02 kg,colza oil 39.60 mL,salt 1.90 g.According the best formula to conduct validation tests,the results indicated that the fuzzy mathematics sensory score of the validation sample was 89.20,compared with the fitting value of regression equation,the error percentage was 3.47%.The fitting accuracy of regression equation was high,and the result of the best formula was accurate and reliable.
文章编号:202019017     中图分类号:    文献标志码:
基金项目:国家重点研发计划(2016YFNC010104);贵州省科技计划课题(黔科合重大专项字[2014]6016)
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