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中国精品科技期刊2020
陈培栋, 万忠民, 王胜录, 马倩婷. 响应面法优化糙米微波改性工艺[J]. 食品工业科技, 2018, 39(6): 152-158. DOI: 10.13386/j.issn1002-0306.2018.06.028
引用本文: 陈培栋, 万忠民, 王胜录, 马倩婷. 响应面法优化糙米微波改性工艺[J]. 食品工业科技, 2018, 39(6): 152-158. DOI: 10.13386/j.issn1002-0306.2018.06.028
CHEN Pei-dong, WAN Zhong-min, WANG Sheng-lu, MA Qian-ting. Optimization for microwave modification technology of brown rice by response surface methodology[J]. Science and Technology of Food Industry, 2018, 39(6): 152-158. DOI: 10.13386/j.issn1002-0306.2018.06.028
Citation: CHEN Pei-dong, WAN Zhong-min, WANG Sheng-lu, MA Qian-ting. Optimization for microwave modification technology of brown rice by response surface methodology[J]. Science and Technology of Food Industry, 2018, 39(6): 152-158. DOI: 10.13386/j.issn1002-0306.2018.06.028

响应面法优化糙米微波改性工艺

Optimization for microwave modification technology of brown rice by response surface methodology

  • 摘要: 以糙米为研究对象,采用微波改性处理提高糙米的蒸煮品质,确定最佳复合处理条件。通过单因素实验确定微波时间、微波功率和糙米初始水分含量对糙米蒸煮品质的影响程度。并结合响应面分析法与期望函数优化微波改性处理工艺。结果表明,微波改性处理糙米的最佳工艺参数为:微波功率2400 W、微波时间75 s、初始水分含量14.5%,在该条件下期望函数值最高为0.76,对应的糙米饭硬度为2865.85 g,吸水率为56.96%,碘蓝值为0.57,验证实验结果与理论预测值无显著性差异(p>0.05),说明该模型回归性良好,实际实验拟合度高,对糙米蒸煮品质改性具有一定指导价值。

     

    Abstract: Brown rice was studied in this experiment,the cooking property of brown rice was improved by microwave modification treatment and the best compound treatment conditions were determined. The influence of brown rice cooking property was determined by single factor experiment with microwave treatment time,microwave power and initial moisture content. The optimal conditions of microwave modification technology were combined by response surface analysis and desirability function. The results showed that the best process parameters for the microwave modification treatment of brown rice was established:microwave power 2400 W,microwave time 75 s,initial moisture content 14.5%. The maximum value of this desirability function was 0.76 and the corresponding hardness,water absorption and iodine blue value of brown rice was 2865.85 g,56.96% and 0.57. No significant difference was shown between the experimental data and model prediction(p>0.05). It accounted for that the model was well regressive and the degree of fitting of actual test was high. The modification of brown rice cooking quality had certain value of guidance.

     

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