葛根全原粉沸腾造粒工艺优化

为探索运用沸腾造粒技术对高膳食纤维葛根全原粉的最佳造粒工艺,采用单因素试验和响应面分析法对主要造粒工艺参数进行优化并得到回归模型,最佳造粒工艺条件为：雾化压力0.19 MPa、进风温度59.21℃、蠕动泵泵速3.13 r/min,经回归分析表明：模型的决定系数R^2为98.81%,造粒后葛根全原粉溶解率预测值为66.48%±0.383%。经验证试验在最佳造粒工艺条件下,造粒后的葛根全原粉溶解率为66.49%±2.44%,较造粒前提高33.98个百分点,且造粒后产品得率可达91.62%,对有效成分葛根素无显著影响,回归模型的相对误差小于1%,表明响应面法对葛根全原粉造粒工艺合理可行。研究结果为速...

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Bibliographic Details
Published in农业工程学报 Vol. 32; no. 19; pp. 296 - 301
Main Author 尹乐斌 夏秋良 张臣飞 杨莹 夏晓玲 赵良忠
Format Journal Article
LanguageChinese
Published 邵阳学院生物与化学工程系，邵阳 422000 2016
豆制品加工技术湖南省应用基础研究基地，邵阳 422000%邵阳学院生物与化学工程系,邵阳,422000%湖南省和鑫生物科技有限公司,武冈,422400
Subjects
优化
响应面法
模型
沸腾造粒
溶解率
葛根全原粉
试验
models
试验
模型
experiments
葛根全原粉
溶解率
response surface methodology
Puerariae radix powder
dissolution rate
optimization
优化
响应面法
boiling granulation
沸腾造粒
Online AccessGet full text
ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.19.040

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Summary:为探索运用沸腾造粒技术对高膳食纤维葛根全原粉的最佳造粒工艺,采用单因素试验和响应面分析法对主要造粒工艺参数进行优化并得到回归模型,最佳造粒工艺条件为：雾化压力0.19 MPa、进风温度59.21℃、蠕动泵泵速3.13 r/min,经回归分析表明：模型的决定系数R^2为98.81%,造粒后葛根全原粉溶解率预测值为66.48%±0.383%。经验证试验在最佳造粒工艺条件下,造粒后的葛根全原粉溶解率为66.49%±2.44%,较造粒前提高33.98个百分点,且造粒后产品得率可达91.62%,对有效成分葛根素无显著影响,回归模型的相对误差小于1%,表明响应面法对葛根全原粉造粒工艺合理可行。研究结果为速溶葛根全原粉的进一步开发利用提供参考。
Bibliography:11-2047/S
To explorethe use ofboiling granulation technology for the optimization of high dietary fiberPuerariae radix powder with granulation process, in this study, the main parameters （i.e., atomizing pressure, inlet air temperature, peristaltic pump speed） of granulation process were optimized by the response surface methodology on the basis of single factor experiments. Based on that, the regression model was generated. The single factor experiment results showed that the parameters of boiling granulation were relative better when atomizing pressure at 0.20 MPa, inlet air temperature at 60℃, peristaltic pump speed at 3 r/min. We analyzed the above single factor experiment results by response surface methodology to obtain the optimal granulation process regression modelThe variance analysis of the regression model, the regression probability was P〈0.0100. On the other hand, the test for lack of fit of the regression model was P=0.5100〉0.1000, showing that the response surface test results and the regressio
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.19.040