首页> 美国卫生研究院文献>Molecules >In Situ Monitoring of the Effect of Ultrasound on the Sulfhydryl Groups and Disulfide Bonds of Wheat Gluten
【2h】

In Situ Monitoring of the Effect of Ultrasound on the Sulfhydryl Groups and Disulfide Bonds of Wheat Gluten

机译:超声波对小麦面筋巯基和二硫键影响的原位监测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。
获取外文期刊封面目录资料

摘要

Ultrasound treatment can improve enzymolysis efficiency by changing the amounts of sulfhydryl groups (SH) and disulfide bonds (SS) in protein. This paper proposes an in-situ and real-time monitoring method for SH and SS during ultrasound application processes using a miniature near-infrared (NIR) optical fiber spectrometer and a chemometrics model to determine the endpoint of ultrasonic treatment. The results show that SH and SS contents fluctuated greatly with the extension of ultrasonic time. The optimal spectral intervals for SH content were 869–947, 1207–1284, 1458–1536 and 2205–2274 nm, the optimal spectral intervals of SS content were 933–992, 1388–1446, 2091–2148 and 2217–2274 nm. According to the optimal spectral intervals, the synergy interval partial least squares (Si-PLS) and error back propagation neural network (BP-ANN) for SH, SS contents were established. The BP-ANN model was better than the Si-PLS model. The correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) for the BP-ANN model of SH were 0.9113 and 0.38 μmol/g, respectively, the Rp2 and residual prediction deviation of SH were 0.8305 and 2.91, respectively. For the BP-ANN model of SS, the Rp and the RMSEP were 0.7523 and 6.56 μmol/g, respectively. The Rp2 and residual prediction deviation (RPD) of SS were 0.8305 and 2.91, respectively. However, the Rp2 and RPD of SS was 0.5660 and 1.64, respectively. This work demonstrated that the miniature NIR combined with BP-ANN algorithms has high potential for in-situ monitoring of SH during the ultrasonic treatment process, while the spectral prediction model of SS needs to be further developed.
机译:超声波处理可以通过改变蛋白质中巯基(SH)和二硫键(SS)的量来提高酶解效率。本文提出了一种使用微型近红外(NIR)光纤光谱仪和化学计量学模型确定超声处理终点的超声应用过程中SH和SS的实时监测方法。结果表明,随着超声时间的延长,SH和SS含量波动较大。 SH含量的最佳光谱间隔为869–947、1207–1284、1458–1536和2205–2274 nm,SS含量的最佳光谱间隔为933–992、1388-1446、2091-2148和2217-2274 nm。根据最佳光谱区间,建立了SH,SS含量的协同区间偏最小二乘(Si-PLS)和误差反向传播神经网络(BP-ANN)。 BP-ANN模型优于Si-PLS模型。 SH的BP-ANN模型的预测集相关系数(Rp)和预测均方根误差(RMSEP)分别为0.9113和0.38μmol/ g,Rp 2 和SH的残差预测偏差分别为0.8305和2.91。对于SS的BP-ANN模型,Rp和RMSEP分别为0.7523和6.56μmol/ g。 SS的Rp 2 和残差预测偏差(RPD)分别为0.8305和2.91。然而,SS的Rp 2 和RPD分别为0.5660和1.64。这项工作表明,将微型NIR结合BP-ANN算法在超声处理过程中对SH进行原位监测具有很高的潜力,而SS的光谱预测模型需要进一步开发。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号