目的基于近红外光谱(near infrared spectrum,NIRS)技术,建立一种快速预测大株红景天片(Rhodiola grandiflora Tablets,RGT)素片硬度的方法。方法采集共600个生产样本与自制样本的NIRS,通过比较不同光谱预处理方法与不同特征变量筛选条...目的基于近红外光谱(near infrared spectrum,NIRS)技术,建立一种快速预测大株红景天片(Rhodiola grandiflora Tablets,RGT)素片硬度的方法。方法采集共600个生产样本与自制样本的NIRS,通过比较不同光谱预处理方法与不同特征变量筛选条件下模型的优劣,建立偏最小二乘(partial least-square,PLS)算法模型,另采集120个样本的NIRS对模型进行外部验证,预测RGT素片硬度。结果建立的素片硬度PLS模型中,无预处理的光谱采用全波段建模的模型效果最佳,预测模型的校正集相关系数(correlation coefficient of training set,R_(cal))与验证集相关系数(correlation coefficient of verification set,R_(pre))分别为0.9719与0.9887,相关性良好,预测均方根误差(root mean square error of prediction,RMSEP)为2.03 N,性能偏差比(ratio of performance to deviation,RPD)为6.68,预测相对偏差(relative standard to deviation,RSEP)为4.24%,模型内部验证的平均相对预测误差为2.82%,外部验证的平均相对预测误差为4.59%,均<5%,对不合格素片的检出率高达97.33%。结论NIRS分析技术结合PLS算法建立的RGT素片硬度预测模型具有良好的模型性能与预测能力,为RGT素片硬度的无损检测提供了一种新方法。展开更多
The orthogonal experimental design was used to optimize the formula of maca lozenge and explore the preparation techniques and product property of maca lozenges. With the sensory evaluation of maca lozenge as the indi...The orthogonal experimental design was used to optimize the formula of maca lozenge and explore the preparation techniques and product property of maca lozenges. With the sensory evaluation of maca lozenge as the indicator, L16 （44） orthogonal design graph was used to select the optimal techniques, and to investigate the stability, heavy metals and microorganisms contents in the tablets. The optimal formula was 80% of maca power, 12% of Rhodiola rosea powder, and 6% of Angelica sinensis powder, where the comprehensive sensory index reached 246. Moreover, under the optimal conditions, various kinds of heavy metals contents were in consistent with the GB164740 standards, and there was no significant change in color, size, friability, and disintegration time, as well as microorganism content during the 12-month preservation. With simple, easy and reliable technique and quality control method, the process study had obtained the national patent process （patent number： ZL200810233796.X）, and the contents of heavy metals and micr6organisms laid foundation for the quality standard.展开更多
文摘目的基于近红外光谱(near infrared spectrum,NIRS)技术,建立一种快速预测大株红景天片(Rhodiola grandiflora Tablets,RGT)素片硬度的方法。方法采集共600个生产样本与自制样本的NIRS,通过比较不同光谱预处理方法与不同特征变量筛选条件下模型的优劣,建立偏最小二乘(partial least-square,PLS)算法模型,另采集120个样本的NIRS对模型进行外部验证,预测RGT素片硬度。结果建立的素片硬度PLS模型中,无预处理的光谱采用全波段建模的模型效果最佳,预测模型的校正集相关系数(correlation coefficient of training set,R_(cal))与验证集相关系数(correlation coefficient of verification set,R_(pre))分别为0.9719与0.9887,相关性良好,预测均方根误差(root mean square error of prediction,RMSEP)为2.03 N,性能偏差比(ratio of performance to deviation,RPD)为6.68,预测相对偏差(relative standard to deviation,RSEP)为4.24%,模型内部验证的平均相对预测误差为2.82%,外部验证的平均相对预测误差为4.59%,均<5%,对不合格素片的检出率高达97.33%。结论NIRS分析技术结合PLS算法建立的RGT素片硬度预测模型具有良好的模型性能与预测能力,为RGT素片硬度的无损检测提供了一种新方法。
基金Supported by the Special Project for the National Basic Work of Science and Technology(2006FY110700)the Provincial Plans for the Scientific and Technological Innovation of Yunnan Province(2007C0219Z)the Special Fund for the Provincial Financial Development of Bio-industry of Yunnan Province(Yuncainong[2011]274)~~
文摘The orthogonal experimental design was used to optimize the formula of maca lozenge and explore the preparation techniques and product property of maca lozenges. With the sensory evaluation of maca lozenge as the indicator, L16 （44） orthogonal design graph was used to select the optimal techniques, and to investigate the stability, heavy metals and microorganisms contents in the tablets. The optimal formula was 80% of maca power, 12% of Rhodiola rosea powder, and 6% of Angelica sinensis powder, where the comprehensive sensory index reached 246. Moreover, under the optimal conditions, various kinds of heavy metals contents were in consistent with the GB164740 standards, and there was no significant change in color, size, friability, and disintegration time, as well as microorganism content during the 12-month preservation. With simple, easy and reliable technique and quality control method, the process study had obtained the national patent process （patent number： ZL200810233796.X）, and the contents of heavy metals and micr6organisms laid foundation for the quality standard.
