葛根提取物中麦芽糊精的中红外快速定量分析

中药提取物因生产工艺、储存条件的要求,需在生产过程中添加一定量的麦芽糊精。目前国内外还没有标准方法测定中药提取物中麦芽糊精的含量。本研究旨在建立一种快速检测葛根提取物混合粉末中辅料麦芽糊精含量的中红外快速测定方法,为企业有效把握提取物质量提供技术支持。以不同麦芽糊精含量的葛根提取物混合粉末样品88份为实验材料,采集已知麦芽糊精含量样品的中红外光谱,对谱图进行基线校正和归一化预处理,通过偏最小二乘回归方法建立定量模型。结果表明:所建定量模型预测效果良好,预测模型稳定性强、预测精度高,预测模型的决定系数R2为0.9819,预测标准差为3.205。采用中红外光谱检测技术可以快速定量葛根提取物中麦芽糊精的含量,有效地监督提取物市场以次充好的混乱现象,为市场监督部门提供技术支持。     

一种基于可预测偏最小二乘法的故障检测方法

将可预测元分析(Fore CA)与偏最小二乘法(PLS)结合用于故障检测,在选取合适的可预测元的基础上,运用偏最小二乘回归,进一步提高模型对系统的预测能力,克服了偏最小二乘回归方法无法反映系统动态时序特性的缺陷,并构造CUSUM统计量和SPE统计量以检测故障是否发生。最后通过TE模型上的仿真实验结果表明:Fore PLS方法能有效检测慢漂移等故障。     

Monitoring of the manufacturing process for ambroxol hydrochloride tablet using NIR-chemometric methods: compression effect on content uniformity model and relevant process parameters testing

This study aimed at using near-infrared (NIR) spectroscopy to monitor compaction pressure for simultaneously determining the tensile strength and content uniformity, as well as moisture and mean particle size of ambroxol hydrochloride tablets. The content uniformity, compression force and tensile strength of the laboratory samples were obtained by pressing a mixture of active principle and excipient components into tablets. To reduce the spectral baseline shift of the laboratory samples, the compaction pressure applied to the mixture was assessed by a variable pressure test. Production samples were added to the test and subjected to principal component analysis. The expanded partial least-squares (PLS) calibration model used to quantify the active content was more accurate than the model constructed from laboratory samples using the production tablets included in the calibration set. The model showed good predictability, with correlation coefficient (R) 0.9977. The validation and reliability of the content model were evaluated to determine trueness and reliability for the measurement of individual production tablets and the laboratory tablets with drug content ranging from 24 to 36?mg. The PLS calibration models for compression force and tensile strength were constructed using the same spectral set assuming both were highly related. These models yielded high R values (0.9955 and 0.9910). The R values of the moisture and mean particle size were 0.9994 and 0.9919, respectively. This study demonstrated that NIR spectroscopy combined with chemometric techniques can be successfully used to quantitatively monitor the tablet manufacturing process in the pharmaceutical industry.     

基于近红外高光谱成像快速无损检测注胶肉研究

采用近红外高光谱成像技术结合化学计量学方法建立注胶肉的快速无损检测模型。首先通过近红外高光谱成像系统获取含有不同浓度梯度卡拉胶的猪里脊肉高光谱图像,然后提取图像中的光谱数据,使用偏最小二乘法(Partial least square,PLS)探究光谱信息与不同掺假比例卡拉胶之间的定量关系。结果表明全波段光谱(900~1700 nm)所构建的PLS校正集模型均方根误差(Root mean square error,RMSE)为1.74%,预测模型RMSE为3.16%。表明基于全波段所建立的PLS模型具有较优的预测性能。利用连续投影算法(Successive projection algorithm,SPA)筛选获得11个特征波长,并优化全波长PLS模型,将预测集样品带入,以验证模型的预测效果,结果表明SPA算法结合PLS建模方法所建立的模型预测效果更优,预测集相关系数(R_P)为0.93,均方根误差(Root mean square error of prediction,RMSEP)为3.51%,预测偏差(Residual predictive deviation,RPD)为2.66。试验表明利用高光谱成像技术可实现对注胶猪肉的快速无损检测。     

Near-infrared spectroscopy to assist authentication and labeling of Asiago d’allevo cheese

This study investigated the possibility of using near-infrared spectroscopy (NIRS) for the authentication of Asiago d’allevo, a protected designation of origin cheese from northern Italy. Latent variable models applied on spectral data were developed and used to estimate several chemical properties and to classify the available dataset according to the location and management of the cheesemaking factory (lowland and alpine), the ripening age (6, 12, 18 and 36 months), the altitude of milk production (low, medium, medium–high and high), and the period of the year of the cheese production (May, July and September). The variable importance in projection index was used to identify the most informative spectral regions for discrimination. Results showed that NIR spectra can be used both to accurately estimate several chemical properties and to classify the samples according to the different experimental conditions under investigation with the same discrimination capacity provided by traditional chemical analysis.     

A framework for joint estimation of age,gender and ethnicity on a large database

Human age, gender and ethnicity are valuable demographic characteristics. They are also important soft biometric traits useful for human identification or verification. We present a framework that can estimate the three traits jointly. The joint estimation framework could deal with the mutual influence of age, gender, and ethnicity implicitly. Under this joint estimation framework, we explore different methods for simultaneous estimation of age, gender, and ethnicity. The canonical correlation analysis (CCA) based methods, and partial least squares (PLS) models are explored under our joint estimation framework. Both the linear and nonlinear methods are investigated to measure the performance. We also validate some extensions of these methods, such as the least squares formulations of the CCA methods. We found some consistent ranking of these methods under our joint estimation framework. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity. An analysis of this property is given based on the rank theory. The experiments are conducted on a very large database containing more than 55,000 face images.