Process analytical technology case study,part III: Calibration monitoring and transfer

This is the third of a series of articles detailing the development of near-infrared spectroscopy methods for solid dosage form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to develop a system for continuous calibration monitoring and formulate an appropriate strategy for calibration transfer. Indcators of high-flux noise (noise factor level) and wave-length uncertainty were developed. These measurements, in combination with Hotelling’s T² and Q residual, are used to continuously monitor instrument performance and model relevance. Four calibration transfer techniques were compared. Three established techniques, finite impulse response filtering, generalized least squares weighting, and piecewise direct standardization were evaluated. A fourth technique, baseline subtraction, was the most effective for calibration transfer. Using as few as 15 transfer samples, predictive capability of the analytical method was maintained across multiple instruments and major instrument maintenance.     

Application of NIR Spectroscopy Coupled with PLS Regression for Quantification of Total Polyphenol Contents from the Fruit and Aerial Parts of Citrullus colocynthis

Introduction

Citrullus colocynthis (L.) Schrad is extensively used to treat diabetes, obesity, fever, cancer, amenorrhea, jaundice, leukemia, rheumatism, and respiratory diseases. Chemical studies have indicated the presence of several cucurbitacins, flavones, and other polyphenols in this plant. These phytochemical constituents are responsible for the interesting antioxidant and other biological activities of C. colocynthis.

Objective

In the present study, for the first time, near infrared (NIR) spectroscopy coupled with partial least square (PLS) regression analysis was used to quantify the polyphenolic phytochemicals of C. colocynthis.

Methodology

The fruit and aerial parts of the C. colocynthis were extracted individually in methanol followed by fractionation in n‐hexane, chloroform, ethyl acetate, n‐butanol, and water. Near infrared (NIR) spectra were obtained in absorption mode in the wavelength range 700–2500 nm. The PLS regression model was then built from the obtained spectral data to quantify the total polyphenol contents in the selected plant samples.

Results

The PLS regression model obtained had a R² value of 99% with a 0.98 correlationship value and a good prediction with a root mean square error of prediction (RMSEP) value of 1.89% and correlation of 0.98. These results were further confirmed through UV–vis spectroscopy and it is found that the ethyl acetate fraction has the maximum value for polyphenol contents (101.7 mg/100 g; NIR, 100.4 mg/100 g; UV–vis).

Conclusions

The polyphenolic phytochemicals of the fruit and aerial parts of C. colocynthis have been quantified successfully by using multivariate analysis in a non‐destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation. Copyright © 2017 John Wiley & Sons, Ltd.     

Online monitoring of horseradish peroxidase structural changes by Near Infrared (NIR) Spectroscopy

The development of online monitoring techniques is of great relevance for understanding the structural changes of proteins under different conditions in order to maximize their catalytic activity. This study aimed to evaluate the potential of the NIR (near-infrared spectroscopy) technique for the monitoring of alterations of secondary and tertiary structures of Horseradish peroxidase (HRP), an oxidoreductase that has several applications in the industrial environment, food industry and bioremediation. The NIR associated to the multivariate calibration, through the PLS (partial least square) method allowed the construction of a robust model for the prediction of the analysis. The values of the correlation coefficient (R²), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and root mean square error of cross validation (RMSECV) for secondary structure analysis using circular dichroism (CD) data as reference (actual values) were 0.9681, 0.647 (mdeg), 0.945 (mdeg), and 1.12 (mdeg), respectively. For tertiary structure analysis, fluorescence spectroscopy (FL) data were used as reference. R², RMSEC, RMSEP and RMSECV were, respectively 0.9999, 1.95 (a.u.), 2.09 (a.u.); and 2.19 (a.u.). NIR combined multivariate calibration showed promising results for sctructural changes monitoring of HRP.     

A novel method for analyzing thick tablets by near infrared spectroscopy

A near-infrared (NIR) spectroscopic method to determine content uniformily of a large, thick tablet using an approach that could facilitate future validations has been developed. A CT ibuprofen 800-mg tablet weighs about 1150 mg and is about 18.6 mm wide and 7.6 mm thick. The FT NIR spectrometer was optimized for transmission spectra of the tablets by moving it to the sample compartment and placing it immediately behind the tablet. In spite of this dedicated setup, the transmission spectra obtained were very poor, indicating that the NIR radiation was not reaching the detector. The spectra of the tablet improved with use of a simple preparation in which a flat-face die applies pressure of 20 000 psi to the tablet, this reduced the thickness of the tablet from 7.6 mm to 3.6 mm. A calibration model was developed for tablets with drug content ranging from 70% to 130% of label. The calibration model was tested using a validation set of tablets with a drug content of 752, 800, and 848 mg. The results obtained were within 1.5% of the known drug content of the validation set, tablets. Even with the sample preparation, the content uniformity results of 10 tablets could be determined using this method in less than 1 hour. The approach described in this article could also be used to validate NIR content uniformity methods for orther formulations. Published: July 12, 2001.     

人血清中胆固醇近红外光谱快速检测初步研究

以全自动生化分析仪测定结果为参考值,采用傅利叶变换近红外透射光谱技术,结合偏最小二乘法,建立人血清中胆固醇和甘油三酯的定标模型。利用内部交叉验证和自动优化功能对预测模型进行了优化,确定了最优建模参数。模型对人血清中胆固醇和甘油三酯定标样品集的预测值与参考值的相关系数r分别为0．9011、0．9593,预测校正标准误RMSECV分别为15．0mg／dL,21．6mg／dL。表明利用近红外光谱分析技术实现血清中胆固醇和甘油三酯快速检测是可行的。     

Detecting and quantifying meat meal or meat and bone meal contamination in fishmeal by visible and near infrared reflectance spectra

The use of animal protein feeds such as meat meal or meat and bone meal (MMBM) play an important role in the feed manufacturing industry, but their safe and healthy use in animal feeds is of public concern in order to prevent the spread of bovine spongiform encephalopathy (BSE). The objective of the present work was to develop a technique using near infrared reflectance spectroscopy (NIRS) that would be suitable for detecting and quantifying contaminating levels of MMBM in fishmeal. To this end, a partial least squares (PLS) discriminant analysis and a modified partial least squares (MPLS) quantitative analysis, using visible and NIRS, were developed using a calibration set of 186 samples including 90 samples of pure fishmeal and 96 samples adulterated with MMBM at levels ranging from 10 to 320 g/kg. An external validation set, comprised of 39 pure samples and 54 adulterated samples, was used to validate the calibration model. A PLS discriminant analysis model developed with mathematic pretreatment 1,4,4,1, successfully detected fishmeal adulterated with MMBM. External validation indicated that all samples were discriminated correctly. A MPLS quantitative model, developed with mathematic pretreatment 1,4,4,1, also successfully predicted the MMBM in fishmeal with standard error of cross-validation (SECV) of 27.89 g/kg and ratio of the standard deviation of the validation set to the standard error of prediction (RPD) of 3.37. The calibration and validation results confirm that NIRS could provide the feed industry and inspection bodies with a rapid, non-destructive and non-invasive technique for the detection and quantification of MMBM in fishmeal.