A training set selection strategy for a universal near-infrared quantitative model

Second-order universal calibration

Quantification and qualification of an analyte of interest in pharmaceutical tablets from different manufacturers/companies are a hard task because of the potential presence of various interfering molecules. Indeed, the composition of the tablets covers a wide range of interferents which can be even unknown. As a consequence, we propose to determine the concentration of an analyte of interest regardless of the interferents using the concept of universal calibration. Universal calibration paves the way to the quantification of a specific chemical entity in samples with various compositions and different interferents. This is possible by the trilinear structure of analyte's signal. In fact, the second-order advantage resulting from the second-order universal calibration models is exploited. However, a new second-order calibration strategy was conducted in this work using Trilinear Factor Extraction (TFE). A simulated data set was exemplified to highlight the ability of the proposed procedure in order to accurate extraction of the analyte's concentration profile. Additionally, two real data sets were also explored in order to test the TFE method. In the first case, Acetaminophen was quantified using fluorescence spectroscopy in tablets with different formulations from 6 companies. In the second experimental data, a peptide (Valine-Tyrosine-Valine) was successfully quantified in different samples using spectrofluorimetric data. Finally, these real data sets were analyzed by Multivariate Curve resolution - Alternating Least-Squares (MCR-ALS) under non-negativity and trilinearity constraints for the sake of comparison. The calculated Root Mean Square Error of Predictions (RMSEP) of Acetaminophen were 0.028 and 0.026 for the MCR-ALS and TFE models, respectively. On the other hand, for the second experimental data set, the RMSEP were 0.216 and 0.165, respectively. Finally, based on a paired t-test, the results of MCR-ALS and TFE were not significantly different.

Compilation of a Near-Infrared Library for Construction of Quantitative Models of Oral Dosage Forms for Amoxicillin and Potassium Clavulanate

The accuracy of quantitative models for near-infrared (NIR) spectroscopy is dependent upon calibration samples with concentration variations. Conventional sample-collection methods have shortcomings (especially time-consumption), which creates a “bottleneck” in the application of NIR models for Process Analytical Technology (PAT) control. We undertook a study to solve the problem of sample collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms (ODFs) were used as examples. The aim of this study was to find an approach to construct NIR quantitative models rapidly using a NIR spectral library based on the idea of a universal model. The NIR spectral library of amoxicillin and potassium clavulanate ODFs was defined and comprised the spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (r_T) was used to indicate the similarities of the spectra. The calibration sets of samples were selected from a spectral library according to the median r_T of the samples to be analyzed. The r_T of the samples selected was close to the median r_T. The difference in r_T of these samples was 1.0–1.5%. We concluded that sample selection was not a problem when constructing NIR quantitative models using a spectral library compared with conventional methods of determining universal models. Sample spectra with a suitable concentration range in NIR models were collected rapidly. In addition, the models constructed through this method were targeted readily.

Rapid Analysis of the Quality of Amoxicillin and Clavulanate Potassium Tablets Using Diffuse Reflectance Near-Infrared Spectroscopy

The cycle-closed dimer of amoxicillin influences its critical quality and is an important impurity in amoxicillin and clavulanate potassium tablets. The quality of the tablets could be rapidly evaluated using the impurity as an indicator. Here, we report a quantitative model to determine the cycle-closed dimer in samples from different manufacturers using diffuse reflectance near-infrared (NIR) spectroscopy by partial least squares regression for one y variable (PLS1) and hierarchical cluster analysis. Because the contents of the (active pharmaceutical ingredients) APIs (amoxicillin and clavulanate potassium) and water are also the important indexes of the tablet quality, three other quantitative models were used to confirm the API data and water content. All of the four models facilitate rapid and complete control of the tablet quality. In addition, quantitative models were validated in terms of specificity, linearity, accuracy, repeatability, and intermediate precision according to the International Conference on Harmonisation guidelines by evaluating the characteristics of the NIR spectra. These results confirmed that the models were satisfactory.