Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology

Cárdenas, Vanessa; Cordobés, M.; Blanco, M.; Alcalà, Manel

doi:10.1016/j.jpba.2015.05.002

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The accompanying ANOVA for the linear RSM model (Table 4) also demonstrates that all four structural variables are significant. Furthermore, no serious outliers were detected for the test set as illustrated by plotting Q Residuals versus Hotelling T2 values 22 as shown in Fig. 9b.…”

Section: Resultsmentioning

confidence: 87%

Chemometric Methods for Estimating the Strain Hardening Modulus in Polyethylene Resins

et al. 2018

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The feasibility of using multiway or N-way partial least square (NPLS) methods to estimate physical properties of 1-butene and 1-hexene polyethylene (PE) copolymers directly from multidimensional data obtained from size exclusion chromatography coupled to a Fourier transform infrared detector (SEC FT-IR) was explored. Digital sample sets of horizontal slices (slabs) of two-dimensional data simulating the molecular weight distribution and the corresponding orthogonal FT-IR spectra were correlated to a particular Y-block response using NPLS. The NPLS results were compared to those obtained through separate estimations using various algorithms and exploratory response surface methods. The estimated strain hardening modulus () for bimodal PE-like digital structures could adequately be modeled using both the linear response surface method (RSM) and NPLS. Although different input values were used, the predicted values for by NPLS was found to mirror both the analytical results and the expected structural effects obtained using linear RSM models.

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Section: Resultsmentioning

confidence: 87%

Chemometric Methods for Estimating the Strain Hardening Modulus in Polyethylene Resins

et al. 2018

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“…Redundancy of the calibration designs can be reduced with maximized information content and minimized number of samples. 28–30 However, it is necessary to carefully assess the risks associated with potential variability and determine the effects of removing one or more variability on the long-term performance of NIR models.…”

Section: Introductionmentioning

confidence: 99%

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame

Anderson

Drennen

et al. 2019

Appl Spectrosc

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Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization–Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.

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“…Blanco et al 28 3.9 and 1.6 DR pharm. powder blends (granules) Cárdenas et al 29 0.81-2.68 DR pharm. powder blends and tablets Sánchez-Paternina et al 30 <5 T pharm.…”

Section: Introductionmentioning

confidence: 99%

Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

Ortega-Zúñiga

Reyes-Maldonado

Méndez

et al. 2017

Journal of Near Infrared Spectroscopy

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This study is focused on understanding absorption and scattering effects and their impact on the errors observed in near infrared calibration models developed using partial least squares regression able to predict the number of polypropylene film layers stacked together. The films provided a system with reduced heterogeneity to study the sources of error due to light scattering, selection of spectral preprocessing and spectral region on near infrared calibrations. Near infrared spectra were acquired using two experimental setups with the integrating sphere module of the FT-NIR spectrometer. The first setup consisted in stacking the polymer films to determine the penetration of the near infrared radiation. The second setup was a variation using a reflective surface on the top of the films in transflection mode, increasing the pathlength and therefore transmission and absorption of the material. The estimation of the penetration of radiation was performed using talc placed over the polymers films. The narrow bands of talc, related to first and second overtones of the O-H stretching mode, were used to estimate the near infrared sampling depth into polymer film layers, which ranged from 2.95 to 3.12 mm. Partial least square calibrations developed with up to 30 film layers were accurate with bias values that were not significantly different from zero at the 95% confidence level. Statistical errors were calculated for seven near infrared regions using different spectral preprocessing and the confidence interval of the bias showed that optical sampling is unbiased and there is an absence of systematic error by the near infrared method. A calibration model developed with 50 film layers presented high statistical errors and bias different from zero, indicating a sampling error due the depth of penetration of near infrared radiation. The impact of number of samples in the calibration and validation set was also evaluated. The results showed that bias was significant when the number of samples was less than 11. This finding highlights the lack of systematic error in the near infrared method, as long as the number of samples in the calibration set is representative of the variation to be modelled by a partial least squares regression and the sampling error is reduced.

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Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology

Cited by 11 publications

References 16 publications

Chemometric Methods for Estimating the Strain Hardening Modulus in Polyethylene Resins

Chemometric Methods for Estimating the Strain Hardening Modulus in Polyethylene Resins

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame

Study of near infrared chemometric models with low heterogeneity films: The role of optical sampling and spectral preprocessing on partial least squares errors

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