Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

Cogdill, Robert P.; Anderson, Carl A.; Delgado-Lopez, Miriam; Molseed, David; Chisholm, Robert; Bolton, Raymond; Herkert, Thorsten; Afnán, Ali M.; Drennen, James K.

doi:10.1208/pt060237

Cited by 49 publications

(54 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to apply NIR spectroscopy for quality predicting, first, an NIR instrument has to be calibrated using chemical reference methods. It also requires reliable hardware systems and complicated mathematical techniques to interpret calibration results [28]. While the radiation penetrates the product, its spectral characteristics change through wavelength-dependent scattering process and absorption process.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Mango Fruit Quality from NIR Spectroscopy using an Ensemble Classification

Pronprasit¹,

Natwichai²

2013

IJCA

View full text Add to dashboard Cite

Near-infrared (NIR) spectroscopy is one of the most prominent non-destructive techniques which can classify the quality of agricultural products. An important issue for the NIR is when the new target data from different environments, e.g. area, orchard, harvesting age, harvesting year, etc., is classified by the existing classifier, the predicting performance might not be effective. Furthermore, the classification model re-building can incur the cost in term of laboring or time.In this paper, an ensemble classification is applied to predict the quality of mango cv. Nam Dok Mai Si Thong fruits. The ensemble classification is a technique which combines multiple classifiers to work as a sole classifier. In addition, such individual classifier, sub-classifier, will be assigned a weight according to the similarity to the target dataset for adapting itself to the new environment. In our work, the ensemble classification will contain the classification models, multiple linear regression (MLR) equations, trained from the data with different harvesting year, and harvesting period for the fruit collection on such year. The weight of each subclassifier is obtained from the difference between its prediction error and the prediction error of the target dataset.The proposed approach was evaluated by the experiments in which its performance was compared with the traditional sole classifiers as well as the naïve ensemble classifiers without weighting. From this result, the standard error of prediction (SEP) values of the proposed ensemble classifier, naïve ensemble classifier, and single classifier were 0.95, 1.08, and 0.99, respectively. Such results can illustrate that our proposed ensemble classifier can perform well for the changing environments. General Terms

show abstract

Section: Related Workmentioning

confidence: 99%

Prediction of Mango Fruit Quality from NIR Spectroscopy using an Ensemble Classification

Pronprasit¹,

Natwichai²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Finally, the effect of variation in the spectral profile of the tablet cores (e.g. concentration, process signature [3]) is not known. Since the spectral signature related to coating thickness is highly non-specific, a much more in-depth experimental design would likely be necessary to generate a multivariate calibration model suitably robust to be deployed for in-process coating measurement.…”

Section: Nir Spectroscopymentioning

confidence: 99%

“…PAT) [2][3][4][5] capable of quickly and accurately determining coating depth or thickness are essential for control of, and real-time release from, tablet coating operations. Many analytical techniques have been used for analysis of tablet coatings, including optical microscopy, atomic force microscopy, confocal laser microscopy, X-ray photoelectron spectroscopy, electron paramagnetic resonance, Fourier transform infrared spectroscopy, scanning thermal microscopy [6], and laserinduced breakdown spectroscopy [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Terahertz Pulse Imaging and Near-Infrared Spectroscopy for Rapid, Non-Destructive Analysis of Tablet Coating Thickness and Uniformity

et al. 2007

Self Cite

View full text Add to dashboard Cite

In this study, coating thickness and uniformity of production-scale pharmaceutical tablets were investigated using near-infrared (NIR) and terahertz pulse imaging (TPI) spectroscopy. Two coating formulations were considered; samples for each coating formulation were obtained at 0, 1, 2, 3, 4, and 5% coating weight. NIR spectra were collected, and regressed with respect to batch percent weight gain. While standard errors of calibration (SEC) less than 0.5% were observed for both formulations, the calibrations were not specifically sensitive to coating thickness. An upper limit for NIR coating thickness analysis was estimated to be~4 -6% weight gain for this system. The NIR calibrations were used as filters to choose subsets of samples for TPI, and as a secondary method for validation of TPI results. The features in TPS time-domain spectra result when an incident THz plane wave meets a refractive index interface, which may be converted to an absolute distance. Therefore, assuming that a discernible difference in refractive index between coating material and core exists, coating thickness can be determined non-destructively. Coating thickness measurements from TPI and NIR spectroscopy were compared to estimate the lower limit for quantitative TPI coating analysis; a lower limit of~35 mm was obtained for this system. Optical microscopy was employed on a subset of samples to validate absolute thickness values; reasonable correlations between the three methods were obtained. TPI was considered advantageous relative to the other methods, as similar results were obtained without the need for destructive sampling or empirical calibration development.

show abstract

“…Due to the lesser sensitivity of the reflectance mode of NIR to moving sample error than transmission mode (19), the reflection mode was used in this study. The approach in this study involved three processing steps: Firstly, building the NIR partial least square (PLS1) calibration models for drug content and CQA of ribbed flakes using different off-line strategies (sampling mode: static or dynamic), spectral preprocessing methods, positioning of the probe, and beam size.…”

Section: Introductionmentioning

confidence: 99%

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

Samanta

Karande

et al. 2012

AAPS PharmSciTech

View full text Add to dashboard Cite

Abstract. This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young's modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake's undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam sizẽ 1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.

show abstract

Process analytical technology case study part I: Feasibility studies for quantitative near-infrared method development

Cited by 49 publications

References 23 publications

Prediction of Mango Fruit Quality from NIR Spectroscopy using an Ensemble Classification

Prediction of Mango Fruit Quality from NIR Spectroscopy using an Ensemble Classification

Comparison of Terahertz Pulse Imaging and Near-Infrared Spectroscopy for Rapid, Non-Destructive Analysis of Tablet Coating Thickness and Uniformity

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

Contact Info

Product

Resources

About