466106 Overcoming the Challenges of Noisy Data during the Calibration of NIR As a PAT Technique to Measure Roller Compacted Ribbon Density

Tuesday, November 15, 2016: 4:43 PM
Continental 5 (Hilton San Francisco Union Square)
Mary Ellen Crowley1, Avril Hegarty2, Michael McAuliffe3, Graham O'Mahony1, Luke Kiernan4, Kevin Hayes2,5 and Abina Crean1, (1)School of Pharmacy, University College Cork, Cork, Ireland, (2)Mathematics Applications Consortium for Science and Industry (MACSI), University of Limerick, Limerick, Ireland, (3)Cork Institute of Technology, Cork, Ireland, (4)Innopharma Labs, Dublin, Ireland, (5)Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland




                A number of research groups have investigated the use of NIR spectroscopy for monitoring ribbon density [1-4]. These studies have taken advantage of the fact that NIR spectra are affected by the density of the material being analysed and offer the potential of measuring/monitoring roller compacted ribbon density in real time [1-3, 5]. In order to develop a calibration equation relating density to the NIR spectra Gupta [2] fitted a regression line through all the scanned wavelengths and used the fitted slope of the line. However, this approach does not take into account issues which arise when there are multiple scans on multiple samples of the same type of material, and each scan records simultaneous NIR spectra for several probes. The spectral dataset can contain erroneous outliers. Data processing in real time is challenging as sampling error outliers must be either filtered or cleaned from the data set before further analysis. [6]. Visual inspection of the data and removal of sampling error outliers though justifiable is not practical for real time data processing. Existing data cleaning and filtering models are complex and are essentially off-line operations [6]. A simple solution is required.

This study aims to highlight how variability in roller compacted ribbon quality can impact on NIR spectral measurement and proposes a simple method of data analysis to deal with this.

  <>Materials and Methods

  Roller compaction: A Fitzpatrick CCS220 roller compactor was used for this study. The roll speed was kept constant at 2 rpm, roll gap at 0.5 mm and the roll pressure was adjusted for each run to 2, 4, 6, 8, 10, 12, 14 and 16 kN. Blends of microcrystalline cellulose, lactose monohydrate and magnesium stearate were compacted. Each of the three blends differed in MCC % moisture content. MCC was stored at 43% for blend 1, blend 2 at 11% and blend 3 at 75% relative humidity. The variation in moisture was introduced to assess its impact on the ribbon quality and thus challenge the NIR technique. Ribbon envelope density was determined by the GeoPyc™ 1360.

NIR spectral collection: Four MultiEyeTM NIR probes scanned samples at-line in the manner in which they were produced by the roller compactor i.e. samples were not cut to a specific size. This set up ensured outliers were detected in a similar manner to which they would be detected in-line when monitoring the process. The Perkin Elmer Spotlight 400 FT-IR was also used and the off-line results were compared to the at-line MultiEye™ data. Samples were cut to a specific size to facilitate off-line Perkin Elmer Spotlight analysis.

Statistical data analysis: Three different calibration methods were developed between spectral slopes and envelope density and compared; (1) using the entire data set unfiltered, (2) a visual discard method and finally (3) a 33% Trimmed Mean method.


RESULTS AND Discussion


        Calibration of the NIR method to ribbon density was challenging due to the presence of spectral data from broken, split and curved ribbons in the data set particularly for blend 3 which had the highest moisture content (6.96 % w/w).  All initial processing of the full data set indicated that some form of data cleaning was required in order to process the MultiEye™ at-line data set. The visual discard method was applied to the full data set and was found to be particularly successful for blends 1 and 2 (Figure 1). After using the visual discard data cleaning method MulitEye™ data the calibration correlation was found to be as good as the off-line higher scan resolution Perkin Elmer spectrometer. Blend 1 off line r=0.92, visual discard r=0.95.

However it is necessary to replace the visual method of spectra cleaning with a non-subjective method which was capable of screening for these erroneous probe readings. The trimmed mean eliminates a specified percentage of the data from both the high and the low ends of the dataset before evaluating the standard mean of the remaining data. For this data set the trimmed mean method sets a limit on how data is cleaned from the data set allowing for the removal of a faulty probe reading (25% of data) or a poor sample (33% of data). The 33% Trim Mean in place of the Full Mean reduced the impact of spectral variation or misreads between samples or probes. The Trim Mean method was found to be at least as successful as the Visual Discard method at cleaning the data set prior to development of the calibration equation (Table 1 and Figure 2). The Trim Mean method was preferred as the decision on data to include in the calibration model as it is not subjective and simple to apply. Major variation is apparent in the spectral data for Blend 3 (75%) which renders it unsuitable for prediction and calibration. These processing variations were attributes to the high moisture of MCC in this compacted blend.




        The 33% Trim Mean method offers a simple and practical solution to dealing with the NIR spectral challenges when applying this PAT technique to roller compacted ribbon in-line/at-line. Further development of this method when used for calibration could investigate the optimal number of ribbon samples to % data trimmed to achieve a statically robust balance of data cleaning. Increasing the number of samples used to during calibration may allow for a greater retention of data in the TrimMean e.g. 5 samples and keeping 3 giving a 20% Trim Mean. The Trim Mean method is therefore no less successful then other simple calibration methods but offers the advantage of a simple, easy to follow and non-biased means of removing NIR data collected due to probe sampling error at-line/in-line.




        Research is funded by the Synthesis and Solid State Pharmaceutical Centre (SSPC) under grant number 12/RC/2275, the Mathematics Applications Consortium for Science and Industry funded by Science Foundation Ireland Investigator Award 12/IA/1683 and the Pharmaceutical Manufacturing Technology Centre, Ireland. Microcrystalline cellulose PH102 donated by FMC Corporation. Innopharma Laboratories for use of the MultiEyeTM NIR Spectrometer.  


1.             Acevedo, D., et al., Evaluation of Three Approaches for Real-Time Monitoring of Roller Compaction with Near-Infrared Spectroscopy. AAPS PharmSciTech, 2012. 13(3): p. 1005-1012.

2.             Gupta, A., et al., Nondestructive measurements of the compact strength and the particle-size distribution after milling of roller compacted powders by near-infrared spectroscopy. J. Pharm. Sci., 2004. 93(4): p. 1047-1053.

3.             Gupta, A., et al., Influence of ambient moisture on the compaction behavior of microcrystalline cellulose powder undergoing uni-axial compression and roller-compaction: A comparative study using near-infrared spectroscopy. J. Pharm. Sci., 2005. 94(10): p. 2301-2313.

4.             Lim, H., et al., Assessment of the critical factors affecting the porosity of roller compacted ribbons and the feasibility of using NIR chemical imaging to evaluate the porosity distribution. Int. J. Pharm., 2011. 410(1-2): p. 1-8.

5.             McAuliffe, M., et al., The Use of PAT and Off-line Methods for Monitoring of Roller Compacted Ribbon and Granule Properties with a View to Continuous Processing. Organic Process Research & Development, 2014. 19(1): p. 158-166.

6.             Liu, H., S. Shah, and W. Jiang, Online outlier detection and data cleaning. Comput. Chem. Eng., 2004. 28(9): p. 1635-1647.


Figure 1 Left panel - Representative spectral data for one piece of ribbon as scanned by the MultiEye™ NIR probes at-line.  Probe 1 (continuous line), Probe 2 (dashed line), Probe 3 (dotted line) and Probe 4 (dashed line in lower absorbance region), each of the 5 scans for each probe is seen as a different color i.e. red, yellow, blue, green and purple. When applying the visual discard method to this spectral data set, probe 4 was removed from the data set. Right panel –Representative spectral data for ribbon samples from the same batch as measured by Perkin Elmer Spotlight off-line.

Table 1. Calibration models and the respective correlation coefficients (r) for each of the three blends as calculated by the three different data discard methods. ED indicates envelope density.


Calibration Method


Calibration model


Trim Mean

Blend 1

ED=0.416 + 3584 x Average Spectral Slope


Visual Discard

Blend 1

ED=0.409 + 3628 x Average Spectral Slope


Full Mean

Blend 1

ED=0.420 + 3788 x Average Spectral Slope


Trim Mean

Blend 2

ED=0.319 + 3788 x Average Spectral Slope


Visual Discard

Blend 2

ED=0.170 + 4257 x Average Spectral Slope


Full Mean

Blend 2

ED=-0.041 + 5587 x Average Spectral Slope


Trim Mean

Blend 3

ED=0.465 + 3021 x Average Spectral Slope


Visual Discard

Blend 3

ED=0.734 + 1515 x Average Spectral Slope


Full Mean

Blend 3

ED=0.474 + 3115 x Average Spectral Slope



Figure 2. Regression of NIR spectral slopes on Envelope Density. Note: symbols 1=Trim Mean samples, 2= Visual Discard samples and 3 = Full Mean samples. Solid line is fitted line for Trim Mean samples, dotted line is for Visual Discard samples.


Extended Abstract: File Not Uploaded