A comparison of near infrared method development approaches

JOURNALOFNEARINFRAREDSPECTROSCOPY

1

ISSN: 0967-0335 © IM Publications LLP 2009

doi: 10.1255/jnirs.xxx All rights reserved

A comparison of near infrared method development approaches using a drug product on different spectrophotometers and chemometric software algorithms

Assad Kazeminy,a Saeed Hashemi,a Roger L. Williams,b Gary E. Ritchie,c Ronald Rubinovitzd and Sumit Sene,*

aIrvine Pharmaceutical Services, Inc., 10 Vanderbilt, Irvine, CA 92618, USA

bUnited States Pharmacopeial Convention, 12601 Twinbrook Parkway, Rockville, Maryland 20852, USA

cFormer United States Pharmacopeial Convention, 12601 Twinbrook Parkway, Rockville, Maryland 20852, USA

dBüchi Corporation, 19 Lukens Drive, New Castle, DE 19720, USA

eUnited States Food and Drug Administration, 19701 Fairchild, Irvine, CA 92612, USA. E-mail: [email protected]

It is well known that spectral variability in near infrared (NIR) spectroscopy can be attributed to the analyst, sample, sample positioning,

instrument confi guration and software (in both algorithm formats and structures used as well as in the execution of data pre-treatment

and analysis). It is often acknowledged that the single largest factor impacting NIR results is sample presentation. However, what is

obvious but not often acknowledged is that there are instrumental and software differences as well. These differences, evident in the

quality of the spectra, may impact the chemometrics that are subsequently performed and, possibly, the results obtained from the

multivariate statistical models. In order to investigate just what are these sources of variability, and just how much these variations

may impact the results of the multivariate models for predicting the identifi cation of pharmaceutical dosage forms, a study has been

conducted. To the authors’ knowledge, no other systematic study of this kind has been published. In this study, we are interested in

learning what variability, if any, the choices for instrument and software have on the development of a NIR method for the identifi cation

of pharmaceutical dosage forms. Furthermore, we would like to learn what and how do the choices made early on in the experimental

design impact the fi nal quality of the spectra and the resulting multivariate models obtained from these spectra. A study protocol was

designed, using a common data set consisting of four formulations of Ibuprofen, involving three investigating parties, namely, US FDA,

USP and Irvine Pharmaceutical Services and using three NIR instruments, namely (listed in alphabetical order), a Bruker spectrometer,

a Büchi spectrometer and a Foss spectrometer. Based on the results and despite differences in instrument confi guration [dispersive or

Fourier Transform (FT)], number of spectral data points, principal components analysis (PCA) or factorisation algorithms, and valida-

tion modelling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis.

More importantly, this study shows that the same NIR method spectral range and pre-treatment parameters can be used, and that

nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity

of pharmaceutical dosage forms.

Keywords: near infrared (NIR) spectroscopy, instrument variability, chemometric software algorithms, multivariate discriminant analysis, PCA

A. Kazeminy et al., J. Near Infrared Spectrosc. 17, xxx–xxx (2009)

Received: 9 June 2009 ■ Revised: 30 September 2009 ■ Accepted: 11 October 2009 ■ Publication: 2009

2 An NIR Comparison of Method Development Approaches Using A Drug Product

It has been successfully demonstrated that near infrared

(NIR) spectroscopy can be used for the analysis of pharma-

ceutical products using a single instrument and software

confi guration.1–6 The focus of this study was to determine

if the variability observed across different NIR instruments

and chemometric software packages can be an impediment

for the deployment of this technique if different instrument

and software types are to be used for the development of NIR

models.

The United States Food and Drug Administration (US

FDA), the United States Pharmacopeia (USP) and Irvine

Pharmaceutical Services, have undertaken a feasibility

study using NIR spectroscopy to identify, by the chemometric

method of multivariate discriminate analysis using principal

component analysis (PCA) and factorisation, four formula-

tions of Ibuprofen (200 mg): two branded (Advil and Motrin)

and two store-branded (CVS and Rite Aid) Ibuprofen (200 mg)

immediate-release tablets, using three different instrument/

software combinations. The instruments used included

(listed in alphabetical order), a Bruker Vector 22N FT-NIR

spectrometer, Bruker Optik GmbH, Ettlingen, Germany, a

Büchi NIRFlex Solids, Flawil, Switzerland and a Foss XDS

Rapid Content Analyser, Laurel, USA. The software used from

each instrument, respectively, were OPUS 5.5, NIRCal 5.2 and

Vision 3.4. The Unscrambler 9.7, a stand-alone multivariate

analysis and experimental design software package, was

used as referee software to assist in developing a common

model. The Bruker Vector 22N FTNIR spectrometer and the

Foss XDS Rapid Content Analyser used for this study are

both located at the USP, and the Büchi NIRFlex Solids spec-

trometer was located at the Irvine Pharmaceutical Services.

A study protocol was designed, using a common data set

consisting of these four formulations of Ibuprofen, involving

the three investigating parties, using the above-mentioned

different NIR instrument/software combinations. To our

knowledge, this is the first study of its kind investigating

the variability of NIR instrument/software combinations and

their impact on chemometric models for discriminate drug

analysis.

There were four specifi c objectives of the study:

(1) Demonstrate that by the use of the computerised algo-

rithms of PCA and factorisation, three identical, or nearly

identical, spectral libraries can be obtained on three different

instruments. These spectral libraries will be comprised of

the same samples drawn from the same manufactured lots,

scanned the same way and modelled using the same software

settings.

(2) Compare four formulations of Ibuprofen: (200 mg)

two branded and two store-branded Ibuprofen (200 mg).

Immediate-release tablets will result in four distinct clusters

in multi-dimensional space.

(3) Demonstrate that the calibration models are able to analyse

unknown tablets (validation set) and determine if they are

ibuprofen (200 mg) immediate-release tablets from one of the

four known sources of origin that constitute the libraries or

are not from any of these known sources.

(4) The results from each analysis of unknown samples should

give the same predicted results, indifferent of the model used,

and despite instrument, spectra, algorithm, or number of cali-

bration samples, demonstrating that a protocol can be used in

each laboratory, independent of location, device, analyst and

software used to perform the identifi cation.

ExperimentalInstrumentation and software

The instruments that were used included a Bruker Vector 22N

FT-NIR spectrometer with a spectral range of 4000–12000 cm–1

and spectral resolution set at 8 cm–1 (Bruker Optik GmbH,

Ettlingen, Germany), Büchi NIRFlex Solids with a spectral

range 1000–2500 nm (10,000–4000 cm–1) and spectral resolu-

tion set at 8 cm–1 (Büchi, Flawil, Switzerland) and Foss XDS

Rapid Content Analyser with a spectral range of 400–2500 nm

(25,000–4000 cm–1) and a spectral resolution set by the speci-

fi ed bandpass of 9 nm (Foss NIRSystems, Inc., Laurel, MD, USA).

The software used for each instrument were, respectively, OPUS

5.5, NIRCal 5.2, and Vision 3.4. The Unscrambler 9.7, a stand-

alone multivariate analysis and experimental design software

package (CAMO Technologies Inc., Trondheim Norway), was

used as referee software to assist in developing a common

model.

Validation of instrument operation

Tests for wavelength accuracy, photometric precision, accu-

racy and noise were performed on all of the instruments prior

to tablet measurements as recommended by the USP general

information chapter <1119> Near-Infrared Spectroscopy

(http://www.usp.org/USPNF/revisions/usp31nf26secondSup-

plement01.html.7 (Graeme has asked me to reference this

as Reference 7 and renumber the remaining references. Is

this okay?) Vendor recommended performance tests for each

instrument were run as well. The tests are routine quality

control tests of the instrument performance and as such were

performed at the prescribed intervals to verify correct instru-

ment performance.

Sample sets

Table 1 lists the sources for the samples used for the study. Ten

lots of four formulations of Ibuprofen (200 mg) were obtained:

two branded (Advil, Motrin) and two store-branded (CVS, Rite

Aid) Ibuprofen (200 mg) immediate release tablets. The formu-

lations for each are shown in Table 2. Three sets of samples

were prepared for each instrument. These sets comprise the

calibration set, test set and validation set. For this study, manu-

facturer lots 1–8 were used for the calibration set and test set

and manufacturer lots 9 and 10 were set aside and used for the

validation sets. While one of the purposes of the protocol was

Introduction

A. Kazeminy et al., J. Near Infrared Spectrosc. 17, xxx–xxx (2009) 3

to control the preparation of the calibration, test, and validation

sample sets, it was found later that the calibration set prepared

for the Foss instrument was missing one tablet (see Table 6

footnote) and the validation set for the Foss instrument and

the Bruker instrument were different from the validation set

prepared for the Büchi instrument, containing only 28 tablets

from the Motrin validation set instead of 40 tablets originally

intended, all due to packaging errors. This error had no effect

on the outcome of the study (model prediction accuracy of vali-

dation samples) as will be shown later in this paper. The sample

sets were prepared as shown in Table 3.

Sample collection and analyses

The study protocol was modelled on that described by Scafi

and Pasquini.8 The multivariate chemometric approach to

calibrate, validate and maintain the spectral libraries was

based on work proposed by Workman and Brown.9,10 Log

1/R (sometimes referred to as Absorbance) spectra from the

Bruker and Foss instrument, and refl ectance spectra from

the Büchi instrument, were collected from each tablet for

the calibration and test sets by averaging 32 scans in diffuse

reflectance mode against a 99% reflectance background

reference. The spectra were collected across the following

ranges from each instrument: Bruker samples were

scanned from 11,999 cm–1 to 3999 cm–1 (833 nm to 2500 nm),

Büchi samples were scanned from 10,000 cm–1 to 4000 cm–1

(1000 nm to 2500 nm) and using the Foss instrument, each

tablet was scanned from 400 nm to 2500 nm, Photographs of

the tablets are shown in Figure 1. The Unscrambler 9.7 was

used to investigate the Foss calibration set spectral proper-

ties that could be used to distinguish each formulation. Tables

4 and 5 show the series of spectral pre-treatments that were

tested for use in order to achieve a PCA that could be used

to determine the identity of the samples from the validation

sets. The test sets were used to optimise the prediction of

the fi nal models before being used to predict the validation

sets. The Bruker software, OPUS 5.5, uses the factorisa-

tion method to model and so the calibration set from this

instrument, while subjected to PCA in The Unscrambler 9.7,

required a different approach when constructed in the OPUS

5.5 software. Table 6 lists the model values for the calibration

modelling approach.

Development of NIR

calibrations

Based on observations from the Unscrambler 9.7 exploration,

a Savitzky–Golay, fi rst derivative, 21 point smoothing, 3rd order

polynomial, from 1000 nm to 2500 nm and a PCA model, using

Mahalanobis distance in the wavelength range from 1400–

1500 nm, was postulated as the optimal model to apply to the

Bruker, Büchi and Foss calibration data sets. Figures 2(a), 2(b)

Lot Brand—Advil Brand—Motrin Generic—CVS Generic—Rite Aid

1 B946681 PCA189 6EE0102 P45032

2 × 200 = 400 tablets 3 × 100 = 300 tablets 2 × 250 = 500 tablets 1 × 500 = 500 tablets

2 B87154 LLA103 6HE0515 P44389


3 B94669 PCA112 7BE0119 P424477


4 B27624 PCA226 7AE0039 P44686


5 B91364 PBA123 7CE0268 P14360

2 × 150 = 300 3 × 100 = 300 tablets 1 × 750 = 750 tablets 3 × 100 = 300 tablets

6 B73322 PBA194 7AE0699 P42476


7 B33863 PAA016 6LE0478 P42498


8 B91414 PBA186 7AE0270 P44151


9 B98483 PEA106 6GE0118 P44688


10 B91386 LLA329 7BE0606 P42058


Table 1. Sources of Ibuprofen (200 mg) samples used for this study.


and 2(c) show that four clusters can easily be separated from

each other in all three data sets. The following discussions

track the model development in the OPUS 5.5, NIRCal 5.2 and

Vision 3.4 software packages.

Pre-treatment I: baseline correction

Based upon the exploration of data pre-treatments shown in

Tables 4 and 5, a baseline correction using the function:

f(x) = x – min(X),

where x is a variable and X denotes all selected variables for

this sample, was fi rst applied in the Unscrambler 9.7. A base-

line correction was shown to be the simplest pre-treatment

applied to the calibration data sets to effect separation of all

four formulations, although barely. Furthermore, it was deter-

mined that while it is possible to apply a baseline transform

function to the Foss calibration set using the Vision 3.4 soft-

ware, the same mathematical transformation was not found

to be available in the Bruker or the Büchi software.

Using the Bruker OPUS 5.5 software, the closest approxima-

tion to the baseline offset function used in the Unscrambler 9.7

would be a normalisation function. This would then be followed

by the Savitzky–Golay, first derivative, 21 point smoothing,

3rd order polynomial, from 1000 nm to 2500 nm, followed by

factorisation (which is a Bruker PCA algorithm using Euclidian

distance within the Opus IDENT set-up module where the cali-

bration set resides. It was further noted that the default fi rst

derivative transformation for the OPUS 5.5 software is a cubic

polynomial equation.

The Büchi NIRCal 5.2 software required the use of several

spectra manipulations; to reverse the x-axis, orient the spectra

in the same direction as they are for the Foss and Bruker data,

prior to performing the PCA to the data [Figures 3(a), 3(b) and

3(c)]. The Savitzky–Golay fi rst derivative transformation for

the NIRCal 5.2 software is also a cubic polynomial equation.

In addition to other derivative functions, the NIRCal software

allows the use of customisable linear fi lters, which allowed

matching to the same derivative treatment used in the other

Advil Motrin CVS-Ibuprofen Rite Aid-Ibuprofen

Acelytated monoglycerides

Beeswax

Propylene glycol

Cellulose

Lactose

Sodium starch glycolate

Ethoxyethanol

Lecithin

Pharmaceutical glaze

Povidone

Semithicone

Sodium benzoate

Sodium lauryl sulphate

Sucrose

Fd&C yellow #6

Magnesium stearate

Polydextrose

Polyethylene glycol

FD&C yellow #6

Magnesium stearate

Polydextrose

Polyethylene glycol

Croscarmellose sodium

Microcrystalline cellulose

Pharmaceutical shellac

Pregelatinised starch

Carnauba wax

Shellac

Ppregelatinised starch

Carnauba wax

Hypromellose

Carnauba wax

Hypromellose

Croscarmellose sodium

Microcrystalline cellulose

Hypromellose

Iron oxides

Silicon dioxide

Corn starch

Ibuprofen

Stearic acid

Titanium dioxide

Iron oxides

Colloidal silicon dioxide

Corn starch

Ibuprofen

Stearic acid

Titanium dioxide

Fumed silica gel

Corn starch

Ibuprofen

Stearic acid

Titanium dioxide

Iron oxides

Colloidal silicon dioxide

Corn starch

Ibuprofen

Stearic acid

Titanium dioxide

Table 2. Formulations of Ibuprofen (200 mg) samples.


software packages. The resulting fi lters for the Büchi calibra-

tion set are listed in Table 6.

Pre-treatment II: Savitzky–Golay fi rst derivative, 21 point smoothing, cubic polynomial

The smoothing and differentiation of paired data by the proce-

dure of simplifi ed least squares, now called the Savitzky–

Golay Filter,11 was developed for the removal of the random

noise from paired data sets, such as that obtained for NIR,

Log 1/R versus wavelength. For the data sets from this study,

it can be shown that the calibration by PCA or factorisation is

most affected by sampling variation manifested as baseline

offsets. The offsets result in signifi cant overlap of spectra

with closely resembling spectral features, which cannot be

completely resolved in spectral space. Tables 4 and 5 show

a systematic approach of pre-treating the calibration sample

spectra. The pre- treatments were applied and investigated

for their ability to remove sampling variation from the spectra.

The plus sign indicates that the samples from these formula-

tions can be separated into distinct clusters from the other

samples of a different formulation using the associated pre-

treatment, whereas the minus sign indicates that samples

from these formulations cannot be separated into distinct

clusters from the other samples of a different formulation

using the associated pre-treatment. The contribution of the

variability from the samples can be seen in Figures 3(a), 3(b)

and 3(c). The baseline offset is evident in the exploded plot of

the spectra in Figure 4 and the effect of smoothing and deriv-

ative pre-treatment is also evident as well (Note: The number

of data points applied for smoothing the spectra during deri-

vatisation were investigated and 21 data points was found

to be optimal for discerning spectral features of the four

formulations in spectral space). Particularly noticeable at

the wavelength 1440 nm, one can observe a sharp band in the

untreated spectra, which is attributed to Innovator A samples

(Advil) [Note: This is due to the sucrose in the formulation

as indicated in Table 2. The origin of this unique band is

discussed by Davies and Miller.12 The impact of band reso-

lution from dispersive and FT is most noticeable in Figure

4, which is the expanded view of the first derivative and

smoothed spectra. The result of applying the Savitzky–Golay

fi rst derivative, 21 point smoothing fi lter is that it changes

the directions of the of the Generic A and Generic B spectra

relative to both the Innovator A and Innovator B spectra in the

region between 1400 nm and 1500 nm.

Principal component calculations

The PCA models [either by Mahalanobis distance (MD) for the

Foss and Büchi instruments or Euclidian distance (ED) for the

Bruker instrument) resulted in correct and accurate predic-

tions of all three validation sets. Mahalanobis distance is the

statistical distance taking into account the variance of each

variable and the correlation coeffi cients. In the case of a single

Laboratory/instrument Experimental

samples

Innovator

manufacturer

Advil

Innovator

manufacturer

Motrin

Generic

manufacturer

CVS

Generic

manufacturer

Rite Aid

Total

United States

Pharmacopeia/

Foss

Calibration Set 6 tabs × 8 lots = 48 6 tabs × 8 lots = 48 6 tabs × 8 lots = 48 6 tabs × 8 lots = 48 192

Test Set 2 tabs × 8 lots = 16 2 tabs × 8 lots = 16 2 tabs × 8 lots = 16 2 tabs × 8 lots = 16 64

Validation set 20 tabs × 2 lots = 40 14 tabs × 2 lots = 28 20 tabs × 2 lots = 40 20 tabs × 2 lots = 40 148

United States

Pharmacopeia/

Bruker




FDA/Irvine Pharmaceutical

Services, Inc./

Büchi




Total 312 288 312 312 1224

Table 3. Study design.

Figure 1. Ibuprofen (200 mg) tablets. Upper left, Advil, upper

right, Motrin, lower left CVS, and lower right, Rite Aid.


variable, it is the square of the distance (between two objects,

or between an object and the centroid) divided by the variancea

Euclidian distance is simply the distance between two varia-

bles and is calculated as the arithmetic difference, i.e. variable

1–variable 2. For a bi-variate model, the squared distances

between two vectors in multi-dimensional space are the sum

of squared differences in their coordinates. As noted previ-

ously, the algorithms used by each software were different

by the calculations employed for score distances. The signifi -

cance of this difference is that, even though the distances were

calculated in the principal component space (PC space) for the

Foss and Büchi calibration sets, and in wavelength space for

the Bruker calibration set, on similarly pre-treated spectra,

using the same wavelength range, and on different number of

principal components, the models gave identical predictions

for the validation sets (see Table 7). The reason that the PCA

calculation is invariant in either wavelength space or PC space

is explained by De Maesschalck et al.6

The supervised training by PCA, a discriminant analysis

technique, relies on the measurement of distances between

objects in order to achieve classifi cation. As noted by De

Maesschalck et al., this can occur in wavelength space or

in PC space, and on normalised or unnormalised spectra.

Another factor that De Maesschalck et al. point out, that may

not be obvious to a casual user of NIR techniques, is that

“the MD and the ED can also be calculated using a smaller

number of latent variables (PCs) obtained after PCA analysis

instead of the original variables. In this case, the MD, however,

does not need to correct for the covariance between the vari-

ables, since PCs are by defi nition orthogonal (uncorrelated).

However, the way each of the residual PCs is weighted in the

computation of the distance must be taken into account.” The

paper goes on to clarify the relationship between the ED and

the MD, particularly how each is calculated in the original

variable space and the PC space. It should be noted here

that the Bruker software, OPUS 5.5, utilises an approach

referred to as factorisation, which is explained as a spectral

distance calculation. Table 7 gives the model values for each

Spectra treatment

1000–2500 nm

Cluster

Innovator A Innovator B Generic A Generic B

Untreated spectra + + – –

Baseline corrected + + + +

First derivative + – + +

Second derivative + + – –

Baseline corrected

First derivative

+ + – –

Baseline corrected

Second derivative

+ – + +

*Pre-treatments applied to the raw spectra that resulted in a separate and distinct cluster for any member of the four sample sets following principle com-

ponent analysis are denoted by a plus sign, and pre-treatments that did not result in separate and distinct clusters are denoted by a minus sign. Plus signs,

in bold, indicates that all four sample sets were successfully separated

Table 4. Data pretreatments from 1000–2500 nm.

Spectra treatment

(1400–1500 nm)

Cluster

Innovator A Innovator B Generic A Generic B

Untreated spectra + – – –

Baseline corrected + + – –

First derivative + + – –

Second derivative + or yes? – – –

Baseline corrected

First derivative+ + + +

Baseline corrected

Second derivative+ – – –

**See footnote from Table 4

Table 5. Data pre-treatments from 1400–1500 nm.

aFrom a glossary provided with the permission of Bryan Prazen of the

Synovec Group, Department of Chemistry, University of Washington.

Found at http://www.spectroscopynow.com/coi/cda/detail.cda?id=101

18&type=EducationFeature&chId=9&page=1


calibration from each instrument and the Camo software.

Figure 4 shows the expanded view of the derivative spectra,

and Figure 5 are the scores plots from each of the software

(Should this phrase be deleted?).

Unscrambler 9.7 Vision 3.4 OPUS 5.5 NIRCal 5.2

Calibration set 191 191* 192 192

Test set 64 64 64 64

Validation set 148 148 148 160

Raw spectra range 400–2499.50 nm 400–2499.50 nm 11999.86–3999.952 cm–1 4000–10000 cm–1

Number of raw

data points

4200 4200 2075 1501

Baseline

transform**

Baseline offset

f(x) = x–min(X)

(1000–2499.50 nm)

Math treatments

baseline correction

(1000 nm)

Manipulate

offset normalisation†

after transforming

spectra from

wavenumber to

wavelength

(1000–2500 nm)

Add constant, •

Constant = –1000

Shift neg to•

0, 2500–1000

(total 1501/1501)

N/A N/A Change from

wavenumber to wave-

length

Change from

wavenumber to

wavelength

N/A N/A N/A Absorbance Log10(1/×)

Savitzky–Golay

First derivative

(SG 1st deriv.)

(1000–25000 nm),

using a 3rd order

polynomial,

21 point smoothing

Modify /transform/

derivatives/S. Golay/

variables

(1000 nm–2500 nm)/

1st derivative/

21 points smoothing/

3rd order polynomial

Math treatments

Savitzky–Golay

First derivative/

Region

minimum/1000 nm/

region maxi-

mum/ 2500 nm/21

point, cubic spline

polynomial

Evaluate/Setup

Identity test method/

Load method/

Parameters/

Preprocessing/First

derivative 21 points/

Regions/7695.18 cm–1–

6248.72 cm–1, cubic

spline polynomial

Linear fi lter (84075,

10032, –43284,

–78176, –96947,

–101900, –95338,

–79564, –56881,

–29592, 0, 29592,

56881, 79564, 95338,

101900, 96947, 78176,

43284, –10032,

–84075, 3634092)

PCA Task Mahalanobis

Distance in Principal

Component

Space/1400–1500 nm

Mahalanobis

distance in prin-

cipal component

space/spectral

fi ltering/ wavelength

min.1400 nm/

wavelength max.

1500 nm

PCs: 5

Threshold: Probability

Level: 0.999

Factorisation: six

factors (in wavelength

space)

Threshold: 0.25/1400–

1500 nm

Mahalanobis

Distance in Principal

Component

Space/1400–1500 nm

*Only seven tablets were packaged for sample 6UFGB (lot 6 USP/Foss/Generic B–Rite Aid), all other lots from UFGB: 1,2,3,4,5,7, and 8 were packaged to

contain eight tablets, six for the Calibration set and two for the test set.

**Since the baseline algorithms were all signifi cantly different and do not give suffi ciently similar spectral pre-treatments, it was decided to drop this step

and proceed directly to the derivative/smoothing step on all calibration data.

†The attempt to use normalisation to match the baseline correction from the Vision 3.4 and Unscrambler 9.7 software was met with another unexpected ob-

stacle which forced the investigators to not use a baseline correction as the fi rst step of pre-treating the calibration spectra. The normalisation pre-treatment

algorithm resides in a different module than the IDENT module where the calibration model resides. In order to carryout the pre-treatment on the calibration

and subsequent unknown samples, a macro that would pre-process the spectra and automatically load those spectra into OPUS IDENT as well as being able

to analyse an unknown sample using offset normalisation spectral pre-processing and automatically output a result, would be required. Since this is only a re-

quirement of the OPUS 5.5 software, it was decided to drop this step and proceed directly to the derivative/smoothing step on all calibration data.

Table 6. Model values for each calibration.


2b

Innovator B

Innovator A

Generic B

Generic A

2a

Innovator A

Innovator B

Generic B

Generic A

22c

Innovator A

Innovator B

Generic B

Generic A

Figure 2. (a) Büchi PCA Score Plot (Unscrambler 9.7) using, (b) Foss PCA Score Plot (Unscrambler 9.7) and (c) Bruker PCA Score Plot

(Unscrambler 9.7).

(a)

(b)

(c)


3

lo

g (

1/R

)

Innovator A

3b

(b)

Lo

g (

1/R

)

WWavelength

Lo

g (

1/R

)

(c)

Figure 3. (a) Büchi raw spectra plot (NIRCal 5.2), (b) Foss raw spectra plot (Vision 3.4) and (c) Bruker raw spectra plot (OPUS 5.5).

(a)

(b)

(c)


(a)

(c)

(d)

(b)

Büchi

18

(a)

(c)

(d)

(b)

(a)(b)

(c)

(d)

Foss

.0010

.0005

0

.0005

.0010

.0015

.0020

1400.61nm1411.29nm1422.12nm1433.13nm1444.31nm1455.66nm1467.19nm1478.91nm1490.82nm

Variables

Line Plot

(a)

(b)

(c)

(d)

Bruker

Figure 4. Expanded view of Büchi, Foss and Bruker derivative spectra of calibration and test set: (a) Innovator A, (b) Generic A, (c)

Generic B and (d) Innovator B.


Validation results and

discussion

Table 7 lists a summary of the results of the validations sets

from the, Bruker, Büchi and Foss instruments, respectively.

The results demonstrate that all four objectives of the study

were met. Four distinct computerised algorithms of PCA

and factorisation were used to construct three separate

spectral libraries from a common calibration and test set,

each residing on different instruments in different laborato-

ries and one residing on stand-alone software, the referee

model. The use of the “referee” model helped establish the

baseline values for the model parameters such as spectral

range, spectra pre-treatment, calibration algorithms, etc.

that could be used across the various software platforms

despite the variations in instrument bandwidth, spectral data

points, algorithms for smoothing, derivative and other calcu-

lations. Specifi cally, while it was found that a PCA model

based on calculating Mahalanobis distance in PC space,

second derivative Gap 20, second order polynomial was suffi -

cient to model the calibration and test set on two of the three

instruments, it became evident early on in the experiment

that by using exactly the same model consisting of the same

algorithm calculation and same pre-treatment routine was

not possible. However, by the use of the referee software

which, when loaded with the calibration set from all three

instruments, one could easily determine how to optimise the

spectral range, and smoothing and derivative pre-treatment

parameters in order to achieve similar calibration and test

set parameters. The limiting calibration setting was found to

be the polynomial function, which effects how many points

are used to calculate the smoothing and derivative function.

As was shown in Figure 4, there is a very narrow spectral

range (100 nm or 1446.46 cm–1 on which separation can be

made on the four data sets in spectra space.

Having met the fi rst objective with some deviations, the

second, third and fourth objectives were easily met, as

the three models were successful at predicting the vali-

dation sets for each instrument, resulting in four distinct

clusters in multidimensional space, each cluster repre-

senting the innovator and generic brand Ibuprofen formula-

tions. Additionally, samples from Generic A lots 6GE0118

(CVS Brand) and P42058 (Rite Aid) were correctly identifi ed

as not belonging to Generic A lots comprising the cali-

bration sets for all three data sets. It was observed that

these samples have a distinct banding pattern in the region

from 1400 nm–1500 nm from all other sample spectra. The

most likely cause of this is assuredly due to the presence

of a component that absorbs in the NIR region not found in

those samples comprising the calibration set. As a result of

the new band in the critical region between 1400–1500 nm,

these samples also produce a fi fth separate and distinct

cluster in PC space.

It must be mentioned here that this experiment, being

performed in different laboratories within different organisa-

tional cultures, was totally driven by a protocol that was jointly

crafted and agreed upon prior to execution. The authors feel

that this is a key point since this experiment was designed to

meet specifi c objectives, despite the fact that the instruments,

software and personnel were at different locations. This, of

course, was not the major factor. The major factor was trying

to coordinate all of the steps within the protocol from within

different organisations. While NIR experiments are generally

described as non-destructive, fast and cost effective, when

done on a large scale they require planning, discussion and

coordination. This is rarely mentioned. It is hoped that this

example may serve as a model for future applications that

involve large sample sets and multi-organisations using

multiple instrument–software combinations. The current

global pharmaceutical counterfeiting problem is one area that

should benefi t from examples like the one demonstrated in

this paper.

ConclusionsThe specifi c objective of the study was to obtain log 1/R spectra

of four formulations of Ibuprofen (200 mg) from two branded

Instrument Lot Innovator A

tablets

Innovator B

tablets

Generic A Generic B

Foss 9 20 20 0 20

10 20 8 0 20

Bruker 9 20 20 0 20

10 20 8 0 20

Büchi 9 20 20 0 20

10 20 20 0 20

Twenty tablets were tested for each combination of instrument and lot, except for Lot 10 of Innovator B where only eight were tested on the Foss and Bruker

instruments

Table 7. Number of tablets identifi ed.


and two store-branded Ibuprofen (200 mg) immediate-release

tablets and use them to design, develop, validate and deploy a

calibration model that can subsequently be used to correctly

classify by discriminant analysis using PCA, log 1/R spectra

from unknown samples (validation set) on NIR instruments of

varying types and software confi gurations.

This experiment was designed to study the impact that NIR

instrument hardware and software configurations have on

NIR method development. Several variables were detected

and assessed. Spectrometer types, sample holders, spec-

tral acquisition settings, data pre-treatments and PCA algo-

rithms were studied. NIR method development was attempted

by three different analysts on three different instruments

located in two different laboratories. It was determined

that even though identical samples were used for model-

ling and prediction, and the same calibration approach was

tried on the accompanying software, spectra differences were

observed due to the number of data points, and that these

impact the ability to perform the same or similar data pre-

treatments in different software. Furthermore, the algorithms

employed in each software platform may limit the ability to

deploy a method developed on any single software platform

to be deployed across different software platforms. However,

despite the differences observed, it was possible to find a

common method using each software that enabled accurate

predictions of the validation samples when each model was

used independent of instrument and software confi guration.

Knowing the sources of variability that impact the log 1/R

NIR spectrum will minimise the overall prediction varia-

bility and increase the likelihood of correctly classifying by

discriminant analysis, the log 1/R spectra from unknown

samples subsequently measured and compared to the spec-

tral library and classifi ed by the calibration model, when

model parameters are used on different instrument and

software combinations.

It was found that using Savitzky–Golay, fi rst derivative, 21

point smoothing, third order polynomial, pre-treated spectra

and either PCA or factorisation model (either by MD in prin-

cipal component space for the Foss and Büchi or ED in wave-

length space for the Bruker Factorisation method) resulted in

different models but possessing the same accuracy capabili-

ties (100%) for predicting samples comprising similar valida-

tion sets. Each model consisted of 192 calibration samples

(except the Foss calibration set which had one missing tablet

for a total of 191 calibration samples) and 64 test set samples

developed for each NIR instrument, Each model correctly and

accurately (100%) predicted 160 validation samples using the

Büchi model, and 148 validation samples using the Bruker

and Foss models (Graeme Batten thinks that this sentence

should be deleted. Do you agree?). One validation sample set,

a store-branded Ibuprofen (200 mg) immediate-release tablet,

was correctly identifi ed as not belonging to the samples repre-

sented in the calibration set by all three models. Based on

these results, and despite differences in instrument confi gu-

ration [dispersive or Fourier Transform (FT)], number of spec-

tral data points, PCA or factorisation algorithms and validation

modelling approach, exact and accurate spectroscopic results

can be achieved using NIR spectroscopy for discriminate anal-

ysis. More importantly, this study shows that the same NIR

method spectral range and pre-treatment parameters can

be used and that nearly the same multivariate models can

be obtained, despite instrumental and software differences,

to accurately predict the identity of pharmaceutical dosage

forms.

AcknowledgementsThe authors are grateful for the contributions to this project

from each of the following scientists: Darrell Abernathy,

Rebecca Allen, Todd Cecil, Walter Hauck, Andrea Iwanik,

Steven Lane, Samir Wahab and Patricia White from USP,

Rudy Flach, Charles Petersen and Heather Coffi n from Irvine,

William Martin from the FDA, Michael Surgeary from Büchi,

Cynthia Kradjel from Integrated Technical Solutions and Verne

Hebard from Bruker.

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Documents

A comparison of near infrared method development approaches