non-invasive ‘through-package’ assessment of the microstructural

8/6/2019 Non-invasive through-package assessment of the microstructural

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LWT 40 (2007) 737743

Research Note

Non-invasive through-package assessment of the microstructural

quality of a model food emulsion by the NMR MOUSE

Adrian M. Haiduca, Elena E. Trezzaa, Dagmar van Dusschotenb, Aleksander A. Reszkac,John P.M. van Duynhovena,

aUnilever Food and Health Research Institute, Unilever R&D, P.O. Box 120, 3133 AT Vlaardingen, The NetherlandsbDSM Research, P.O. Box 18, 6160 MD Geleen, The Netherlands

cSCC Global Technology Centre, Unilever R & D, P.O. Box 120, 3133 AT Vlaardingen, The Netherlands

Received 4 August 2005; received in revised form 11 February 2006; accepted 16 February 2006

Abstract

Recently, NMR has been demonstrated in a truly non-invasive through-package sensor mode, also denoted as the MObile Universal

Surface Explorer (MOUSE). In this feasibility study, we present a first example where we use the MOUSE sensor for assessment of the

microstructural quality of a food material. We have taken model systems consisting of protein-stabilized oil-in-water emulsions, where an

important microstructural quality parameter is water exudation (WE). In order to establish a sound relation between MOUSE signals

and WE, it was necessary to deploy multivariate calibration techniques. It was found that the performance of the MOUSE is comparable

to that of conventional benchtop NMR. Thus it was demonstrated that the NMR MOUSE presents a good option for non-invasive

assessment of microstructural quality parameters, e.g. in manufacturing and in the supply chain.

r 2006 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved.

Keywords: NMR MOUSE sensor emulsions

1. Introduction

Within the foods industry we are witnessing a transition

from the classical process and quality control taking place

in an off-line laboratory, towards non-invasive, on-line

and real-time measurement of product quality parameters.

This has resulted in the tight integration of process and

quality control, and is yielding significant economical

benefits. Many on-line measurement technologies have

become available that can be integrated with process and

quality control in a versatile manner (Senorans, Ibanez, &Cifuentes, 2003). Most of these measurement tools are

based on spectrophotometry (Williams & Norris, 2003)

and provide only feedback on the chemical composition of

the food system of interest, however. Although food

quality is often perceived as compositional, i.e. the presence

of molecules that can be tasted or smelled, the dominating

factor is often the microstructure of the product. The

microstructure of a product is particularly important for

semi-solid foods, ranging from sauces and dressings to

yoghurt, spreads and ice cream. Sofar primarily ultrasound

technology has matured enough to allow the on-line

measurement of solid-to-liquid ratios (Prakash & Ramana,

2003) and (for specific systems) rheological properties

(Bamberger & Greenwood, 2004; Ross, Pyrak-Nolte, &

Campanella, 2004). Despite these successes, one has to

conclude that these techniques have their limitations withrespect to versatility when it comes to assessment of a

broad range of microstructural parameters. NMR is a

good candidate to fulfil such a role, and within the foods

industry it is applied to assess a range of microstructural

features on (relatively) low-cost benchtop spectrometers

(Todt, Burk, Guthausen, Kamlowski, & Schmalbein,

2001). However, since these applications still require that

a sample is taken and placed in the benchtop NMR

spectrometer, this technology still belongs to domain of the

classical process and quality control laboratory.

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www.elsevier.com/locate/lwt

0023-6438/$30.00 r 2006 Swiss Society of Food Science and Technology. Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.lwt.2006.02.026

Corresponding author. Tel.: 3110 4605534; fax: 31 10 4605310.

E-mail address: [email protected]

(J.P.M. van Duynhoven).
http://www.elsevier.com/locate/lwthttp://dx.doi.org/10.1016/j.lwt.2006.02.026mailto:[email protected]:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.lwt.2006.02.026http://www.elsevier.com/locate/lwt


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Recently, NMR has also been presented in a truly non-

invasive mode, by deploying one-sided magnets with built-

in measurement coils (Blu mich et al., 1998), also denoted as

the MObile Universal Surface Explorer (MOUSE). The

MOUSE consists of two separate magnets attached

antiparallel wise to an iron yoke. Sandwiched between

these magnets is a simple RF coil, which is used fortransmitting RF pulses and receiving the NMR signal

(Fig. 1). The first applications of the NMR MOUSE were

in non-invasive assessment of polymer quality (Guthausen

et al., 2000), but also the first applications in food

technology have appeared. These food applications of the

MOUSE focussed on the non-invasive assessment of

chemical compositional parameters (Guthausen et al.,

2004; Pedersen, Ablett, Martin, Mallet, & Engelsen,

2003). However, the aforementioned sensitivity of NMR

to distribution and dynamic state of water implies that

the MOUSE should also be a versatile sensor for the

microstructural quality of foods (Martinez et al., 2003). In

this study, we will present a first example where we use the

MOUSE sensor in this respect.

For this feasibility study we have taken model systems

consisting of oil-in-water emulsion gels, stabilized by

proteins. These systems form complex structures (Haiduc

& van Duynhoven, 2005; Haiduc, van Duynhoven,

Heussen, Reszka, & Reiffers-Magnani, submitted; Pudney,

Hancewicz, Cunningham, & Brown, 2004), built from fat

droplets and a protein aggregate network in which water is

dispersed in pores with a range of sizes (schematically

depicted in Fig. 2). One important quality parameter of

these materials is their water exudation (WE). In the

classical approach, WE is determined by measuring theamount of water that is lost from the material when it is

subjected to gravitational forces. As long as these materials

are contained, e.g. in a tub, water cannot leave the

structure and water is distributed homogeneously. How-

ever, when the material is put in geometry where water can

leave the structure, exudation takes place, which can take

between 1 and 8 h. Recently, also a NMR method was

described that could measure loss of water from a food

microstructure (Hinrichs, Go tz, & Weisser, 2003), but also

this method is intrinsically slow. When a more rapidalternative methodology would become available, in a truly

non-invasive, through-package mode, this would open

opportunities for process and quality control in manufac-

turing and the supply chain.

In our study, we first identified the underlying micro-

structural basis of WE by conventional benchtop NMR.

Subsequently we assessed the microstructural quality

of the model systems by non-invasive, through-package

MOUSE measurements. We will also outline the require-

ment of multivariate calibration techniques for exploiting

experimental data generated by the MOUSE sensor.

2. Materials and methods

2.1. Acquisition

A Bruker Minispec MQ 20 NMR spectrometer (Bruker

BioSpin, Rheinstetten, Germany), equipped with a variable

temperature relaxation probe (dead time 7 ms) was used for

the conventional benchtop NMR experiments. Samples

were measured in a 10-mm diameter tube, filled to a height

of 1 cm. Transverse relation decays, consisting of a free

induction decay (FID) and a subsequent CarrPurcell

MeiboomGill (CPMG) pulse train (Meiboom & Gill,1958) were recorded, using an in-house pulse programme

written in the Bruker Exspel programming language. Using

this combined FID-CPMG method, a large range of

transverse relaxation times can be assessed in a single

experiment. The 901801 pulse spacing in the CPMG

sequence was 0.3 ms. The CPMG decay was sampled by

averaging 20 points at the top of each echo. The FID part

of the sequence was sampled with a time interval of 1 ms.

The decays were measured at 25 1C. All MOUSE NMR

measurements were performed on a Bruker MQ MiniSpec

spectrometer that was connected to a separate radio

frequency (RF) unit and a portable magnet with a built-

in RF coil (RWTH, Aachen, Germany). In order to get

meaningful data, a CPMG train of very short RF pulses is

required. In between these pulses, the NMR signal arises in

the form of very sharp echoes that are each fitted to a

polynomial to determine the amplitude. The phase of the

first five echoes can be determined and the whole echo train

is then subjected to a so-called zero-order phase correction,

using this phase information. Now, the imaginary part of

the complex signal can be disregarded. This improves the

signal to noise ratio and significantly reduces the baseline.

These improvements were implemented directly into the

Bruker control software. Decay signals were recorded

with the RF pulse spacing varying between 50 and 250 ms.

ARTICLE IN PRESS

N

tub

S N

S

Fig. 1. Schematic presentation of the NMR MOUSE.

A

B

C

Fig. 2. Schematic representation of oil-in-water emulsion gel stabilized by

protein: (A) continuous protein gel phase, (B) oil droplets and (C) pores.

A.M. Haiduc et al. / LWT 40 (2007) 737743738


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The number of echoes we acquired was always set to 512.

The number of averages was set to 128. Due to slow T1-

relaxation, the delay between the individual averages was

set to 4 s, which yields an experimental time of 8 min for

one MOUSE measurement.

2.2. Samples

A series of 9 protein-stabilized oil-in-water model

emulsions was prepared, containing vegetable fat, pre-

dominantly native whey protein concentrate (Nutrilac

QU7560, ex Arla foods), skimmed milk powder, potassium

sorbate as a preservative, demineralized water and gelatine.

An emulsion premix was prepared at 60 1C, subsequently

heated to 851C and then homogenized. Heating and

holding steps took 20 min. Homogenization was performed

using an APV Lab1000 homogenizer, single stage at 300

bar by default, and is always preceded by turraxing the

mixture at 8000 rpm for 2 min (Kiokias, Reiffers-Magnani,

& Bot, 2004). Samples were acidified to a pH in the

range 4.54.9 using a 50% citric acid solution in

demineralized water, and homogenized again at 200 bar.

Emulsions were filled in tubs, sealed, and stored at 5 1C

until further analysis. The WE of these samples was

characterized by means of a simple leakage test, and is

expressed as percentage water lost. The repeatability and

reproducibility of the leakage test is 1.6% and 2.8%,

respectively.

2.3. NMR data analysis

Partial least squares (PLS) and multilinear regression(MLR) are well known techniques and their theoretical

background will not be discussed here (Massart, Vande-

ginste, Buydens, de Jong, & Smeyers-Verbeke, 1997). In

this work PLS is used directly on the NMR decays. For

MLR, the decays are first transformed from the continuous

domain to the discrete domain of T2 distributions and

amplitudes using the Nonnegative Least Squares (NNLS)

algorithm (Bro & De Jong, 1997). The decays are averaged,

and the resultant data is fitted without an initial guess of

the number of components or T2 values. The basis matrix

used in the NNLS regression is constructed from the NMR

CPMG magnetization function:Mt M0 expt=T2 (1)

where M0 is the amplitude at time t 0, T2 is the

corresponding relaxation time constant and t is the

acquisition time axis. M0 is set to 1 and the T2 value is

varied across the interval 0.23000 ms with an equidistant

step of 30 ms. In matrix notation, the equation solved with

NNLS is

S AK, (2)

subject to AX0, where S is the experimental NMR decay

matrix, and A are the unknown amplitudes of the

exponential basis function K. The CPMG part follows a

sum of exponential decays fitted with NNLS:

St X

Mn expt=T2n; n 1 to N, (3)

where Mn

is the amplitude of the nth exponential, T2n is the

corresponding relaxation time constant, t is the acquisition

time axis and N is the number of exponential functions or

components in the sample. The sum SMn is smaller thanthe normalized value, the difference is due to components

with very short T2s for which the magnetization decays to

zero in the FID part.

For MLR the following calibration equation is used:

P B0CX

BnMn, (4)

where P is the calibrated parameter, B0 is the MLR

constant and the unit constant C the sum of all

magnetizations. This equation can be rewritten as

P B0M0 X

Mn X

BnMn B0M0

X

B0 BnMn. 5In this work, the term B0 Bn is denoted as scaled

coefficients.

2.4. Monte-Carlo analysis

The value of the amplitudes obtained by a NNLS is

influenced by the noise on the experimental decays. This is

a well-known problem in numerical inversion and the usual

work-around is by the use of a regularization parameter

(Butler, Reeds, & Dawson, 1981; Provencher, 1982).

However, a regularized fit leads to a continuous distribu-

tion of T2s over a wide range, which would impede the useMLR for performing a calibration. Furthermore, in

regularized fitting approaches small disturbances in the

decays are known to introduce relatively large artefacts in

the T2 distributions. Hence, in this work, it was chosen not

to deploy regularization. As an alternative, the influence of

the noise on the NNLS result was tested using a Monte-

Carlo approach. For each recorded decay, the difference

between the NNLS fit and the experimental data (residuals)

is assumed to be due to normally distributed noise.

Simulated noise is constructed using a random number

generator with the same variance as the residuals and then

added to the NNLS fit, thus simulating a new experiment

on the same samples. The resultant new decays are then

fitted again. The process is repeated 100 times, resulting in

a distribution of amplitude values which can be used to

derive statistical parameters.

2.5. Software

The commercial software package SIMCA-P (Umetrics,

Umea, Sweden) was used for (PLS) model development.

Orthogonal Signal Correction (Wold, Antti, Lindgren, &

Ohman, 1998) was applied in order to yield a PLS

model with a single principal component. Data pre-

processing was performed in EXCEL and MATLAB

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A.M. Haiduc et al. / LWT 40 (2007) 737743 739


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(The MathWorks, Natick, MS, USA), using in house

developed routines. Transformation of the NMR decays to

discrete distributions of transverse relaxation times (T2) by

means of NNLS and MLR of the resulting amplitudes

versus functional material parameters was achieved using

Matlab routines developed in house.

3. Results and discussion

3.1. Inspection of conventional and MOUSE NMR decays

Raw NMR decays obtained by conventional benchtop

NMR and the MOUSE are presented in Fig. 3. Whereas

both types of signal were recorded within comparable

measurement times (ca. 8 min), they differ significantly with

respect to signal-to-noise ratio. Furthermore, the MOUSE

signals decay much more rapidly. In order to extract

common T2 populations in the NMR decays, a NNLS

transformation was carried out (Bro & De Jong, 1997). The

NNLS procedure was performed on decays recorded by

conventional (benchtop) means, and by the MOUSE.

The resulting T2 distributions are presented in Fig. 4.

In the NMR decays that were measured by conventional

means, three T2 components can be discerned, which

can be assigned (Haiduc & van Duynhoven, 2005) to (I)

oil (E170 ms), (II) oil and protein-associated water

(E200 ms) and (III) water in pores (4500 ms). For the

NMR MOUSE, two T2 population distributions are

obtained, centred around 10 and 70 ms. The T2 populations

as measured by the MOUSE cannot be related to the ones

assessed by regular benchtop NMR in a straightforward

manner. The strong magnetic field inhomogeneities that are

inherent to the one-sided MOUSE magnets lead to strong

diffusional contributions to the transverse relaxation time.

This is particularly effective for rapidly diffusing watercomponents and less for slowly diffusion oil. From this, we

may assume that the component at 10 and 70ms can

(roughly) be assigned to water and oil, respectively.

3.2. Prediction of WE from NMR decays recorded by

conventional NMR and the MOUSE

Since conventional benchtop NMR decays allow for

physical interpretation, these were first used for explorative

multivariate modelling. The most straightforward method

to build such a model is by means of PLS. PLS has the

advantage that it can predict WE directly from the

continuous NMR exponential decays. As is shown in

Table 1A, this results in a good predicting model for WE.

The PLS model also generates a so-called loading which

represents the signal in the NMR decays that is responsible

for the relation between decays and WE. This signal is

presented in Fig. 5A, but is difficult to be interpreted in a

physical manner. This is due to use of centred and scaled

data in the PLS model. This procedure has the advantage

that it avoids extracting a very strong component which is

just an average NMR decay and has no relation with WE.

The disadvantage, however, is that when dealing with

continuous NMR decays, a loading is obtained with both

positive and negative amplitudes. This complicates the

ARTICLE IN PRESS

0

0.2

0.4

0.6

0.8

1.0

0.1 1 10 100 1000

Time [ms]

0

0.2

0.4

0.6

0.8

1

0.1 1 10 100 1000

Time [ms]

(B)

(A)

Fig. 3. Raw NMR decays obtained by (A) conventional benchtop time-

domain NMR and (B) the MOUSE. Decays were normalized with respect

to intensity at 0.1 ms and time scale (horizontal) is plotted logarithmically.

0 150 300 450 600 750 900 1050 1200 1350 1500

time [ms]

Amplitude

Amplitude

I

II

III

(A)

(B)

Fig. 4. T2 distribution obtained by NNLS fitting of CPMG data obtained

by conventional benchtop NMR (A) and the NMR-MOUSE (B). The

assignments of T2 populations IIII in (A) are outlined in the text.



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straightforward explanation of WE in terms of micro-

structural features, i.e. T2 populations.

In order to obtain calibration models that allow for

physical interpretation, another approach was explored

where MLR is applied to NNLS signal amplitudes

extracted from decays measured by conventional means.

The MLR model is built using the NNLS amplitudes asresponses, and the functional parameters as predictors. The

quality of the NNLSMLR models is illustrated by the plot

in Fig. 6A. MLR is performing similar, or better, as PLS as

is illustrated in Table 1A. The MLR model has the

additional advantage that these can be interpreted in a

physical manner (Haiduc & van Duynhoven, 2005). The

coefficients of the MLR establish a direct linear relation

between the amplitudes of the three T2 populations and the

calibrated parameter. The relaxation times T2 for thesethree populations relate to different microstructural

features of the emulsion, thus enabling physical interpreta-

tion. The scaled MLR coefficients in Fig. 5B represent the

physical relation between microstructural features (in the

form of T2 populations) and the calibrating (functional)

parameters P. In Fig. 5B one can observe that WE is

dependent on population I and (mostly) on pore popula-

tion III. The large T2 value associated with Population III

point towards water confined in relatively large pores in the

continuous phase of the emulsion. The water in these pores

is more prone to drain from the emulsion than other

populations.

The results of both PLS and MLR demonstrate that a

microstructure related quality property can be predicted

from decays recorded on benchtop NMR equipment.

The MLR model is not simply a multivariate black box,

it also allows for building understanding of WE in

ARTICLE IN PRESS

-0.1

-0.05

0

0.05

0.1

0 500 1000 1500 2000 2500 3000

Time [s]

Am

plitude

-50

0

50

100

150

200

250

0 150 300 450 600 750 900 1050 1200 1350 1500

Time [ms]

Amp

litude

(A) (B)

Fig. 5. (A) PLS loading (coefficients) for calibration of conventional benchtop NMR decays with Water Exudation. (B) Scaled coefficients for an MLR

model that predicts Water Exudation based on conventional benchtop decays.

Table 1

Goodness of fit (R2) and root mean square errors (RMSE) of calibration

(Cal) and cross validation (xVal) for PLS and MLR models constructed

from (A) conventional benchtop and (B) MOUSE decays

A B

Conventional MOUSE

PLS MLR PLS MLR

R2 0.93 0.96 0.78 0.97

RMSE-Cal 1.17 1.13 3.05 0.99

RMSE-xVal 1.57 1.71 3.42 1.99

Values in bold indicate strong models.

0

5

10

15

20

25

0 5 10 15 20 25

Predicted Water Exudation [%]

MeasuredWaterExudatio

n[%]

0

5

10

15

20

25

0 5 10 15 20 25

Predicted Water Exudation [%]

MeasuredWaterExudatio

n[%]

(A) (B)

Fig. 6. Goodness of fits of NNLS-MLR models based on (A) conventional benchtop NMR decays and (B) MOUSE decays.



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microstructural terms. The aforementioned PLS and MLR

approaches were also applied to the NMR decays recorded

with the MOUSE. There is a small difference between the

calibration and cross-validation errors of the MOUSE PLS

model (Table 1B), indicating that the model is not

overfitted. Compared to the model based on conventional

benchtop NMR (Table 1A), the MOUSE PLS model is less

favourable. This is because PLS needed only one statisti-

cally relevant component in the MOUSE model, whereas

three components were needed to describe the conventional

benchtop NMR data. Much more promising are the MLR

results obtained after the NMR MOUSE decays were

transformed with NNLS into sets of amplitudes and

observed T2* values (Fig. 4B). The quality of the MOUSE

MLR model for WE (Fig. 6B) is comparable to the results

from the conventional benchtop NMR experiments

(Fig. 6A). We note that only two NNLS amplitudes

were used in the MLR model, hence there is a minimal

risk of overfitting the data. This is also illustrated by

the relative small difference between calibration and cross-

validation errors. MLR model errors for the MOUSE

data are larger than for conventional benchtop NMR

(Table 1B), which can be attributed to the poorer

signal to noise ratio of the MOUSE data. This will havean impact on the value of the amplitudes obtained by a

NNLS fit to the exponential decays. The influence of the

noise on the amplitudes was tested using a Monte-Carlo

approach. The result for the WE model shows that this

parameter is independent of the noise (Table 2), which on

average has a variance (square of standard deviation) of

7105 for the NMR-MOUSE and 2106 for a

conventional benchtop NMR experiment. Simulated

data showed that the NNLS fit becomes unstable when

the noise variance reaches 5 104, almost an order of

magnitude higher than in our experiments. Hence, we

conclude that MOUSE decays can be used to make reliable

predictions of WE.

This work demonstrates that low field NMR relaxome-

try can be deployed to assess the microstructural quality of

food products, by both conventional benchtop NMR as

well as the NMR MOUSE. The models constructed from

conventional benchtop NMR data, have the advantage

that they allow for the understanding of WE in micro-

structural terms. The NMR MOUSE data are more

difficult to be interpreted, but allow for assessment of

WE in a truly non-invasive (through package) mode. This

may offer promising options for non-invasive assessment

of other microstructural quality parameters, i.e. in manu-

facturing and in the supply chain.

Acknowledgements

We acknowledge the European Union (Marie Curie

Fellowship, HPMI-CT-2002-00167, Benchtop NMR tech-

niques for the structural characterization of foods) for the

funding of A.H. and E.T.

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Table 2

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for water exudation

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R2 average from MC simulation 0.971

R2 95% confidence 0.9680.973



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