J Sci Food Agric 1996,70,347-354

ComDarison of the Acoustic and Mechanical Signitures of Two Cellular Crunchy Cereal Foods at Various Water Activity Levels Ralf Tesch, Mark D Normand and Micha Peleg* Department of Food Science, University of Massachusetts, Amherst, MA 01003, USA (Received 6 February 1995; revised version received 20 July 1995; accepted 22 September 1995)

Abstract: Cheese balls and croutons at various water activity levels (0.1 1-0.75) were compressed between parallel metal plates using a Universal testing machine and their acoustic emissions were recorded at compact disc quality (sampling rate of 44.1 kHz). The sound wave record (up to about 2 MB) had high intensity bursts at irregular intervals. These records were compressed after the back- ground noise had been filtered out to produce files of less than 48 kB. The com- pressed signatures were characterised by their mean and peak amplitude, two measures of the sound emission intensity, and by the amplitude’s standard devi- ation and the mean magnitude of the power spectrum, two measures of the acoustic signature complexity. All four parameters could be used to monitor the plasticisation effect of water. Although their magnitudes were correlated, they did not always change in unison upon moisture sorption. The standard deviation and mean magnitude of the power spectrum of the compressed acoustic signa- tures were only broadly correlated with their correspondents in the normalised mechanical signatures primarily because they were not determined from the same particles and because the latter, for technical reasons, were sampled at the low rate of 6 Hz.

Key words: cereal, acoustics, moisture, texture, mechanical properties, crispness, glass transition.

INTRODUCTION the amplitude(intensity)-time record into a power- frequency spectrum with the fast Fourier transform

Perhaps the most salient feature of crunchy cereal foods (FFT) algorithm rather than the frequency analyser is the noise emitted during their mastication. It is pro- used previously (eg Seymour and Hamann 1984; Lee et duced by mechanical failure events upon their disinte- al 1990). gration and its intensity, as well as other characteristics, Recently, microcomputers equipped with sound strongly depends on the specimen’s moisture content or recording and playback accessories have become widely water activity (aw). available. Although primarily designed for editing

Acoustic aspects of foods have been studied since the music, they can also be used in the analysis of any type early 1960s (Drake 1963, 1965). They mainly focused on of sound, the ‘crunch’ of foods included, recorded at or the properties of the acoustic signature and its relation below digital audio tape (DAT) quality. The problem is to perceived textural attributes (eg Vickers and Bourne that with the corresponding high sampling rates (up to 1976; Vickers and Wasserman 1979; Christensen and 48 kHz) the data files can be very large. For example, a Vickers 1981; Vickers 1981; Mohamed et a2 1982) and digital record of a typical ‘crunch’ of a compressed on instrumental and mathematical methods for the cheese ball snack (see below) recorded at compact disc signal recording and analysis (eg Seymour and Hamann (CD) quality can be on the order of 2-4 MB in size. 1984, 1988; Lee et al 1988, 1990). The latter has been Such large files cannot be processed by most standard greatly facilitated by the widespread availability of statistical packages either at all or within an acceptable computer programs that can perform the conversion of time frame. They can, however, be processed and the

results stored on optical disks using certain advanced * To whom correspondence should be addressed. programs such as MATLAB (see below).

J Sci Food Agric 0022-5142/96/$09.00 0 1996 SCI. Printed in Great Britain 347

348 R Tesch et a1

The objectives of this work were to characterise the acoustic signatures of selected crunchy foods recorded at CD quality in quantitative terms and to use the latter to monitor the effects of increased water activity on the ‘crunch’ loss. It has been recently documented how increased water activity affects the jaggedness of the mechanical signature (Barrett et al 1992; Ulbricht et a1 1994; Wollny and Peleg 1994), and how moisture con- tents affect the intensity of the emitted noise (Attenburrow and Davis 1993). Since both the mechani- cal and acoustic signatures change simultaneously as a result of moisture sorption, it is of interest to know whether or which corresponding features change in unison. Consequently, an additional objective of the work was to correlate ‘jaggedness’ measures of both types of signature to reveal whether they can be used interchangeably to monitor the effects of water activity.

MATERIALS AND METHODS

Cheese balls and croutons were purchased at a local supermarket. They were removed from the package and stored in evacuated desiccators over saturated solutions of LiC1, CH,COOK, MgCl,, K,CO,, Mg(NO,), , NaBr, NaNO, and NaCl at an ambient temperature of 25°C. This corresponds to water activity levels of about 0.11, 0.23, 0-33, 0.43, 0.52, 0.57, 0.65 and 0.75, respec- tively (Greenspan 1977). Specimens from each desicca- tor (4-5) were removed and tested individually after 4-7 days, a time sufficient to reach a constant weight and hence practical equilibrium.

Mechanical testing

Each snack specimen, after being weighed and having its dimensions recorded with a caliper, was compressed diametrically with an Instron model 1000 Universal Testing Machine (Instron Corp, Canton, MA, USA) interfaced with a Macintosh I1 microcomputer through a Strawberry Tree interface card. A program written by one of the authors (MDN) was used to operate the instrument and to collect and process the data as described in Barrett et al(1992) and Rohde et a1 (1993a, b). The crosshead speed in all the experiments was 20 mm min-’ and the data retrieval rate was 6 points s- l , that is, 18 points per mm deformation.

Normalised mechanical signatures

The digitised force-time relationships (120-200 points) were fitted with a fourth degree polynominal model using the MATLAB 4.2a package for the Macintosh (The MathWorks Inc, Natick, MA, USA). The normal- ised (dimensionless) force was calculated by dividing the residuals by the fitted value (Wollny and Peleg 1994). The data corresponding to up to about 15% strain were discarded since their exaggerated fluctuations are an

artefact of the fitting procedure (Barrett et al 1992; Wollny and Peleg 1994).

The truncated file is hereby referred to as the ‘nor- malised mechanical signature’ or simply the ‘mechanical signature’ (Fig 1). It was subjected to the same mathematical/statistical analyses that were applied to the acoustic signatures (see below).

Acoustic recording

Specimens (individual particles) were also compressed between parallel metal plates at a rate of 20 mm s-’ using a QTS 25 Stevens testing machine (Stevens Co, Hertfordshire, AL, USA). This machine was borrowed because of its quieter operation in comparison with the Instron. The compression took place in a cardboard insulated cell to reduce external noise. The noise emitted by the specimen was recorded with a micro- phone connected to an Apple Quadra 840AV micro- computer (32 MB RAM, 500 MB hard disk) at a sampling rate of 44.1 kHz. Tests were run several times without a specimen to identify the testing system’s back- ground noise which was later filtered out from the specimen’s raw acoustic signature (see below) using the SoundEdit 16 1 .O. 1 program (Macromedia Inc, San Francisco, CA, USA). The signature was then saved on an optical disk for later processing and analysis.

Treatment of the acoustic signal

The acoustic emission of the crunchy foods tested was composed of bursts (Fig 2, also see below). The noise level between the bursts was the same or smaller than the background noise (amplitude of about 500) and hence could not yield any useful information. When sound below that level was filtered out there was no noticeable effect on the properties of the recording.

In order not to deal with large files containing long sequences of zeros the filtered signatures were com-

CROUTONS

a m = 0.11 a w = 0.75

t 1 0 5 10 15

TIME [aac]

Fig 1. Examples of typical force-time curves and normalised mechanical signatures of ‘crunchy’ and plasticised croutons.

Acoustic and mechanical signatures of crunchy cereal foods

0-

349

x 10‘ CHEESE

0 3 0 0.2 0.4 0.6 t 5000 d z

0

0 0.05 0.1 -5000

TIME [sec]

BALLS 5000 I 111 I

H I 0 0.05 0.1

-5000

5000

0

I ”

-5000; I’ I ’ rnl . J 0.01 0.02 0.03 0.04

TIME [sec]

Fig 2. Examples of typical acoustic signatures of a ‘crunchy’ cheese ball-a section of the signal as recorded (top left), an enlarged section (top right), an enlarged section after filtering the background noise (bottom left), and a section of the compressed signature

(bottom right).

pressed, that is, all the quiet, zero-valued, intervals between the bursts were removed from the file before it was analysed mathematically. Hence, all the following discussion will refer to the compressed signatures which still contained up to about 35000 data points. All the tests were performed with four replicates.

RESULTS AND DISCUSSION

Characterisation of the acoustic signature

Traditionally, acoustic signatures are transformed into a power spectrum to identify characteristic frequencies. Despite the files’ size, obtaining the power spectra of the compressed signatures using the FFT algorithm did not pose a special problem (Fig 3). However, because of the ‘spiky’ nature and the irregularity of the acoustic bursts, a clearly recognised characteristic pattern did not always emerge. In the case of the ‘crunchy’ (dry) samples, the peak frequency was usually on the order of 5-10 kHz for the cheese balls and 5-20 kHz for the croutons which is in general agreement with previously published reports (Lee et aJ 1988, 1990). The peak fre- quency though was an insensitive and not always clearly defined parameter for signature characterisation.

x IO‘CHEESE BALL, ow =0.11 15r ., .

FREQUENCY [kHz1

Fig 3. Example of a typical power spectrum of ‘crunchy’ cheese ball.

In contrast the mean magnitude of the power spectrum, which seems to capture the amount of detail in the com- pressed signature and also serves as a jaggedness measure, was a fairly consistent parameter which was subsequently used (see below). It should be mentioned that the signature compression must have affected the power spectrum. It primarily eliminated the very low frequencies that represented the intervals between the sound outbursts. These, however, were mainly produced by what can be called gross inhomogeneities in the specimens’ cellular structure and their random orienta- tion in relation to the metal plates between which the specimens were compressed. These frequencies are much lower than those that are associated with what is per- ceived as crunchiness and therefore could safely be dis- regarded.

Another consequence of the ‘spiky’ record was the inadequacy of the blanket algorithm previously used in characterising the jaggedness of mechanical signatures (Barrett et al 1992; Wollny and Peleg 1994) to deter- mine the apparent fractal dimension of the acoustic sig- natures. The same can be said of methods based on auto-correlation where, again, it was not the size of the data file that hindered interpretation but the nature of the record itself. It appears that if such methods are to be applied to acoustic signatures of crunchy foods recorded at CD quality, different or modified algo- rithms ought to be adopted.

Statistical parameters

A time series can be characterised in terms of its sta- tistical features. In our case these were as follows.

(a) The standard deviation of the compressed signa- ture and the mean magnitude of its Fourier

350 R Tesch et a1

power spectrum. Both are measures of the signa- ture’s amount of details or degree of complexity.

(b) The maximum and the mean amplitude which are both measures of the emitted sound’s relative intensity. Since the sound wave has alternating positive and negative values with a mean of about zero, the identification of both the peak and mean amplitudes were calculated from the absolute magnitudes.

Since determination of these parameters, as well as the calculation of the power spectrum, is a straightfor- ward mathematical operation, they could be determined for both the acoustic and mechanical signatures and then compared. But a word of caution is appropriate here. The ‘peak’ amplitude of the mechanical signature can be an artefact of the fitting procedure and need not always have a clear physical significance-in contrast to the mean amplitude which is a measure of the fluctua- tion intensity and hence of the degree of jaggedness. Although, due to the adequate fit of the fourth degree polynomial model, the peak amplitude could serve as a jaggedness measure it was excluded from the analysis of the mechanical signatures because of its inherent depen- dency on the model used for the normalisation.

Effect of water activity

The effects of water activity on the features of the acous- tic and mechanical signatures of cheese balls and crou- tons are demonstrated in Figs 4-7. Although the scatter is fairly large the plasticising effect of moisture is clearly and unambiguously evident irrespective of which param- eter is used. It is also obvious that at a, above about 0.65 the acoustic signal practically vanishes, and that this corresponds to the smoothing of the force- -

2ooo- 250001 , a 8 I

$ -I a 15001 4, 1 ~ z o o o ~ 2 15000 (, 1 z y 10000

a 5000

0 0

1000

1 500

WATER ACTIVITY WATER ACTIVITY

Fig 4. Effect of water activity on the acoustic signature parameters of cheese balls. (Vertical bars represent the mea-

surement range.)

0.0 - 0.20 I‘,

0.00 - 0.01 5

Y 0 a

WATER ACTIVITY

Fig 5. Effect of water activity on the mechanical signature parameters of cheese balls. (Vertical bars represent the mea-

surement range.)

deformation relationship and of the normalised mecha- nical signature (see below).

The relationship between the jaggedness parameters of the mechanical signature and the corresponding per- ceived

1500

YI

3 f1ooo

a a 2 500 s

0

degree of crunchiness determined sensorily was

z 20000

i 15000

2 y 10000 I

a I:

a 5000

0

z 2500

1500 n a

z U

500

a 6

c 5

0 F

x 4

5 3

2 2

a

u

WATER ACTIVITY WATER ACTIVITY

Fig 6. Effect of water activity on the acoustic signature parameters of croutons. (Vertical bars represent the measure-

ment range.)

Acoustic and mechanical signatures of crunchy cereal foods 351

6 0.7 . 2 0.6 . 5 0 .5 . 0

5 0.3 . 0 2 0.2 4

0.1

0.4 *

.

. 0.oL ' ' ' ' ' I

0.6 I

WATER ACTIVITY

Fig 7. Effect of water activity on the mechanical signature parameters of croutons. (Vertical bars represent the measure-

ment range.)

previously described in terms of a model based on Fermi's function (Peleg 1994; Wollny and Peleg 1994). Although this model can be used, as originally formu- lated or with modification, to describe the relationship between acoustic parameters and a,, the scatter of the data in at least some of the parameters was too large for meaningful determination of the model constants. What is clear, however, is that although all the relationships between the mechanical and acoustic parameters and a , had a region where the magnitude dropped drastically, signifying plasticisation or glass transition, the a , range at which the drop occurred was not always the same (see below).

Intracorrelations between the acoustic and mechanical parameters

The existence of correlations between the various jag- gedness parameters of the acoustic as well as the mecha- nical signatures is evident from the plots given in Figs 8-11. Generally, they could be described in terms of a power relationship whose coefficients are summarized in Table 1. The correlations were expected since at least some of the parameters, notably the standard deviation, the mean amplitude and the mean magnitude of the power spectrum, are different measures of the signa- tures' degree of jaggedness. Similar correlations were observed between the mean magnitude of the power spectrum of the mechanical signature and its apparent fractal dimension in a variety of other similar snacks (Rohde et al 1993a, b; Wollny and Peleg 1994). The nature of the correlation, however, varied considerably among the parameters but to a lesser extent between the two foods. Generally, the correlations between the 'complexity parameters' were relatively smooth, while those between the intensity and complexity parameters had a considerable scatter. Most probably, this is an indication that the sound intensity and its complexity are to some extent independent manifestations of the specimen failure and are regulated by different physical mechanisms.

Part of the scatter, however, must have also been due to a certain unavoidable degree of textural non-

CHEESE BALLS (mechanical) 0.3 1-1 0.3 7 1

5 0.2 > W 0

0

o z u)

a

0.0 0.0 0.0 0.005 0.010 0.015 0.00 0.05 0.10 0.15 0.20

MEAN MAGNITUDE [X 10'1 MEAN AMPLITUDE

Fig 9. Correlation between the standard deviation and the mean magnitude of the power spectrum of the mechanical sig-

natures of cheese balls.

CHEESE BALLS (acoustic)

3 15000 ; L 10000

E 5000

0 0

0

2000

1000

-J 0 1840

W 0 11 u)

1 0

F 2 W 0

0

4 0

a

3 F u)

MEAN AMPLITUDE MEAN MAQNITUDE [X 10'1 MEAN AMPLITUDE

Fig 8. Correlations between selected parameters of the acoustic signatures of cheese balls.

352 R Tesch et a1

CROUTONS (acoustic)

25000

$! 2000

# 1500 in 0. ;"" ; 20000 =a ' 15000 a '

Y

k! 5000 5 500 Of 500 lOdb 1 5dO

2500 I I

MEAN AMPLITUDE MEAN MAGNITUDE (1 1o'I MEAN AMPLITUDE

Fig 10. Correlations between selected parameters of the acoustic signatures of croutons.

uniformity among the specimens. Also, that the acoustic-record filtering and compression had some effect cannot be completely ruled out. But even if the effects of all these factors could be accounted for, one should still remember that the morphology of the signa- tures, mechanical or acoustic, has unique features that cannot be characterised fully by a single numerical index. Thus, although a trend-such as plasticisation- can be established with any of the mentioned param- eters, monitoring and characterisation of the more subtle changes require the use of several parameters

0.8

0.7 5 0.6

5 0.5

0.4

2 0.3 c1 z 0.2 a

0.1

0.0

n

CROUTONS (mechanlcal)

0.00 0.05 0.10 0.15

am 0.5

0.3

0.2 0.1

0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6

MEAN MAGNITUDE [X $0'1 WEAN AMPLITUOE

Fig 11. Correlation between the standard deviation and the mean magnitude of the power spectrum of the mechanical sig-

natures of croutons.

simultaneously. Among the latter, the standard devi- ation and the mean amplitude are the easiest to deter- mine since the compressed record can be used directly for their calculation without any mathematical trans- formation. Their relative magnitudes can also be inter- preted intuitively in both the mechanical and acoustic signatures as measures of the latter's relative complex- ity.

Relationships between the mechanical and acoustic signature parameters

The jaggedness of the mechanical signature, which is indicative of the complexity of the failure pattern, was expected to have a corresponding complexity in the acoustic signature. This is because at least some of the same failure events that are recorded in the mechanical signature are the direct cause of the sound emission. The general trend is evident in the relationships between the magnitudes of the corresponding jagged- ness parameters of the mechanical and acoustic signa- tures as shown in Figs 12 and 13. The scatter in the plots, however, is too broad to permit the establishment of any mathematical relationship between the two types of parameters. Probably this was primarily due to the

TABLE 1 Correlation between the statistical parameters of the acoustic and mechanical signatures"

Material Signature Parameter ( Y )

Cheese balls Acoustic Peak amplitude Std deviation Std deviation

Mechanical Std deviation Std deviation

Croutons Acoustic Peak amplitude Std deviation Std deviation

Mechanical Std deviation Std deviation

Parameter ( X )

Mean amplitude Mean magnitude Mean amplitude Mean magnitude Mean amplitude

Mean amplitude Mean magnitude Mean amplitude Mean magnitude Mean amplitude

a n r z Fig

0.13 1.2 0.075 1 *4 1.2

13 0.99 1.5 1.8 1.3

1.7 0.49 1.4 0.43 1 .o 1 .o 0.50 1.0 0.50 1 .o

0.798 8 0.997 8 0.985 8 0.985 8 0.998 9

0.790 10 1.000 10 0.979 10 0.945 11 0.995 11

a Fitted with the model y = ax"

Acoustic and mechanical signatures of crunchy cereal foods 353 - MEAN AMPLITUDE I X 10’1 MEAN MAGNITUDE

z fn

c fn

fn 12

0 0 1000 0 F 2

0 0 500

0 a 0 0 0 U

0.00 0.05 0.10 0.15 0.20 0.00 0.05 0.10 0.15 0.20 0.0 0.2 0.4 0.6 0.8 1 [X lo“] MECHANICAL SIGNATURE MECHANICAL SIGNATURE MECHANICAL SIGNATURE

Fig 12. Correlation between acoustic and mechanical parameters of cheese balls.

fact that the signatures were not determined from the same particles-the data points in Figs 12 and 13 are the means of several values determined at the same water activity level. It is also possible, however, that because the sampling rates of the mechanical and acoustic signatures are several orders of magnitude apart, there need not be a tight correlation for theoreti- cal reasons (see below).

Relation to glass transition

The relationship between the parameters of the acoustic and the mechanical signatures and of the level of water activity can be interpreted as evidence of a glass tran- sition (Slade and Levin 1991; Levin and Slade 1994) completed at an a , of about 0.65 (Figs 4-7) around which both materials became practically ‘silent’. A similar finding was reported by Attenburrow and Davis (1993) who showed that starch and gluten become ‘silent’ at moisture contents of about 120 and 160 g kg-’, respectively. As already mentioned, the a, of the transition’s onset is less clear, and would be judged differently according to the parameter used. This may not only be due to the data scatter. The observed general shape of the relationships between the complex- ity or intensity parameters and water activity can be interpreted in several, not mutually exclusive ways. One is that the changes in different mechanical properties during the transition need not occur in unison (Attenburrow and Davis 1993; Wollny and Peleg 1994). A well-known phenomenon is that the glass transition

temperature of polymeric materials, when monitored through dynamic mechanical testing, can be shifted con- siderably if the frequency is changed. This is a reflection of the ability, or inability, of the molecules to follow the changes in the stress field. Although the stress field geometry and propagation mode in a failing porous snack are far more complex, it is still theoretically pos- sible that at least some parts of the acoustic signatures are a record of events not fully revealed in the mechani- cal signature, or vice versa, because of the differences in frequency. Another interpretation has to do with the cellular structure of the materials examined. If the tran- sition has a differential effect on the cell wall and on the particle’s overall structure then the record of their com- bination must be affected by factors whose relative dominance alters as moisture is absorbed. In such a case, different mechanical or acoustic parameters are not expected and are not observed to rise and fall in unison. The issue of which mechanism is responsible cannot be resolved on the basis of this work. This is because neither the contributions of the different struc- tural levels to the signatures could be identified and iso- lated nor is the effect of frequency on the mechanical properties of the particulates tested fully known.

CONCLUSIONS

Although the acoustic signatures of the two crunchy foods tested (recorded at CD quality) have a very large amount of data, they can be processed by currently available microcomputers and software. The signatures

CROUTONS

STANDARD DEVIATION MEAN AMPLITUDE [X 10’1 MEAN MAGNITUDE

P l O O O

!iJ 500

12

0 fn

0 a a

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0 1 2 3 4 5 6 7 8[x 10”l MECHANICAL SIGNATURE MECHANICAL SIGNATURE MECHANICAL SIGNATURE

Fig 13. Correlation between acoustic and mechanical parameters of croutons.

354 R Tesch et a1

can be characterised by statistical parameters, notably the standard deviation and the mean magnitude of the sound amplitude. These can conveniently be used to monitor plasticisation as a result of moisture sorption (and probably by other additives). The power spectrum of the acoustic signature indicates peak emission a t about 10 kHz but because the shape of the peak and the power spectrum as a whole vary dramatically between samples, their interpretation is not as straight- forward as one would expect. Corresponding statistical parameters of the mechanical and acoustic signatures had only a very broad correlation. However, explana- tion of the relationship between mechanical and acous- tic signatures requires knowledge of exactly how failure occurs in cellular brittle food materials and how the sound waves are produced. These are areas where there was little progress in the past but which may see increased development in the future in light of the easy availability of hardware and software to process acous- tic signals recorded at high sampling rates.

ACKNOWLEDGEMENTS

The support of the work by the USDA-NRICGP (Grant 920954) is gratefully acknowledged. The authors also express their thanks to M r David Barrett for his very useful advice.

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