Elsevier

Journal of Food Engineering

Volume 229, July 2018, Pages 93-101
Journal of Food Engineering

Assessment of acoustic-mechanical measurements for crispness of wafer products

https://doi.org/10.1016/j.jfoodeng.2017.11.006Get rights and content

Highlights

  • Wafer products were examined by two fracturing tests along with sensory analysis.

  • Sound emissions were recorded simultaneously with tests.

  • Wafers’ crispness could be differentiated by the parameters of the cutting test.

  • Wafers’ creamy was related with mechanical parameters of the 3-point bending test.

  • Force peaks number and maximum sound pressure showed correlation on both tests.

Abstract

The objective of this work was to investigate instrumental tests regarding the capacity to differentiate crispy wafer products of different quality and regarding to correlations between instrumental parameters and sensory descriptors. Therefore two fracturing methods, a 3-point bending and a cutting test with simultaneously recorded sound emissions and a descriptive sensory analysis were carried out with nine different brands of wafers representing different qualities.

The results showed that both instrumental methods are capable to differentiate products of different quality, but in different ways. Only the maximum sound pressure (r = 0.89) and the number of force peaks (r = 0.83) of both tests correlate. The sensory descriptor “crispness” was mainly correlated with the area under sound-displacement curve (r = 0.76) and mean sound value (r = 0.59) of the cutting test, and weakly related to the number of force peaks (r = 0.42), the maximum sound pressure (r = 0.50) and the area under sound-displacement curve (r = 0.42) of the 3-point bending test.

Introduction

The food attribute “crispness” related to sound emission is commonly referred as quality description of food during biting or chewing (Duizer, 2001, Duizer, 2004, Mallikarjunan, 2004, Vickers, 1983) meaning freshness and wholesomeness and one of the important texture characteristics appreciated by customers (Piazza et al., 2007, Saeleaw and Schleining, 2011, Tunick et al., 2013). Crispy foods are generally appealing and enjoyable (Szczesniak and Kahn, 1971), due to the fact that the sounds when biting or eating have positive affect on the customer perception (Spence and Shankar, 2010).

Wafer is also considered as crispy food and crispness of it is primary textural attribute perceived at the first bite during bending (Martinez-Navarrete et al., 2004). Manufacturing process, ingredients compositions and keeping conditions can usually affect the crispness, crunchiness of wafers furthermore water intake causes the soggy or leathery property (Stephen et al., 1994) which leads the poor quality cause the low consumption of final product. To understand crispness of wafer different tests such as sensory, mechanical and acoustical can be applied. Commonly, most known method to determine crispness of wafer is sensory test which has some difficulties such as time consuming, not convenient for routine tests, requiring more statistical works and most of all providing participants who have good knowledge in texture attributes (Gregersen et al., 2015, Zdunek et al., 2011). To overcome these difficulties, acoustic methods were tired to assess wafer samples by using an Acoustic Envelope Detector (AED) attached to the Texture Analyser (TA) and force-displacement and acoustic signals were simultaneously recorded.

Earlier researches on determination of crispness from crispy foods started by adapting sensory tests techniques and later acoustic detection devices and mechanical methods were developed (Christensen and Vickers, 1981, Drake, 1963, Drake, 1965, Edmister and Vickers, 1985, Hi et al., 1988, Kapur, 1971, Mohamed et al., 1982, Seymour and Ann, 1988, Szczesniak, 1963, Vickers and Bourne, 1976, Vickers, 1984, Vickers, 1985). Those methods, mainly 3-point bending, cutting, penetration, compression methods, allowed scientist to predict crispness of snack foods (Duizer, 2001) and showed good correlations between acoustic-mechanical and sensory parameters. Mohamed et al. (1982) indicated that performing both acoustic and mechanical measurements together can allow predicting better crispness than using only this test. Later, Chaunier et al., 2005, Chen et al., 2005 and Varela et al. (2006) approved also this by testing different kinds of solid crispy foods.

From these works regarding acoustic parameters, Chen et al. (2005) demonstrated that maximum sound pressure (Smax) and acoustic events could be used to range biscuit crispness, which had highest Smax and acoustic event values was also highest in crispness. According to this assumption, they could differentiate six biscuits samples with the highest correlation between acoustic and sensory measurements. Varela et al. (2008) indicated that number of sound peaks (NSP) was better to discriminate precooked chicken nuggets and directly related to crispness. Primo-Martín et al., 2008, Primo-Martin et al., 2009 worked on bread crust and explained that high NSP and force events determined the crust crispness better. In another work, Salvador et al. (2009) stated that sensory crispness on potato chips was positively related with number of force peaks (NFP), Smax and NSP. Saeleaw et al. (2012) showed also mean sound pressure (MS), NSP and NFP could be used to determine the crispness of rye-based extrudates and cassava crackers produced in different process conditions such as extrusion conditions and frying parameters respectively. Recent years, NSP and maximum force peak (Fmax) were also used to characterize crispness of extruded cereals (Chanvrier et al., 2014), biscuits (Blonska et al., 2014) and apples (Cybulska et al., 2012) whereas Smax, NSP and MS were used for hazelnut kernels (Giacosa et al., 2016) and apple as well (Piazza and Giovenzana, 2015). Jakubczyk et al. (2017) studied also on co-extruded snacks by using MS, Smax and NSP parameters and explained that milk filling extrudates were crispier than jelly filling ones since they had highest values of these acoustic parameters. Works on these samples demonstrated that acoustic parameters were well correlated within sensory ones and gave them opportunity to use fast and reliable method to examine textural properties of crispy-crunchy foods.

Wafer is also a crispy product and until now there were few studies (Juodeikiene and Basinskiene, 2004, Martinez-Navarrete et al., 2004, Mohammed et al., 2014) on the definition of wafer texture by using acoustic tests. The aim of this work was to evaluate acoustical and mechanical parameters for wafer quality applying different test methods and then to relate those parameters with sensory descriptors.

Section snippets

Samples

Wafer samples of nine different brands (Bella, Manner, Sweet Gold, Napoli, Biscoteria, Jadro, Fin Carre Normal, Fin Carre Strawberry and Fin Carre Lemon) were analysed. All were of 16 mm thickness, 51 mm length and 18 mm width with 9 layers. The selection was based on the aim to have samples with different qualities, no matter if the different qualities were based on different recipes of the filling or different moisture content. Samples were kept in its original package at 24 °C. For each

Evaluation of wafers acoustic amplitude-time and force-time curves

Fig. 1-A and -B show typical force-time and acoustic amplitude-time curves of the 3-point bending (A) and cutting test (B). In general it can be seen, that the acoustic peaks do not necessarily correlate with the force peaks. From this it can be concluded, that the force and acoustic signal provide different information.

For the 3-point bending test the maximum sound emission Smax is usually obtained within the first 5 s of the test, when the first layer of wafer sample is cracked. During this

Conclusion

The acoustic-mechanical results of both instrumental tests were quite different, however there were some correlations between both tests regarding the parameters NFP and Smax. The higher these values, the crispier a product is.

Among the instrumental parameters, Fmax-LF (r = 0.939), AF-NSP (r = 0.914), AS-DSmax (r = 0.936), AS-NSP (r = 0.945), AS-LS (r = 0.948), NSP-LS (r = 0.982) of the 3-point bending and Fmax-AF (r = 0.934), NSP-LS (r = 0.980) of cutting test showed good correlations.

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