Classification of puffed snacks freshness based on crispiness-related mechanical and acoustical properties

Journal of Food Engineering

Volumn 226, Issue , 2018, Pages 53-64

  Sanahuja, Solange ^a   Fédou, Manuel ^b   Briesen, Heiko ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b NONE   (Switzerland)

Author keywords

Crushing sounds; Food texture; Machine learning; Mechanical properties; Multisensory integration; Sensory crispiness crispness

Indexed keywords

ARTIFICIAL INTELLIGENCE; CRUSHING; FREQUENCY BANDS; LEARNING SYSTEMS; MECHANICAL PROPERTIES; NEURAL NETWORKS; QUALITY CONTROL; SENSORY ANALYSIS;

ACOUSTICAL PROPERTIES; ARTIFICIAL NEURAL NETWORK ALGORITHM; CLASSIFICATION ACCURACY; FOOD TEXTURE; MACHINE LEARNING MODELS; MULTISENSORY INTEGRATION; RESEARCH AND DEVELOPMENT; SENSORY CRISPINESS/CRISPNESS;

LEARNING ALGORITHMS;

EID: 85043302741     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2017.12.013     Document Type: Article

References (55)

1. Auvray, M., Spence, C., The multisensory perception of flavor. Conscious. Cognit. 17:3 (2008), 1016–1031.
2. Banerjee, R., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N., A review on combined odor and taste sensor systems. J. Food Eng. 190 (2016), 10–21.
3. Beale, M.H., Hagan, M.T., Demuth, H.B., Multilayer neural networks and backpropagation training. I. T. M. The MathWorks, Inc, (eds.) Neural Network Toolbox™ User's Guide, 10.0, 2017, The MathWorks, Inc, Natick, 3.1–3.30.
4. Beck, D.A.C., Carothers, J.M., Subramanian, V., Pfaendtner, J., Data science: accelerating innovation and discovery in chemical engineering. AIChE J. 62:5 (2016), 1402–1416.
5. Bourne, M.C., Correlation between physical measurements and sensory assessments of texture and viscosity. Bourne, M.C., (eds.) Food Texture and Viscosity: Concept and Measurement, 2002, Vol. Food Science and Technology, 294–323.
6. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G., Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43 (2003), 1882–1889.
7. Chen, J., Engelen, L., Food Oral Processing: Fundamentals of Eating and Sensory Perception. 2012, Wiley-Blackwell.
8. Chen, J., Karlsson, C., Povey, M., Acoustic envelope detector for crispness assessment of biscuits. J. Texture Stud. 36:2 (2005), 139–156.
9. Christensen, C.M., Vickers, Z.M., Relationships of chewing sounds to judgments of food crispness. J. Food Sci. 46:2 (1981), 574–578.
10. Crisinel, A.-S., Cosser, S., King, S., Jones, R., Petrie, J., Spence, C., A bittersweet symphony: systematically modulating the taste of food by changing the sonic properties of the soundtrack playing in the background. Food Qual. Prefer. 24:1 (2012), 201–204.
11. De Belie, N., Sivertsvik, M., De Baerdemaeker, J., Differences in chewing sounds of dry-crisp snacks by multivariate data analysis. J. Sound Vib. 266 (2003), 625–643.
12. Dijksterhuis, G., Luyten, H., de Wijk, R., Mojet, J., A new sensory vocabulary for crisp and crunchy dry model foods. Food Qual. Prefer. 18:1 (2007), 37–50.
13. DIN, Sensory Testing Methods - Intensity Test, vol. 10966, 1997, Beuth, Berlin, 6.
14. DIN, Sensory Analysis - Consumer Sensory Evaluation, vol. 10974, 2008, Beuth, Berlin, 38.
15. Domingos, P., A few useful things to know about machine learning. Commun. ACM 55:10 (2012), 78–87.
16. Drake, B.K., Food crushing sounds. An introductory study. J. Food Sci. 28:2 (1963), 233–241.
17. Duizer, L., A review of acoustic research for studying the sensory perception of crisp, crunchy and crackly textures. Trends Food Sci. Technol. 12:1 (2001), 17–24.
18. Duizer, L., Campanella, O.H., Barnes, G.R.G., Sensory, instrumental and acoustic characteristics of extruded snack food products. J. Texture Stud. 29:4 (1998), 397–411.
19. Elder, R.S., Mohr, G.S., The crunch effect: food sound salience as a consumption monitoring cue. Food Qual. Prefer. 51 (2016), 39–46.
20. Huang, N.E., Fast EMD/EEMD Code [package]. 2014 (Retrieved from RCADA website).
21. ISO, Preferred Numbers Series of Preferred Numbers. 1973.
22. ISO, D., Sensory Analysis - Methodology - Ranking, vol. 8587, 2006, Beuth, Berlin, 27.
23. ISO, D.E., Sensory Analysis - Methodology - Paired Comparison Test. vol. 5495, 2007, Beuth, Berlin.
24. ISO, D.E., Sensory Analysis - General Guidelines for the Selection, Training and Monitoring of Selected Assessors and Expert Sensory Assessors. vol. 8586), 2014, Beuth, Berlin.
25. Katz, E.E., Labuza, T.P., Effect of water activity on the sensory crispness and mechanical deformation of snack food products. J. Food Sci. 46 (1981), 403–409.
26. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521:7553 (2015), 436–444.
27. Liu, X., Tan, J., Acoustic wave analysis for food crispness evaluation. J. Texture Stud. 30 (1999), 397–408.
28. Liu, Y., Huang, B.-Z., Sun, Y.-H., Chen, F.-Y., Yang, L., Mao, Q., Liu, J.S., Zheng, M.-Z., Relationship of carrot sensory crispness with acoustic signal characteristics. Int. J. Food Sci. Technol. 50 (2015), 1574–1582.
29. Luckett, C.R., Meullenet, J.-F., Seo, H.-S., Crispness level of potato chips affects temporal dynamics of flavor perception and mastication patterns in adults of different age groups. Food Qual. Prefer. 51 (2016), 8–19.
30. Luyten, H., Plijter, J.J., Van Vliet, T., Crispy/crunchy crusts of cellular solid foods: a literature review with discussion. J. Texture Stud. 35 (2004), 445–492.
31. Luyten, H., Van Vliet, T., Acoustic emission, fracture behavior and morphology of dry crispy foods: a discussion article. J. Texture Stud. 37:3 (2006), 221–240.
32. MathWorks, Nonparametric supervised learning. The MathWorks, Inc, I.T.M., (eds.) Statistics and Machine Learning Toolbox™ User's Guide, 11.1, 2017, The MathWorks, Inc, Natick, 18.11–18.244.
33. Mohamed, A.A.A., Jowitt, R., Brennan, J.G., Instrumental and sensory evaluation of crispness: I - in friable foods. J. Food Eng. 1:1 (1982), 55–75.
34. Peleg, M., On fundamental issues in texture evaluation and texturization—a view. Food Hydrocolloids 20 (2006), 405–414.
35. Pellegrino, R., Luckett, C.R., Shinn, S.E., Mayfield, S., Gude, K., Rhea, A., Seo, H.-S., Effects of background sound on consumers' sensory discriminatory ability among foods. Food Qual. Prefer. 43 (2015), 71–78.
36. Saeleaw, M., Schleining, G., A review: crispness in dry foods and quality measurements based on acoustic–mechanical destructive techniques. J. Food Eng. 105:3 (2011), 387–399.
37. Salvador, A., Varela, P., Sanz, T., Fiszman, S.M., Understanding potato chips crispy texture by simultaneous fracture and acoustic measurements, and sensory analysis. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 42:3 (2009), 763–767.
38. Sanahuja, S., Briesen, H., Dynamic spectral analysis of jagged mechanical signatures of a brittle puffed snack. J. Texture Stud. 46:3 (2015), 171–186.
39. Seymour, S.K., Hamann, D.D., Crispness and crunchiness of selected low moisture foods. J. Texture Stud. 19:1 (1988), 79–95.
40. Srisawas, W., Jindal, V.K., Acoustic testing of snack food crispiness using neural networks. J. Texture Stud. 34 (2003), 401–420.
41. Szczesniak, A.S., Classification of textural characteristics. J. Food Sci. 28:4 (1963), 385–389.
42. Taniwaki, M., Kohyama, K., Mechanical and acoustic evaluation of potato chip crispness using a versatile texture analyzer. J. Food Eng. 112:4 (2012), 268–273.
43. Taniwaki, M., Sakurai, N., Kato, H., Texture measurement of potato chips using a novel analysis technique for acoustic vibration measurements. Food Res. Int. 43:3 (2010), 814–818.
44. Tunick, M.H., Onwulata, C.I., Thomas, A.E., Phillips, J.G., Mukhopadhyay, S., Sheen, S., Liu, C.K., Latona, N., Pimentel, M.R., Cooke, P.H., Critical evaluation of crispy and crunchy textures: a review. Int. J. Food Prop. 16 (2013), 949–963.
45. Van Vliet, T., Primo-Martín, C., Interplay between product characterisitics, oral physiology and texture perception of cellular brittle foods. J. Texture Stud. 42 (2011), 82–94.
46. Varela, P., Chen, J., Fiszman, S., Povey, M.J.W., Crispness assessment of roasted almonds by an integrated approach to texture description: texture, acoustics, sensory and structure. J. Chemometr. 20:6–7 (2006), 311–320.
47. Vickers, Z.M., Crackliness: relationships of auditory judgments to tactile judgments and instrumental acoustical measurements. J. Texture Stud. 15:1 (1984), 49–58.
48. Vickers, Z.M., Crispiness and crunchiness - a difference in pitch?. J. Texture Stud. 15:2 (1984), 157–163.
49. Vickers, Z.M., Sensory, acoustical, and force-deformation measurements of potato chip crispness. J. Food Sci. 52:1 (1987), 138–140.
50. Vickers, Z.M., Instrumental measures of crispness and their correlation with sensory assessment. J. Texture Stud. 19 (1988), 1–14.
51. Vickers, Z.M., Bourne, M.C., Crispness in foods - a review. J. Food Sci. 41 (1976), 1153–1157.
52. Vickers, Z.M., Bourne, M.C., A psychoacoustical theory of crispness. J. Food Sci. 41 (1976), 1158–1164.
53. Woods, A.T., Poliakoff, E., Lloyd, D.M., Kuenzel, J., Hodson, R., Gonda, H., Batchelor, J., Dijksterhuis, G.B., Thomas, A., Effect of background noise on food perception. Food Qual. Prefer. 22:1 (2011), 42–47.
54. Wu, Z., Huang, N.E., Ensemble empiricla mode decomposition: a noise-assited data analysis method. Adv. Adapt. Data Anal. 1:1 (2009), 1–41.
55. Zampini, M., Spence, C., The role of auditory cues in modulating the perceived crispness and staleness of potato chips. J. Sensory Stud. 19:5 (2004), 347–363.