A comparison of food crispness based on the cloud model. Issue 1 (30th August 2017)

• Record Type: Journal Article
• Authors: Wang, Minghui; Sun, Yonghai; Hou, Jumin; Wang, Xia; Bai, Xue; Wu, Chunhui; Yu, Libo; Yang, Jie
• Is Part Of: Journal of texture studies. Volume 49:Issue 1(2018)
• Page Start: 102
• Page End: 112
• Publication Date: 2017-08-30
• Subjects: acoustic signal -- cloud model -- food crispness -- microstructures -- verification experiment; Food texture -- Periodicals; Food -- Composition -- Periodicals; 664.02
• DOI: 10.1111/jtxs.12295
• Languages: English
• ISSNs: 0022-4901

Abstract

Abstract: The cloud model is a typical model which transforms the qualitative concept into the quantitative description. The cloud model has been used less extensively in texture studies before. The purpose of this study was to apply the cloud model in food crispness comparison. The acoustic signals of carrots, white radishes, potatoes, Fuji apples, and crystal pears were recorded during compression. And three time‐domain signal characteristics were extracted, including sound intensity, maximum short‐time frame energy, and waveform index. The three signal characteristics and the cloud model were used to compare the crispness of the samples mentioned above. The crispness based on the Ex value of the cloud model, in a descending order, was carrot > potato > white radish > Fuji apple > crystal pear. To verify the results of the acoustic signals, mechanical measurement and sensory evaluation were conducted. The results of the two verification experiments confirmed the feasibility of the cloud model. The microstructures of the five samples were also analyzed. The microstructure parameters were negatively related with crispness ( p < .01). Practical applications: The cloud model method can be used for crispness comparison of different kinds of foods. The method is more accurate than the traditional methods such as mechanical measurement and sensory evaluation. The cloud model method can also be applied to other texture studies extensively.