Physicochemical properties and rheological behavior of chrysanthemum powder made by superfine grinding and high pressure homogenization

Abstract

The effects of superfine grinding on the properties of chrysanthemum powder (CP) and high pressure homogenization (HPH) on the rheological properties of CP suspensions were investigated. Superfine grinding reduced particle size to a D 50 of 28.45 μm. Instantaneous flow function and wall friction tests demonstrated that superfine ground CP had poor flowability and was more cohesive than more coarsely ground samples. Superfine grinding increased thermal stability and reduced the crystallinity of CP. After HPH treatment, the apparent viscosity and consistency coefficient (k, from 0.027 ± 0.005 to 1.847 ± 0.472) of CP suspension was significantly improved (p <.05), indicating that HPH treatment increased the consistency and reduced the fluidity of CP suspensions. As the HPH pressure increased from 40 to 120 MPa, the storage modulus (G') and loss modulus (G") of the CP suspensions increased, which fitted well with the Power Law model (R 2 >.961). The CP suspension is a non-Newtonian fluid with shear thinning behavior. Flow and rheological characteristics are measured under different consolidation pressures and HPH pressures, respectively. These findings reveal the potential application of superfine grinding and high pressure homogenization in the development of CP.

Effect of Mechanical Grinding on the Physicochemical, Structural, and Functional Properties of Foxtail Millet (Setaria italica (L.) P. Beauv) Bran Powder

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Extracting barberry juice: Mathematical models describing loading stage, stress‐relaxation behavior, and momentary juice mass

“…The study of single‐sample (Li, Zhang, & Thomas, 2016), bulk‐sample (Akangbe & Herak, 2017), radial (Ihueze & Mgbemena, 2017), uniaxial (Kabutey et al, 2021), quasi‐static (Hasseldine et al, 2017), and dynamic (Azadbakht, Vahedi Torshizi, & Asghari, 2019) are the prevailing loading methods. Regression or mathematical (Sadrnia et al, 2008), artificial neural network (Vahedi Torshizi, Khojastehpour, Tabarsa, Ghorbanzadeh, & Akbarzadeh, 2020), finite element (Li, Cornish, et al, 2021; Li, Li, & Tchuenbou‐Magaia, 2021), and discrete element (Diels, Wang, Nicolai, Ramon, & Smeets, 2019) are common modeling methods.…”

“…The porosity coefficient is defined as the volume occupied by the air inside the sample divided by the sample's total volume. The porosity of the material can be calculated by the bulk density and the particle density of the sample by Equation (1) (Iaccheri, Cevoli, Dalla Rosa, & Fabbri, 2021; Li, Cornish, Zheng, & Wu, 2021): …”