Analysis of microstructures and macrotextures for different apple cultivars based on parenchyma morphology

Abstract

Fuji, Golden Delicious, and Jonagold parenchyma were imaged by confocal laser scanning microscopy to be extracted morphology characteristics, which were used to analyze the relationship with macrotexture of apples tested by penetration and compression. Before analyzing the relationship, the significantly different morphology parameters were reduced in dimensions by principal component analysis and were proved to be availably used for distinguishing the different apple cultivars. For compression results, cell did not absolutely determine the hardness in different apple cultivars, and the pore should also be taken into consideration. With the same size in cell feret diameter, the bigger the pore feret diameter was, the softer the apple became. If no difference existed in pore feret diameter size, the cultivar became harder with a narrower distribution in cell feret diameter. The texture parameters were compared with the roundness parameters in the same or inverse changing trends to explore the relationship. High correlations were found between the texture parameters (energy required in whole penetration (Wt), fracture force (Fp), crispness) and pore solidity (R(2) > 0.924, P < 0.001). Compactness of parenchyma played an important role in fruit texture. This research could provide the comprehension about relationship between microstructure and macrotexture of apple cultivars and morphological values for modeling apple parenchyma, contributing to numerical simulation for constitutive relation of fruit.

A comparison of food crispness based on the cloud model

“…However, each dimension is independent from each other, and two‐dimensional cloud model still analyzes the crispness of the samples in respective dimensions. To explain the overall crispness characteristics of the samples, the comprehensive cloud model was used and the weights of the three time‐domain characteristics were calculated through the entropy method (Ye, ; Zhou et al, ). After calculating, the corresponding weights of the three characteristics are ξ 1 = 0.374148, ξ 2 = 0.320382, ξ 3 = 0.305470.…”

“…ImageJ 2× (National Institutes of Health, Bethesda, MD) was used to analyze the microstructures of the samples. The area, perimeter, and diameter of the microstructures were used to analyze the causes of different crispness (Alamar, Vanstreels, Oey, Molto, & Nicolai, ; Hou et al, ).…”

Nanovibrational stimulation of mesenchymal stem cells induces therapeutic reactive oxygen species and inflammation for 3D bone tissue engineering

“…We thus decided to generate a composite gel using a collagen sponge to provide rigidity while maintaining biocompatibility. Insoluble collagen was freeze dried to form a highly porous structure (pore size 227.74±72.93, measured using Feret's diameter 44 ), which was ~4 mm high and ~11 mm in diameter ( Figure 6A), and has an elastic modulus of 1.08±0.29 MPa (Zwick-Roell compression testing). The acellular scaffolds were held in place with a small weight while 2.5 mls of MSCcontaining neutralised collagen solution was poured over them.…”

\theta(1) time parallel algorithm for determining the diameters of multi-level image on the reconfigurable mesh computer machine

“…The diameter of objects in an image represents today an indispensable tool for the characterization and classification of forms. It is used as a measurement tool in a multitude of disciplines such as medical imagery [13], agriculture [1] [8], geology [7] [9] [15], nanotechnology [14], Composite Materials, Microstructures [10] [5], Microbiology and Medicine Biology [2] [18]. Most of the algorithms used in these fields are essentially based on determining the diameter of the smallest convex polygon that surrounds the component.…”

“…This polygon can be either a rectangle, or in the most general case the convex envelope of the component. The diameter of a convex polygon P is defined by the greatest distance which separates any pair of points (p, q) from this polygon: diam (P) = max {dist (p, q)} (1) Initially this approach was proposed by Shamos in 1978 [4] to find the diameter of a convex polygon. In 1983, Toussaint [16] demonstrated that the same algorithm serves to solve other geometric problems with a complexity of O (n).…”