Prediction of the nanomechanical properties of apple tissue during its ripening process from its firmness, color and microstructural parameters

Cárdenas-Pérez, Stefany; Méndez-Méndez, Juan Vicente; Chanona-Pérez, Jorge; Zdunek, Artur; Vera, Norma Güemes; Calderón‐Domínguez, Georgina; Rodríguez-González, Francisco

doi:10.1016/j.ifset.2016.11.004

Cited by 46 publications

(18 citation statements)

References 33 publications

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“…It is probable that E values of Ep surface showed the wide distribution and skewed to the left due to the anisotropy of the sample, the cells are constituted by many organelles, irregular topography, complex biopolymers, and fibers that provide a heterogeneous and anisotropic surface to cells. This exponential distribution has been observed in other vegetal samples such as apple (Cardenas-Pérez et al, 2017), Arabidopsis cells (Radotic et al, 2012), shoots and apical meristems of Arabidopsis (Milani et al, 2011) are skewed to the left. In the current study, the samples were kept in isotonic media and MS medium in order to avoid the hydration changes in the tomato root cells, thus the physiological conditions of growth were maintained during the nanoindentation of the samples.…”

Section: Resultssupporting

confidence: 55%

“…AFM is used to indent and mechanically characterize micro- and nano-sized structures due to the ability of the instrument to apply forces in the nanonewton range. Nanoindentation by AFM yields force curves with the linear portion of the approach curve being suitable for fitting by mathematical models to determine the sample stiffness (Notbohm et al, 2011; Zdunek & Kurenda, 2013; Cárdenas-Pérez, et al, 2016, 2017). During nanoindentation measurements, the hysteresis phenomenon between the approach and retract curves was observed.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have reported nanoindentation data during the ripening process of apple Pa tissue, with the Young's modulus ranging from 0.96 ± 0.42 to 0.11 ± 0.06 MPa from the initial to the final day of ripening. This means that the dynamic growth cells represent a mechanical change in their stiffness, and this change can be able to be measured by AFM (Cardenas-Pérez et al, 2017). The cell wall material of mango was also evaluated; the stiffness decreased from 1.69 ± 1.02 to 0.39 ± 0.16 MPa over 16 days.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Nanomechanical Properties of Tomato Root by Atomic Force Microscopy

et al. 2019

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Here, different tissue surfaces of tomato root were characterized employing atomic force microscopy on day 7 and day 21 of growth through Young's modulus and plasticity index. These parameters provide quantitative information regarding the mechanical behavior of the tomato root under fresh conditions in different locations of the cross-section of root [cell surface of the epidermis, parenchyma (Pa), and vascular bundles (Vb)]. The results show that the mechanical parameters depend on the indented region, tissue type, and growth time. Thereby, the stiffness increases in the cell surface of epidermal tissue with increasing growth time (from 9.19 ± 0.68 to 13.90 ± 1.68 MPa) and the cell surface of Pa tissue displays the opposite behavior (from 1.74 ± 0.49 to 0.48 ± 0.55); the stiffness of cell surfaces of Vb tissue changes from 10.60 ± 0.58 to 6.37 ± 0.53 MPa, all cases showed a statistical difference (p < 0.05). Viscoelastic behavior dominates the mechanical forces in the tomato root. The current study is a contribution to a better understanding of the cell mechanics behavior of different tomato root tissues during growth.

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Section: Resultssupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Nanomechanical Properties of Tomato Root by Atomic Force Microscopy

et al. 2019

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“…From its early days, AFM has also been used to investigate mechanical properties, either in the nanoindentation mode or in force spectroscopy mode. The nanoindentation mode may be used for the evaluation of the stiffness of cell walls from fruit (Pieczywek, Kozioł, Konopacka, Cybulska, & Zdunek, 2017; Zdunek, Kozioł, Cybulska, Lekka, & Pieczywek, 2016) or a piece of tissue (Braybrook, 2015; Cárdenas‐Pérez et al., 2017; Torode et al., 2018) even though pectins play a crucial role in the development of these properties, this approach does not provide information concerning pectin conformation. Unlike indentation, force spectroscopy involves measuring the force required to extend a molecule by a certain amount, it is directly related to the stiffness of the molecule (Figure 6).…”

Section: Evaluation Of Pectin Conformationmentioning

confidence: 99%

The primary, secondary, and structures of higher levels of pectin polysaccharides

Zdunek

Pieczywek

Cybulska

2020

Comp Rev Food Sci Food Safe

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182

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Pectin is a heteropolysaccharide abundant in the cell wall of plants and is obtained mainly from fruit (citrus and apple), thus its properties are particularly prone to changes occurring during ripening process. Properties of pectin depend on the string‐like structure (conformation, stiffness) of the molecules that determines their mutual interaction and with the surrounding environment. Therefore, in this review the primary, secondary, and structures of higher levels of pectin chains are discussed in relation to external factors including crosslinking mechanisms. The review shows that the primary structure of pectin is relatively well known, however, we still know little about the conformation and properties of the more realistic systems of higher orders involving side chains, functional groups, and complexes of pectin domains. In particular, there is lack of knowledge on the influence of postharvest changes and extraction method on the primary and secondary structure of pectin that would affect conformation in a given environment and assembly to higher structural levels. Exploring the above‐mentioned issues will allow to improve our understanding of pectin functionality and will help to tailor new functionalities for the food industry based on natural but often biologically variable source. The review also demonstrates that atomic force microscopy is a very convenient and adequate tool for the evaluation of pectin conformation since it allows for the relatively straightforward stretching of the pectin molecule in order to measure the force‐extension curve which is directly related to its stiffness or flexibility.

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“…for modelling process and successfully validated. Examples of this are studies where the MLR model for the cheese curd dry matter during curd treatment achieved high accuracy with a value R 2 = 91.85 [30], Young's modulus (YM) of apple tissue obtained R 2 > 0.95 [31], or eight aroma properties of Dianhong tea produced R 2 > 0.95 [19]. This tool was applied widely in other fields of science, such as environmental modelling [32] or waste water problems [33], proving its usefulness, simplicity and rapidity in preparation, which was also shown in the preparation of the models for overall quality changes during storage of spreadable processed gouda cheese.…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

Stangierski

Weiss

Kaczmarek

2019

Eur Food Res Technol

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The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R 2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R 2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

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Prediction of the nanomechanical properties of apple tissue during its ripening process from its firmness, color and microstructural parameters

Cited by 46 publications

References 33 publications

Evaluation of Nanomechanical Properties of Tomato Root by Atomic Force Microscopy

Evaluation of Nanomechanical Properties of Tomato Root by Atomic Force Microscopy

The primary, secondary, and structures of higher levels of pectin polysaccharides

Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

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