Performance evaluation of aloe vera-based edible coating on cucumber through kinetics study and machine vision

Sarker, Ayesha

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https://hdl.handle.net/2142/110677 Copy

Description

Title

Performance evaluation of aloe vera-based edible coating on cucumber through kinetics study and machine vision

Author(s)

Sarker, Ayesha

Issue Date

2021-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Grift, Tony

Doctoral Committee Chair(s)

Grift, Tony

Committee Member(s)

• Kalita, Prasanta
• Cadwallader, Keith
• Williams, Martin
• Deltsidis, Angelos

Department of Study

Engineering Administration

Discipline

Agricultural & Biological Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Aloe vera
• Kinetics
• Cucumber
• Shelf-life
• Machine vision

Abstract

"Cucumber is a popular vegetable with a relatively short shelf life. To extend its shelf-life, edible coatings such as Aloe vera individually or in combination with other ingredients comprise a promising treatment for retaining the postharvest quality of fruits and vegetables. However, the performance of treatment in reducing the degradation rate of a quality parameter should be determined quantitatively; therefore, a kinetics study that involves the study of chemical reaction rates and mechanisms is essential. In addition, measuring a quality parameter, such as color, often follows a destructive technique, which is time-consuming and costly. The objective of this study was to identify the optimum concentration of Aloe vera-based coating and to study its effect on the shelf-life and degradation kinetics of fresh and minimally processed cucumber during storage, as well as to assess the suitability of a machine vision system as a non-destructive tool for continuous monitoring of cucumber for its quality changes. The first part of the study assessed the efficacy and performance of Aloe vera (AV) gel-glycerol edible coating to extend the shelf life of minimally processed cucumber. To assess the optimal coating formulation, four concentrations (0, 30, 50, and 100% [v/v]) were tested on cucumber slices stored at 4 ± 1C and at 20 ± 1C. Slices coated with 50% and 100% AV had reduced physicochemical changes and suppressed microbial loads while maintaining overall visual quality (visual appearance) during storage. During the kinetics study, zero and first-order kinetic models fitted well to the experimental data of L*, a*, and b* color parameters with sufficiently high coefficients of determination (R2) values. In addition, the kinetic rate constants of color changes were found to be temperature dependent as described by the Arrhenius equation that is used to reveal the relation between chemical reactions and temperature. Overall, throughout the kinetics study, lower color degradation rates were observed in Aloe-vera coated slices compared to control slices. In the second part of the study, fresh cucumbers were coated with Aloe vera (AV) gel and Carboxymethyl Cellulose (CMC). Control, 20%, 30%, and 50% (V/V) AV + 1% (W/V) CMC coating formulations were prepared and applied on cucumbers stored at 15°C and 23°C. Quality parameters such as weight, firmness, color, pH, total soluble solids (TSS), titratable acidity (TA), and mold count were monitored at regular intervals until day 20. The degradation kinetics of stored cucumbers, namely weight loss, firmness and color changes, and the effect of coating treatment on the degradation kinetics were studied by fitting kinetic models to the specified experimental data. Both zero and first-order kinetic models fitted well with weight loss, firmness, and color change data with a reasonably high coefficient of determination (R2) values. The Arrhenius equation was used to study the effect of temperature on the kinetic parameters, and . Temperature increment, in general, resulted in the acceleration of the degradation process. From the kinetics study, 30% AV + 1% CMC coating had the most reduced degradation rate among the treatments studied. Overall, based on the interactive effects of coating treatment, temperature, and storage time, 30% AV + 1% CMC coated cucumbers retained the most postharvest quality parameters. In the third part of the study, a machine vision system was used to monitor cucumbers' external quality, such as color changes or the presence of any damage during storage. Images of cucumber were acquired in a ""soft box,"" which provided a highly diffuse lighting scene, ideal for observing visual changes in the skin of cucumber. A cucumber center pixel accumulation (CCPA) algorithm was used to select center pixels from grayscale images. All the center pixels from 400 images (each obtained by 0.9° rotation) were accumulated to obtain an image of 1280*400-pixel size, which corresponds to a whole cucumber surface. This original RGB image was used to monitor the external changes of stored cucumbers by extracting the color and texture (local binary pattern) features. Damage progression plots (DPP) were made from accumulated grayscale images. For the absolute differential damage progression (ADDP) plot, the blue (B) channel in RGB color space was found to be the best in terms of interpreting the damage progression from the plot and the corresponding 3-D histograms. In addition, the k-means clustering technique was applied for defect segmentation from the RGB image. In this experiment, images were taken as input RGB images which were transformed into L*,a*,b* and HSV spaces. The color space that was the most sensitive overall, i.e., could capture most of the information about the day-to-day color changes of cucumber, was identified through a principal component analysis (PCA). According to the PCA, all individual components in the RGB color space were found to be suitable to obtain information about the external changes of cucumber. Overall, the machine vision approach was found suited as a non-destructive technique for monitoring the external quality of cucumber during storage.  "

Graduation Semester

2021-05

Type of Resource

Thesis

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http://hdl.handle.net/2142/110677

Copyright and License Information

Copyright 2021 Ayesha Sarker

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