Browse by author
Lookup NU author(s): Dr Barbara Sturm
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 Luna Shrestha et al.The assessment of the quality of fresh-cut apple slices is important for processing, storage, market value, and consumption. Determination of polyphenol oxidase activity (PPO) in apples is critical for controlling the quality of the final product (i.e., dried apples and juices). Hyperspectral imaging (HSI) is a nondestructive, noncontact, and rapid food quality assessment technique. It has the potential to detect physical and chemical quality attributes of foods such as PPO of apple. The aim of this study was to investigate the suitability of HSI in the visible and near-infrared (VIS-NIR) range for indirect assessment of PPO activity of fresh-cut apple slices. Apple slices of two cultivars (cv. Golden Delicious and Elstar) were used to build a robust detection algorithm, which is independent of cultivars and applied treatments. Partial least squares (PLS) regression using the 7-fold cross-validation method and method comparison analysis (Bland-Altman plot, Passing-Bablok regression, and Deming regression) were performed. The 95% confidence interval (CI) bands for the Bland-Altman analysis between the methods were -4.19 and 13.11, and the mean difference was 3.7e-12. The Passing-Bablok regression had a slope of 0.8 and an intercept of 7.6. The slope of the Deming regression was 0.8 within the CI bands of 0.56 and 1.10. These results show acceptable performance and no significant deviation from linearity. Hence, the results demonstrated the feasibility of HSI as an indirect alternative to the standard chemical analysis of PPO enzyme activity.
Author(s): Shrestha L, Kulig B, Moscetti R, Massantini R, Pawelzik E, Hensel O, Sturm B
Publication type: Article
Publication status: Published
Journal: Journal of Spectroscopy
Year: 2020
Volume: 2020
Online publication date: 29/06/2020
Acceptance date: 09/06/2020
Date deposited: 17/08/2020
ISSN (print): 2314-4920
ISSN (electronic): 2314-4939
Publisher: Hindawi Limited
URL: https://doi.org/10.1155/2020/7012525
DOI: 10.1155/2020/7012525
Altmetrics provided by Altmetric
