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Author
Włodarska Katarzyna (Institute of Quality Science, Poznań University of Economics and Business, Poland), Pawlak-Lemańska Katarzyna (Institute of Quality Science, Poznań University of Economics and Business, Poland), Sikorska Ewa (Institute of Quality Science, Poznań University of Economics and Business, Poland)
Title
NIR Technology for Non-destructive Monitoring of aApple Quality During Storage
Source
LogForum, 2024, vol. 20, nr 1, s. 11-21, tab.,bibliogr. 29 poz.
Keyword
Łańcuch dostaw, Rozwój zrównoważony, Żywność, Jakość, Straty gospodarcze
Supply chain, Sustainable development, Food, Quality, Economic losses
Note
summ.
Abstract
. Background: Post-harvest losses are a major obstacle in achieving sustainable fresh produce chains. The causes of high waste are various and include inadequate logistics and quality control. Therefore, the research and development of rapid and accurate tools for fruit quality control is crucial for food quality assurance. Near-infrared (NIR) spectroscopy has become remarkably valuable in the agri-food sector. The aim of this study was to test NIR coupled with multivariate data analysis as a non-destructive tool to monitor apple quality during short-term storage. Methods: NIR was used to test apples (n=171) from four varieties and with varying levels of freshness. Each sample was measured immediately after harvest (at time T0) and after 14 days of storage at 10 ˚C (T14) in a non-destructive manner. Pattern recognition techniques including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify the apples. Variable importance in projection (VIP) was used to identify NIR spectra ranges that contributed significantly to the discrimination between fresh and stored apples. Results: The classification model distinguishing apples according to variety was characterized by high classification performance reflected in a misclassification error below 1%. The model for apple freshness discrimination also showed good classification performance with errors at the level of 7.9% and 5.8% for validation and prediction, respectively. The global model of eight classes including both apple variety and freshness was characterized by misclassification errors in the range of 1.2-6.3% for validation and 2.0-3.9% for prediction. The VIP method revealed that the spectral ranges contributing significantly to the freshness classification mainly corresponded to the absorption of water. Conclusions: NIR technology coupled with pattern recognition methods was found to be a promising tool for monitoring the overall loss of quality in fresh apples during short-term storage. The results may contribute to the development of a system supporting apple quality control and logistics during storage, e.g., while awaiting sale, transport or further processing. The findings are intended to guide various supply chain members and decision-makers so they can reduce post****harvest losses and improve the performance of the short fruit supply chain.(original abstract)
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ISSN
1895-2038
Language
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.000968
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