Affiliation:
1. Department of Biosystems Engineering, Faculty of Agricultural Sciences University of Guilan Rasht Iran
Abstract
AbstractA data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e‐nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e‐nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion‐based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion‐based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e‐nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.
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1 articles.
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1. Machine learning-assisted electronic nose and gas sensors;Machine Learning and Artificial Intelligence in Chemical and Biological Sensing;2024