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Sensors 2018, 18(6), 1922; https://doi.org/10.3390/s18061922

Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

1
Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan P.O. Box 84156-83111, Iran
2
Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia, The Barcelona Institute for Science and Technology, Baldiri Reixac 4-8, 08028 Barcelona, Spain
3
Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Marti i Franqués 1, 08028 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Received: 16 May 2018 / Revised: 6 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
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Abstract

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods. View Full-Text
Keywords: bitter orange essential oil; headspace gas chromatography–mass spectrometry; artificial neural network; foodomics; chemometrics; feature selection bitter orange essential oil; headspace gas chromatography–mass spectrometry; artificial neural network; foodomics; chemometrics; feature selection
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Taghadomi-Saberi, S.; Mas Garcia, S.; Allah Masoumi, A.; Sadeghi, M.; Marco, S. Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning. Sensors 2018, 18, 1922.

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