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Food Chem

Title:		Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose
Author(s):		Gu S; Wang J; Wang Y;
Address:		"Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China. Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China. Electronic address: jwang@zju.edu.cn. Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, PR China. Electronic address: wywzju@zju.edu.cn"
Journal Title:		Food Chem
Year:		2019
Volume:		20190416
Issue:
Page Number:		325 - 335
DOI:		10.1016/j.foodchem.2019.04.054
ISSN/ISBN:		1873-7072 (Electronic) 0308-8146 (Linking)
Abstract:		"Early detection of Aspergillus spp. contamination in rice was investigated by electronic nose (E-nose) in this study. Sterilized rice artificially inoculated with three Aspergillus strains were subjected to GC-MS and E-nose analyses. Principle Component Analysis (PCA), Partial Least Squares Regression (PLSR), Back-propagation neural network (BPNN), Support Vector Machine (SVM) and Learning Vector Quantization (LVQ) were employed for qualitative classification and quantitative regression. GC-MS analysis revealed a significant correlation between the volatile compounds and total amounts/species of fungi. While X-axis barycenters of PC1 scores were significantly correlated with fungal counts, logistic model could be employed to simulate the growth of individual fungus (R(2)?ª+=?ª+0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R(2)?ª+=?ª+0.886-0.917), respectively. The results demonstrated that E-nose combined with BPNN might offer the feasibility for early detection of Aspergillus spp. contamination in rice"
Keywords:		"Aspergillus/*growth & development/metabolism *Electronic Nose Gas Chromatography-Mass Spectrometry Least-Squares Analysis Neural Networks, Computer Oryza/chemistry/metabolism/*microbiology Principal Component Analysis Support Vector Machine Volatile Organ;"
Notes:		"MedlineGu, Shuang Wang, Jun Wang, Yongwei eng England 2019/05/06 Food Chem. 2019 Sep 15; 292:325-335. doi: 10.1016/j.foodchem.2019.04.054. Epub 2019 Apr 16"