Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose.

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 (R2 = 0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R2 = 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.

[1]  Giancarlo Perrone,et al.  Rapid prediction of ochratoxin A-producing strains of Penicillium on dry-cured meat by MOS-based electronic nose. , 2016, International journal of food microbiology.

[2]  Kozo Nakamura,et al.  High-performance liquid chromatographic determination of phenolic compounds in rice. , 2005, Journal of chromatography. A.

[3]  Anna M. McClung,et al.  Volatile profiles of aromatic and non-aromatic rice cultivars using SPME/GC–MS , 2011 .

[4]  Jun Wang,et al.  A novel framework for analyzing MOS E-nose data based on voting theory: Application to evaluate the internal quality of Chinese pecans , 2017 .

[5]  Joanna Kaczmarek,et al.  Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria , 2015, PloS one.

[6]  Giorgio Sberveglieri,et al.  Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emerging diagnostic tool , 2010 .

[7]  P. Guerre,et al.  Fungal contamination of rice from south Vietnam, mycotoxinogenesis of selected strains and residues in rice , 2003 .

[8]  P. Scott,et al.  Detection of Mycotoxins by Thin-Layer Chromatography: Application to Screening of Fungal Extracts , 1970 .

[9]  Bo Zhou,et al.  Discrimination of different types damage of rice plants by electronic nose , 2011 .

[10]  F. Yu,et al.  Detecting aflatoxin B1 in foods and feeds by using sensitive rapid enzyme-linked immunosorbent assay and gold nanoparticle immunochromatographic strip , 2013 .

[11]  Jun Wang,et al.  Internal quality detection of Chinese pecans (Carya cathayensis) during storage using electronic nose responses combined with physicochemical methods , 2016 .

[12]  Young-S. Kim,et al.  Investigation on the formations of volatile compounds, fatty acids, and γ-lactones in white and brown rice during fermentation. , 2018, Food chemistry.

[13]  H. Abdi.  Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[14]  J. Meis,et al.  Analysis of Growth Characteristics of Filamentous Fungi in Different Nutrient Media , 2001, Journal of Clinical Microbiology.

[15]  Dong Ling,et al.  Evaluation of volatile profile of Sichuan dongcai, a traditional salted vegetable, by SPME–GC–MS and E-nose , 2015 .

[16]  Peng Liu,et al.  Detection of Aspergillus spp. contamination levels in peanuts by near infrared spectroscopy and electronic nose , 2018, Food Control.

[17]  Jun Wang,et al.  Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice , 2015 .

[18]  N. Magan,et al.  Volatiles as an indicator of fungal activity and differentiation between species, and the potential use of electronic nose technology for early detection of grain spoilage. , 2000, Journal of stored products research.

[19]  W. Abraham,et al.  Volatile sesquiterpenes from fungi: what are they good for? , 2011, Phytochemistry Reviews.

[20]  Kang Tu,et al.  Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography–mass spectrometry , 2014 .

[21]  R. Ipsen,et al.  Sensory and rheological characterization of acidified milk drinks , 2008 .

[22]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[23]  Kang Tu,et al.  Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging , 2015, PloS one.

[24]  P. Mallikarjunan,et al.  Mid-infrared spectroscopy for discrimination and classification of Aspergillus spp. contamination in peanuts , 2015 .

[25]  Z. Kurtanjek,et al.  Near-Infrared Spectroscopic Analysis of Total Phenolic Content and Antioxidant Activity of Berry Fruits. , 2016, Food technology and biotechnology.

[26]  Yang Liu,et al.  Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model , 2016, Comput. Intell. Neurosci..

[27]  Abdolabbas Jafari,et al.  Early detection of contamination and defect in foodstuffs by electronic nose: A review , 2017 .

[28]  N. Sabatini,et al.  Volatile compounds in uninoculated and inoculated table olives with Lactobacillus plantarum (Olea europaea L., cv. Moresca and Kalamata) , 2008 .

[29]  T. A. Roberts,et al.  The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. , 1987, The Journal of applied bacteriology.

[30]  Kang Tu,et al.  Discrimination and growth tracking of fungi contamination in peaches using electronic nose. , 2018, Food chemistry.

[31]  Guang Li,et al.  A pattern recognition method for electronic noses based on an olfactory neural network , 2007 .

[32]  Jun Wang,et al.  The prediction of food additives in the fruit juice based on electronic nose with chemometrics. , 2017, Food chemistry.

[33]  Md. Zahurul Haque,et al.  Identification of Aflatoxigenic Fungi and Detection of Their Aflatoxin in Red Chilli (Capsicum annuum) Samples Using Direct Cultural Method and HPLC , 2018 .

[34]  J. Rojas,et al.  Identification and origin of host-associated volatiles attractive to Prorops nasuta, a parasitoid of the coffee berry borer , 2012, Arthropod-Plant Interactions.

[35]  George-John E. Nychas,et al.  Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis , 2013 .

[36]  M. Spiteller,et al.  Quantitative detection of Fusarium pathogens and their mycotoxins in South African maize , 2012 .

[37]  M. Klich.  Identification of common Aspergillus species , 2002 .