Visualization of volatomic profiles for early detection of fungal infection on storage Jasmine brown rice using electronic nose coupled with chemometrics

Abstract Early detection of fungal contamination can prevent fungal infected rice to enter food chains. This study aimed to use electronic nose coupled with chemometrics as a rapid and nondestructive method for detection of fungal contamination on brown rice grain. Jasmine brown rice was artificially infected with Aspergillus and stored at 30 °C and 85% RH. Volatile markers of fungal infected brown rice were identified using solid phase microextraction/gas chromatography–mass spectrometry (SPME/GC–MS). The volatomic profiles of fungal infected rice were analyzed using electronic nose and explored using principal component analysis (PCA). Linear discriminant analysis (LDA) and support vector machine (SVM) were then employed to classify samples with different levels of fungal contamination as a factor of storage time. Partial least squares (PLS) regression model was developed for prediction of the fungal growth on brown rice. The electronic nose coupled with PLS regression could accurately predict the fungal growth and gave coefficient of determination, R2 = 0.969, and root mean square error, RMSE = 0.31 Log CFU/g. The results suggested that the electronic nose can be used as a rapid and nondestructive tool for early detection of fungal infection on rice grain prior to visible growth.

[1]  Roberto Paolesse,et al.  Detection of fungal contamination of cereal grain samples by an electronic nose , 2006 .

[2]  H. Jeleń.  Use of solid phase microextraction (SPME) for profiling fungal volatile metabolites , 2003, Letters in applied microbiology.

[3]  Y. Makino,et al.  Simultaneous assessment of various quality attributes and shelf life of packaged bratwurst using hyperspectral imaging. , 2018, Meat science.

[4]  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.

[5]  K. Burton,et al.  Eight-carbon volatiles in mushrooms and fungi: properties, analysis, and biosynthesis , 2006 .

[6]  A. Alavudeen Basha,et al.  Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform , 2019, Measurement.

[7]  Saeid Minaei,et al.  Real-time aroma monitoring of mint (Mentha spicata L.) leaves during the drying process using electronic nose system , 2018, Measurement.

[8]  H. Tan,et al.  Estimation of fungal growth using the ergosterol assay: a rapid tool in assessing the microbiological status of grains and feeds , 2007, Letters in applied microbiology.

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

[10]  Liang Tao,et al.  Visual comparison based on linear regression model and linear discriminant analysis , 2018, J. Vis. Commun. Image Represent..

[11]  L. Arce,et al.  Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM. , 2019, Food chemistry.

[12]  Johan Schnürer,et al.  Off-odorous compounds produced by molds on oatmeal agar : identification and relation to other growth characteristics , 1993 .

[13]  M. Cousin.  Chitin as a Measure of Mold Contamination of Agricultural Commodities and Foods †. , 1996, Journal of food protection.

[14]  Qiang Zhang,et al.  Comparison of Different Classification Methods for Analyzing Electronic Nose Data to Characterize Sesame Oils and Blends , 2015, Sensors.

[15]  Kazushi Yoshida,et al.  Volatile compounds produced by the fungus Aspergillus oryzae in rice Koji and their changes during cultivation , 1990 .

[16]  Limin Wang,et al.  Distribution and variation of fungi and major mycotoxins in pre- and post-nature drying maize in North China Plain , 2017 .

[17]  Jesús Simal-Gándara,et al.  Use of spectroscopic methods in combination with linear discriminant analysis for authentication of food products , 2018, Food Control.

[18]  Ubonrat Siripatrawan,et al.  Hyperspectral imaging for rapid evaluation and visualization of quality deterioration index of vacuum packaged dry-cured sausages , 2018 .

[19]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[20]  Y. Makino,et al.  Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging. , 2015, International journal of food microbiology.

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

[22]  Yunhong Liu,et al.  Detection Potential of Multi-Features Representation of E-Nose Data in Classification of Moldy Maize Samples , 2017, Food and Bioprocess Technology.

[23]  K. J. Babu,et al.  Freshness Assessment of Broccoli using Electronic Nose , 2019, Measurement.

[24]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[25]  Luc Pronzato,et al.  Simplicial variances, potentials and Mahalanobis distances , 2018, J. Multivar. Anal..

[26]  Kriengkri Timsorn,et al.  Identification of adulteration in uncooked Jasmine rice by a portable low-cost artificial olfactory system , 2017 .

[27]  T. Abee,et al.  1-Octen-3-ol inhibits conidia germination of Penicillium paneum despite of mild effects on membrane permeability, respiration, intracellular pH, and changes the protein composition. , 2005, FEMS microbiology ecology.

[28]  F. Fleurat-lessard,et al.  Integrated management of the risks of stored grain spoilage by seedborne fungi and contamination by storage mould mycotoxins – An update , 2017 .

[29]  Ubonrat Siripatrawan,et al.  Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network. , 2015, Talanta.

[30]  H. Chun,et al.  Multiplex polymerase chain reaction assays for the detection of the zearalenone chemotype of Fusarium species in white and brown rice. , 2018, International journal of food microbiology.

[31]  M. Mostafaei,et al.  Detection and classification of diesel-biodiesel blends by LDA, QDA and SVM approaches using an electronic nose , 2019 .

[32]  Xiaofeng Dai,et al.  A loop-mediated isothermal amplification (LAMP) assay for the rapid detection of toxigenic Fusarium temperatum in maize stalks and kernels. , 2019, International journal of food microbiology.

[33]  Ganesh Kumar Mani,et al.  Electronic noses for food quality : a review , 2015 .

[34]  J. Pitt,et al.  Fungi and Food Spoilage , 1987 .

[35]  J. Frisvad,et al.  Occurrence of Aspergillus section Flavi and aflatoxins in Brazilian rice: From field to market. , 2018, International journal of food microbiology.

[36]  Dariusz Wiącek,et al.  Application of electronic nose with MOS sensors to prediction of rapeseed quality , 2017 .

[37]  Bahram Hemmateenejad,et al.  Qualitative and quantitative analysis of toxic materials in adulterated fruit pickle samples by a colorimetric sensor array , 2018 .

[38]  Yubin Lan,et al.  Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose , 2018, Comput. Electron. Agric..

[39]  E. Angioletto,et al.  Incidence of toxigenic fungi and zearalenone in rice grains from Brazil. , 2018, International journal of food microbiology.

[40]  K. Ohtsubo,et al.  Analysis of Volatile Compounds in the Headspace of Rice Using SPME/ GC/ MS , 2001 .

[41]  R. Levin.  PCR detection of aflatoxin producing fungi and its limitations. , 2012, International journal of food microbiology.

[42]  Mahdi Ghasemi-Varnamkhasti,et al.  Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods , 2018, Trends in Food Science & Technology.

[43]  Jung-Hye Choi,et al.  Species composition of and fumonisin production by the Fusarium fujikuroi species complex isolated from Korean cereals. , 2018, International journal of food microbiology.

[44]  Jacek Gębicki,et al.  Discrimination of selected fungi species based on their odour profile using prototypes of electronic nose instruments , 2018 .