Non-destructive test to detect adulteration of rice using gas sensors coupled with chemometrics methods
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Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
Department of Petroleum Engineering, Knowledge University, Erbil 44001, Iraq
Department of Electric Technology, Electronics and Automation, University of Extremadura, Avda. de Elvas S/n, 06006, Badajoz, Spain
Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Kraków, Poland
Department of Biosystems Engineering, University of Tehran, Tehran 11365-4117, Iran
Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Final revision date: 2023-05-01
Acceptance date: 2023-05-09
Publication date: 2023-07-10
Corresponding author
Marek Gancarz   

Faculty of Production and Power Engineering, University of Agriculture in Krakow, Poland
Int. Agrophys. 2023, 37(3): 235–244
  • E-nose can identified quick and non-destructively pure and mixed rice
  • Chemometric methods were used for the data analysis of the sensor arrays
  • Chemometrics and ANN were used simultaneously to classify the samples
In order to accurately determine and evaluate the odour of rice, it is necessary to identify the substances that affect that odour and to develop methods to determine their amounts. For more than three decades, researchers have been studying the factors that produce and influence the aroma of rice. An electronic nose can be used to detect the volatile compounds of rice, while an olfactory machine is capable of classifying and detecting the variety, origin, and storage time of rice with a high degree of efficiency. This study aimed to investigate the efficacy of electronic noses and other chemometric methods such as principal component analysis, linear discriminant analysis, and the Artificial Neural Network as a cost-effective, rapid, and non-destructive method for the detection of pure and adulterated rice varieties. Therefore, an electronic nose equipped with nine metal oxide semiconductor sensors with low power consumption was used. The results showed that the amount of variance accounted for by PC1 and PC4 was 98% for the samples used. Also, the classification accuracy of the linear discriminant analysis and Artificial Neural Network methods were 100%, respectively. The Support Vector Machines method (including Nu-SVM and C-SVM) was also used, which, in all its functions except the polynomial function, produced 100% accuracy in terms of training and validation.
The authors declare that there is no conflict of interest regarding the publication of this paper. Data availability statement: The datasets used and/or analysed during the current study are available from the corresponding author if a reasonable request is made.
Abdullah A., Adom A., Shakaff A.M., Masnan M., Zakaria A., Rahim N., and Omar O., 2015. Classification of Malaysia aromatic rice using multivariate statistical analysis. Conf. Proc. AIP, AIP Publishing.
Abdullah A., Rahim N., Masnan M., Sa’ad F., Zakaria A., Shakaff A., and Omar O., 2016. Rice and the electronic nose. In: Electronic Noses and Tongues in Food Science (Eds Victor R. Preedy, Maria Rodriguez Mendez). Elsevier, Academic Press.
Afkari-Sayyah A.H., Khorramifar A., and Karami H., 2021. Identification and classification of different grape cultivars using cultivar leaves by electroni nose. J. Environ. Sci. Studies, 6(4), 4382-4389.
Aghili N.S., Rasekh M., Karami H., Azizi V., and Gancarz M., 2022. Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography-mass spectrometry. LWT, 167, 113863. doi:
Arendse E., Nieuwoudt H., Magwaza L.S., Nturambirwe J.F.I., Fawole O.A., and Opara U.L., 2021. Recent advancements on vibrational spectroscopic techniques for the detection of authenticity and adulteration in horticultural products with a specific focus on oils, juices and powders. Food Bioprocess Technol., 14(1), 1-22.
Bieganowski A., Józefaciuk G., Bandura L., Guz Ł., Łagód G., and Franus W., 2018. Evaluation of hydrocarbon soil pollution using E-Nose. Sensors, 18(8), 2463.
Capone S., Epifani M., Quaranta F., Siciliano P., Taurino A., and Vasanelli L., 2001. Monitoring of rancidity of milk by means of an electronic nose and a dynamic PCA analysis. Sensors Actuators B: Chemical, 78(1-3), 174-179.
Casas-Ferreira A.M., del Nogal-Sánchez M., Pérez-Pavón J.L., and Moreno-Cordero B., 2019. Non-separative mass spectrometry methods for non-invasive medical diagnostics based on volatile organic compounds: A review. Analytica Chimica Acta, 1045, 10-22.
Cevoli C., Cerretani L., Gori A., Caboni M., Toschi T.G., and Fabbri A., 2011. Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC-MS analysis of volatile compounds. Food Chemistry, 129(3), 1315-1319.
Champagne E.T., 2008. Rice aroma and flavor: a literature review. Cereal Chemistry, 85(4), 445-454.
Cheaupun K., Wongpiyachon S., and Kongseree N., 2003. Improving Rice Grain Quality in Thailand Rice is Life. Proc.World Rice Research, Japan in Scientific Perspectives For The 21st Century, 20013, 248-249.
Choudhury P., Kohli S., Srinivasan K., Mohapatra T., and Sharma R., 2001. Identification and classification of aromatic rices based on DNA fingerprinting. Euphytica, 118(3), 243-251.
Crowhurst D.G., and Creed P.G., 2001. Effect of cooking method and variety on the sensory quality of rice. Food Service Technol., 1(3), 133-140.
Devos M., Patte F., Rouault J., Laffort P., and van Gemert L.J. (Eds) 1990. Standardized human olfactory thresholds, Oxford University Press, Oxford.
Fitzgerald M.A., McCouch S.R., and Hall R.D., 2009. Not just a grain of rice: the quest for quality. Trends Plant Sci., 14(3), 133-139.
Frigerio G., Mercadante R., Polledri E., Missineo P., Campo L., and Fustinoni S., 2019. An LC-MS/MS method to profile urinary mercapturic acids, metabolites of electrophilic intermediates of occupational and environmental toxicants. J. Chromatography B, 1117, 66-76.
Fukai Y. and Tukada K., 2006. Influence of pre-washing on quality of cooked rice maintained at a constant temterature (Influence of cooking conditions on quality of cooked rice, 1). J. Japanese Society Food Sci. Technol. (Japan), 587-591.
Gancarz M., Dobrzański B. Jr., Malaga-Toboła U., Tabor S., Combrzyński M., Ćwikła D., Strobel W.R., Oniszczuk A., Karami H., Darvishi Y., Żytek A., Rusinek R., 2022. Impact of coffee bean roasting on the content of pyridines determined by analysis of volatile organic compounds. Molecules, 27(5), 1559.
Georgouli K., Del Rincon J.M., and Koidis A., 2017. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry, 217, 735-742.
Ghiasvand A.R., Setkova L., and Pawliszyn J., 2007. Determination of flavour profile in Iranian fragrant rice samples using cold‐fibre SPME-GC-TOF-MS. Flavour Fragrance J., 22(5), 377-391.
Grimm C.C., Bergman C., Delgado J.T., and Bryant R., 2001. Screening for 2-acetyl-1-pyrroline in the headspace of rice using SPME/GC-MS. J. Agric. Food Chem., 49(1), 245-249.
Hai Z., and Wang J., 2006. Electronic nose and data analysis for detection of maize oil adulteration in sesame oil. Sensors Actuators B: Chemical, 119(2), 449-455. doi:
Han Z., Wan J., Deng L., and Liu K., 2016. Oil Adulteration identification by hyperspectral imaging using QHM and ICA. PLoS One, 11(1), e0146547.
Hidayat S.N., Triyana K., Fauzan I., Julian T., Lelono D., Yusuf Y., Ngadiman N., Veloso A.C.A., and Peres A.M., 2019. The electronic nose coupled with chemometric tools for discriminating the quality of black tea samples in situ. Chemosensors, 7(3), 29.
Hu G.X., Wang J., Wang J.J., and Wang X.L., 2011. Detection for rice odors and identification of varieties based on electronic nose technique. J. Zhejiang University (Agric. Life Sci.), 37(6), 670-676.
Huichun Y., Zuozhou X., and Yong Y., 2012. The identification of rice varieties based on electronic nose. J. Chinese Cereals Oils Associ., 27, 105-109.
Jana A., Bandyopadhyay R., Tudu B., Roy J.K., Bhattacharyya N., Adhikari B., Kundu Ch., and Mukherjee S., 2011. Classification of aromatic and non-aromatic rice using electronic nose and artificial neural network. IEEE Recent Advances in Intelligent Computational Systems. 10.1109/RAICS.2011.6069320.
Karami H., Rasekh M., and Mirzaee-Ghaleh E., 2020a. Application of the E-nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J. Food Proc. Preserv., 44(9), e14696. doi:
Karami H., Rasekh M., and Mirzaee-Ghaleh E., 2020b. Qualitative analysis of edible oil oxidation using an olfactory machine. J. Food Measurement Characterization, 14(5), 2600-2610.
Karami H., Rasekh M., and Mirzaee‐Ghaleh E., 2020c. Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. J. Food Processing Preservation, 44(9), e14696.
Karami H., Rasekh M., and Mirzaee-Ghaleh E., 2020d. Comparison of chemometrics and AOCS official methods for predicting the shelf life of edible oil. Chemometrics Intelligent Laboratory Systems, 206, 104165.
Karami H., Rasekh M., and Mirzaee-Ghaleh E., 2021. Identification of olfactory characteristics of edible oil during storage period using metal oxide semiconductor sensor signals and ANN methods. J. Food Proc. Preserv., 45(10), e15749. doi:
Karoui R., and Blecker C., 2011. Fluorescence spectroscopy measurement for quality assessment of food systems – a review. Food Bioprocess Technol., 4(3), 364-386.
Kaur H., and Singh B., 2013. Classification and grading rice using multi-class SVM. Int. J. Scientific Research Publications, 3(4), 1-5.
Khorramifar A., Karami H., Wilson A.D., Sayyah A.H.A., Shuba A., and Lozano J., 2022a. Grape cultivar identification and classification by machine olfaction analysis of leaf volatiles. Chemosensors, 10(4), 125. doi:
Khorramifar A., Rasekh M., Karami H., Covington J.A., Derakhshani S.M., Ramos J., and Gancarz M., 2022b. Application of MOS gas sensors coupled with chemometrics methods to predict the amount of sugar and carbohydrates in potatoes. Molecules, 27(11), 3508.
Khorramifar A., and Rasekh M., 2022. Changes in sugar and carbohydrate content of different potato cultivars during storage. J. Environ. Sci. Studies, 7(1), 4643-4650.
Khorramifar A., Rasekh M., Karami H., Malaga-Toboła U., and Gancarz M., 2021. A machine learning method for classification and identification of potato cultivars based on the reaction of MOS type sensor-array. Sensors, 21(17), 5836. doi:
Kong W.-L., Rui L., Ni H., and Wu X.-Q., 2020. Antifungal effects of volatile organic compounds produced by Rahnella aquatilis JZ-GX1 against Colletotrichum gloeosporioides in Liriodendron chinense× tulipifera. Frontiers in Microbiology, 11, 1114.
Korifi R., Le Dréau Y., Molinet J., Artaud J., and Dupuy N., 2011. Composition and authentication of virgin olive oil from French PDO regions by chemometric treatment of Raman spectra. J. Raman Spectroscopy, 42(7), 1540-1547.
Lerma-García M.J., Ramis-Ramos G., Herrero-Martínez J.M., and Simó-Alfonso E.F., 2010. Authentication of extra virgin olive oils by Fourier-transform infrared spectroscopy. Food Chemistry, 118(1), 78-83.
Li B., Luo Y., Guo C., Yang Y., Yuan X., Xing M., Fan P., Shu C., Li F., Fu H., Yang Z., Chen Z., Ma J., Sun Y., and Sun Y., 2022. Effects of wheat straw returning and potassium application rates on the physicochemical properties and lodging resistance of different stem internodes in direct-seeded rice. Int. Agrophys., 36(4), 309-321.
Li B., Wang H., Zhao Q., Ouyang J., and Wu Y., 2015. Rapid detection of authenticity and adulteration of walnut oil by FTIR and fluorescence spectroscopy: A comparative study. Food Chemistry, 181, 25-30.
Li Y., Fang T., Zhu S., Huang F., Chen Z., and Wang Y., 2018. Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 189, 37-43.
Lim M.Y., Huang J., He F.-R., Zhao B.-X., Zou H.-Q., Yan Y.-H., . . . Xie J.-J., 2020. Quality grade classification of China commercial moxa floss using electronic nose: A supervised learning approach. Medicine, 99(33).
Liu C., Hao G., Su M., Chen Y., and Zheng L., 2017. Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste. J. Food Eng., 215, 78-83. doi:
Liu T., Zhang W., McLean P., Ueland M., Forbes S.L., and Su S.W., 2018. Electronic nose-based odor classification using genetic algorithms and fuzzy support vector machines. Int. J. Fuzzy Systems, 20(4), 1309-1320.
Llobet E., Hines E.L., Gardner J.W., and Franco S., 1999. Non-destructive banana ripeness determination using a ne-ural network-based electronic nose. Measurement Sci. Technol., 10(6), 538.
Mildner-Szkudlarz S., and Jeleń H.H., 2008. The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil. Food Chemistry, 110(3), 751-761.
Noorsal E., 2005. Development of electronic nose system using quartz crystal microbalance odour sensor array. MSc. Thesis, Universiti Sains Malaysia.
Qamar M., Siyah Poush M.R., and Hasibi P., 2013. Salinity tolerance assessment of rice sucrose transporter antisense lines (OsSUT1) at seedling stage (Oryza sativa var. TaiPai). Agricultural Biotechnology J., 5(3), 87-98.
Rahimzadeh H., Sadeghi M., Mireei S.A., and Ghasemi-Varnamkhasti M., 2022. Unsupervised modelling of rice aroma change during ageing based on electronic nose coupled with bio-inspired algorithms. Biosystems Eng., 216, 132-146.
Rasekh M., and Karami H., 2021a. Application of electronic nose with chemometrics methods to the detection of juices fraud. J. Food Proc. Preservation, 45(5), e15432. doi:
Rasekh M., and Karami H., 2021b. E-nose coupled with an artificial neural network to detection of fraud in pure and industrial fruit juices. Int. J. Food Properties, 24(1), 592-602. doi:
Rasooli Sharabiani V., and Khorramifar A., 2022. Recognition and classification of pure and adulterated rice using the electronic nose. J. Environ. Sci. Studies, 7(2), 4904-4910.
Reis N., Franca A.S., and Oliveira L.S., 2013. Discrimination between roasted coffee, roasted corn and coffee husks by Diffuse Reflectance Infrared Fourier Transform Spectroscopy. LWT-Food Sci. Technol., 50(2), 715-722.
Rusinek R., Dobrzański B., Jr., Oniszczuk A., Gawrysiak-Witulska M., Siger A., Karami H., Ptaszyńska A.A., Żytek A., Kapela K., and Gancarz M., 2022. How to identify roast defects in coffee beans based on the volatile compound profile. Molecules, 27, 8530.
Shen F., Wu Q., Su A., Tang P., Shao X., and Liu B., 2016. Detection of adulteration in freshly squeezed orange juice by electronic nose and infrared spectroscopy. Czech J. Food Sci., 34(3), 224-232.
Shen F., Yang D., Ying Y., Li B., Zheng Y., and Jiang T., 2012. Discrimination between Shaoxing wines and other Chinese rice wines by near-infrared spectroscopy and chemometrics. Food Bioprocess Technol, 5(2), 786-795.
Shi C., Yang X., Han S., Fan B., Zhao Z., Wu X., and Qian J., 2018. Nondestructive prediction of tilapia fillet freshness during storage at different temperatures by integrating an electronic nose and tongue with radial basis function neural networks. Food Bioprocess Technol., 11(10), 1840-1852.
Tatli S., Mirzaee-Ghaleh E., Rabbani H., Karami H., and Wilson A.D., 2022. Rapid detection of urea fertilizer effects on VOC emissions from cucumber fruits using a MOS E-Nose sensor array. Agronomy, 12(1), 35. doi:
van Ek J.A. and Trim J.L.M., 1998. Thresholds, 1990. Cambridge University Press.
Vapnik V.N., 2000. The Nature of Statistical Learning Theory. Springer, Berlin.
Vardin H., Tay A., Ozen B., and Mauer L., 2008. Authentication of pomegranate juice concentrate using FTIR spectroscopy and chemometrics. Food Chemistry, 108(2), 742-748.
Wang J., Nuñovero N., Nidetz R., Peterson S.J., Brookover B.M., Steinecker W.H., and Zellers E.T., 2019. Belt-mounted micro-gas-chromatograph prototype for determining personal exposures to volatile-organic-compound mixture components. Analytical Chemistry, 91(7), 4747-4754.
Wongpornchai S., Dumri K., Jongkaewwattana S., and Siri B., 2004. Effects of drying methods and storage time on the aroma and milling quality of rice (Oryza sativa L.) cv. Khao Dawk Mali 105. Food Chemistry, 87(3), 407-414.
Xie L.-J., Ye X.-Q., Liu D.-H., and Ying Y.-B., 2008. Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy. J. Zhejiang University Science B, 9(12), 982-989.
Xu S., Zhou Z., Lu H., Luo X., and Lan Y., 2014. Improved algorithms for the classification of rough rice using a bionic electronic nose based on PCA and the wilks distribution. Sensors, 14(3), 5486-5501.
Yin Y., Hao Y., Yu H., Liu Y., and Hao F., 2017. Detection potential of multi-features representation of e-nose data in classification of moldy maize samples. Food Bioprocess Technol., 10(12), 2226-2239.
Yinian L., Yulun C., Qishuo D., Ruiyin H., and Weimin D., 2022. Analysis of relationship between head rice yield and breaking force of Japonica rice grains at different maturity stages. Int. Agrophysics, 36(1), 1-11.
Zhang H., Wang J., Ye S., and Chang M., 2012. Application of electronic nose and statistical analysis to predict quality indices of peach. Food Bioprocess Technol., 5(1), 65-72.
Zheng X.-Z., Lan Y.-B., Zhu J.-M., Westbrook J., Hoffmann W., and Lacey R., 2009. Rapid identification of rice samples using an electronic nose. J. Bionic Engineering, 6(3), 290-297.
Zhou Z., Kearnes S., Li L., Zare R.N., and Riley P., 2019. Optimization of molecules via deep reinforcement learning. Scientific reports, 9(1), 1-10.
Żytek A., Rusinek R., Oniszczuk A., and Gancarz M., 2023. Effect of the consolidation level on organic volatile compound emissions from maize during storage. Materials, 16, 3066.