Food Analytical Methods

, Volume 12, Issue 10, pp 2226–2240 | Cite as

Advances in Electronic Nose Development for Application to Agricultural Products

  • Wenshen Jia
  • Gang LiangEmail author
  • Zhuojun Jiang
  • Jihua WangEmail author


High agricultural product quality is a fundamental requirement for consumers and odor is an important indicator that reflects product quality. Conventional analysis methods are based on sensory evaluation or on physic-chemical methods (e.g., high-performance liquid chromatography, liquid chromatography with tandem mass spectrometry). Analysis methods should be simple, quick, nondestructive, inexpensive, and specific, with good reproducibility and repeatability. Electronic noses can meet many of these requirements. Electronic nose development for agricultural product quality analysis has been increasing since the 1980s. This review summarizes the extensive achievements to date in electronic nose development for quality analysis/evaluation of agricultural products. First, we briefly introduce electronic noses and describe commonly used data analysis methods (e.g., artificial neural networks (ANNs), principal component analysis (PCA), linear discriminant analysis (LDA)). We then discuss the application of electronic noses to analysis of agricultural products (e.g., fruit, vegetables, tea, grain, meat from livestock and poultry, fish), including freshness evaluation, quality classification, and authenticity assessment variety identification, geographical origin identification, and disease detection. Finally, the problems, prospects, and likely future development of electronic noses for agricultural product quality analysis are highlighted.


Electronic nose Agricultural product Nondestructive testing Quality evaluation 



We thank David MacDonald, MSc, from Liwen Bianji, Edanz Editing China (, for editing the English text of a draft of this manuscript.

Funding Information

This study received financial support from the National Natural Science Foundation of China (No. 21806013, 31801634), the Special Projects of Construction of Science and Technology Innovation Ability of Beijing Academy of Agriculture and Forestry Sciences (No. KJCX20170420), the Beijing Agricultural Forestry Academy Youth Fund (No. QNJJ201630), the Beijing Natural Science Foundation (L182031), the International Cooperation Fund of Beijing Agricultural Forestry Academy (No. GJHZ2018-05), the Project of Beijing Science and Technology (No. Z171100001517017), the Project of Beijing Excellent Talents (No. 2017000020060G127), and the Open Project of Risk Assessment Laboratory for Agro-products of the Ministry of Agriculture (No. KFKT201707).

Compliance with Ethical Standards

Conflict of Interest

Wenshen Jia declares that he has no conflict of interest. Gang Liang declares that he has no conflict of interest. Zhuojun Jiang declares that he has no conflict of interest. Jihua Wang declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

Not applicable.


  1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS regression). WIRES Computl Stat 2:97–106Google Scholar
  2. Adak MF, Yumusak N (2016) Classification of e-nose aroma data of four fruit types by ABC-based neural network. Sensors 16:1–13CrossRefGoogle Scholar
  3. Aleixandre M, Santos J, Sayago I, Cabellos J, Arroyo T, Horrillo M (2015) A wireless and portable electronic nose to differentiate musts of different ripeness degree and grape varieties. Sensors 15:8429–8443CrossRefGoogle Scholar
  4. Alimelli A, Pennazza G, Santonico M, Paolesse R, Filippini D, D’Amico A, Lundström I, Di Natale C (2007) Fish freshness detection by a computer screen photoassisted based gas sensor array. Anal Chim Acta 582:320–328CrossRefGoogle Scholar
  5. Bhattacharyya N, Seth S, Tudu B, Tamuly P, Jana A, Ghosh D, Bandyopadhyay R, Bhuyan M, Sabhapandit S (2007) Detection of optimum fermentation time for black tea manufacturing using electronic nose. Sensor Actuat B-Chem 122:627–634CrossRefGoogle Scholar
  6. Biondi E, Blasioli S, Galeone A, Spinelli F, Cellini A, Lucchese C, Braschi I (2014) Detection of potato brown rot and ring rot by electronic nose : from laboratory to real scale. Talanta 129:422–430CrossRefGoogle Scholar
  7. Borràs E, Ferré J, Boqué R, Mestres M, Aceña L, Busto O (2015) Data fusion methodologies for food and beverage authentication and quality assessment – a review. Anal Chim Acta 891:1–14CrossRefGoogle Scholar
  8. Casalinuovo I, Di Pierro D, Coletta M, Di Francesco P (2006) Application of electronic noses for disease diagnosis and food spoilage detection. Sensors 6:1428–1439CrossRefGoogle Scholar
  9. Cellini A, Biondi E, Blasioli S, Rocchi L, Farneti B, Braschi I, Savioli S, Rodriguez-Estrada M, Biasioli F, Spinelli F (2016) Early detection of bacterial diseases in apple plants by analysis of volatile organic compounds profiles and use of electronic nose. Ann App Biol 168:409–420CrossRefGoogle Scholar
  10. Centonze V, Lippolis V, Cervellieri S, Damascelli A, Casiello G, Pascale M, Logrieco AF, Longobardi F (2019) Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds. Food Chem 277:25–30CrossRefGoogle Scholar
  11. Chang ZY (2013) Study on the multi-intelligence fusion technology for the meat freshness identification based on bionic nose [M]. Biol. Agric. Engine. Inst. Jilin: Jilin UniversityGoogle Scholar
  12. Chen Q, Liu A, Zhao J, Ouyang Q (2013) Classification of tea category using a portable electronic nose based on an odor imaging sensor array. J Pharm Biomed Anal 84:77–83CrossRefGoogle Scholar
  13. Chen J, Sun Y, Shen L (2015) Application of electronic nose in detecting quality of agriculture products. J Anhui Agric Sci 43:364–366Google Scholar
  14. Chen YS, Song K, Wang Q, Liu JH (2018) Research on self-validating MOS gas sensor array and its application. Chinese J Sens Actuators 31(5):677–682Google Scholar
  15. Cheng SM, Wang J, Wang YW, Ma YH (2013) Research on distinguishing tomato seedling infected with early blight disease using different characteristic parameters by electronic nose by electronic nose. Chinese J Sens Actuators 7:68–73Google Scholar
  16. Cheng H, Chen J, Chen S, Wu D, Liu D, Ye X (2015) Characterization of aroma-active volatiles in three Chinese bayberry ( Myrica rubra ) cultivars using GC–MS–olfactometry and an electronic nose combined with principal component analysis. Food Res Int 72:8–15CrossRefGoogle Scholar
  17. Cortellino G, Gobbi S, Rizzolo A (2016) Monitoring shelf life of fresh-cut apples packed in different atmospheres by electronic nose. Acta Hortic 1120:71–78Google Scholar
  18. Cozzolino D, Smyth HE, Lattey KA, Cynkar W, Janik L, Dambergs RG, Francis IL, Gishen M (2006) Combining mass spectrometry based electronic nose, visible–near infrared spectroscopy and chemometrics to assess the sensory properties of Australian Riesling wines. Anal Chim Acta 563:319–324CrossRefGoogle Scholar
  19. Cozzolino D, Cynkar WU, Shah N, Smith PA (2011) Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand? Food Chem 126:673–678CrossRefGoogle Scholar
  20. Dai Y, Zhi R, Zhao L, Gao H, Shi B, Wang H (2015) Longjing tea quality classification by fusion of features collected from E-nose. Chemom Intell Lab Syst 144:63–70CrossRefGoogle Scholar
  21. Dai C, Huang X, Lv R, Zhang Z, Sun J, Aheto JH (2018) Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS-SPME-GC-MS. J Food Saf 38(6):12555–12562Google Scholar
  22. Dai C, Huang XY, Huang DM, Lv RQ, Sun J, Zhang ZC, Ma M, Harrington Aheto J (2019) Detection of submerged fermentation of tremella aurantialba using data fusion of electronic nose and tongue. J Food Process Eng 42(3):13002–13003Google Scholar
  23. Di Rosa AR, Leone F, Cheli F, Chiofalo V (2017) Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – a review. J Food Eng 210:62–75CrossRefGoogle Scholar
  24. Dong WJ, Hu RS, Long YZ, Li HH, Zhang YJ, Zhu KX, Chu Z (2019) Comparative evaluation of the volatile profiles and taste properties of roasted coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS. Food Chem 272:723–731CrossRefGoogle Scholar
  25. Ezhilan M, Nesakumar N, Babu KJ, Srinandan CS, Rayappan JBB (2018) An electronic nose for royal delicious apple quality assessment—a tri-layer approach. Food Res Int 109:44–51CrossRefGoogle Scholar
  26. Feng L, Zhang M, Bhandari B, Guo ZM (2018) A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry tomato fruit treated with high pressure argon. Comput Electron Agric 154:411–419CrossRefGoogle Scholar
  27. Flambeau KJ, Lee WJ, Yoon J (2017) Discrimination and geographical origin prediction of washed specialty Bourbon coffee from different coffee growing areas in Rwanda by using electronic nose and electronic tongue. Food Sci Biotechnol 26:1245–1254CrossRefGoogle Scholar
  28. Franchina FA, Purcaro G, Burklund A, Beccaria M, Hill JE (2019) Evaluation of different adsorbent materials for the untargeted and targeted bacterial VOC analysis using GCxGC-MS. Anal Chim Acta 1066:146–153CrossRefGoogle Scholar
  29. Gancarz M, Wawrzyniak J, Gawrysiak-Witulska M, Wiacek D, Nawrocka A, Tadla M, Rusinek R (2017) Application of electronic nose with MOS sensors to prediction of rapeseed quality. Meas Sci Technol 103:227–234Google Scholar
  30. Gardner JW, Bartlett PN (1994) A brief history of electronic nose. Sensor Actuat B-Chem 18(1-3):210–211Google Scholar
  31. Ghasemi-Varnamkhasti M, Lozano J (2016) Electronic nose as an innovative measurement system for the quality assurance and control of bakery products: a review. Eng Agric Environ Food 9:365–374CrossRefGoogle Scholar
  32. Giungato P, Laiola E, Nicolardi V (2017) Evaluation of industrial roasting degree of coffee beans by using an electronic nose and a stepwise backward selection of predictors. Food Anal Methods 10:3424–3433CrossRefGoogle Scholar
  33. Gliszczyńska-Świgło A, Chmielewski J (2017) Electronic nose as a tool for monitoring the authenticity of food—a review. Food Anal Methods 10:1800–1816CrossRefGoogle Scholar
  34. Goyal S, Goyal GK (2011) Cascade and feedforward backpropagation artificial neural networks models for prediction of sensory quality of instant coffee flavoured sterilized drink. Can J Artif Intel Mach Learn Pattern Recogn 2:78–82Google Scholar
  35. Gu SQ, Wang XC, Liu Y, Zhao Y, Zhang JJ, Xie J, Zheng JZ (2010) Electronic nose for measurement of freshness change of chilled pork during storage at different temperatures. Food Sci Technol 31:172–176Google Scholar
  36. Haddi Z, El BN, Tahri K, Bougrini M, El BN, Llobet E, Bouchikhi B (2015) Instrumental assessment of red meat origins and their storage time using electronic sensing systems. Anal Methods 7:5193–5203CrossRefGoogle Scholar
  37. Han F, Huang X, Teye E, Gu F, Gu H (2014) Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis. Anal Methods 6:529–536CrossRefGoogle Scholar
  38. He YB, Zhang WJ, Dong YS (2018) Temperature compensation technique of metal oxide semiconductor gas sensor. Mach Tool Hydraulics 46(6):43–46Google Scholar
  39. Herrero JL, Lozano J, Santos JP, Suárez JI (2016) On-line classification of pollutants in water using wireless portable electronic noses. Chemosphere 152:107–116CrossRefGoogle Scholar
  40. Hong X, Wang J (2014) Detection of adulteration in cherry tomato juices based on electronic nose and tongue: comparison of different data fusion approaches. J Food Eng 126:89–97CrossRefGoogle Scholar
  41. Hong XZ, Wei ZB, Hai Z (2014) Application of electronic nose and neural network in beef freshness detection. Modern Food Sci Technol 30:278–284Google Scholar
  42. Hou XM, Zhao XW, Duan LR (2016) Researches and applications of high-performance gas sensor based on metal oxide. Mater Report 30(2):8–14Google Scholar
  43. Hui G, Wu Y, Ye D, Ding W (2013) Fuji apple storage time predictive method using electronic nose. Food Anal Methods 6:82–88CrossRefGoogle Scholar
  44. Jia W, Liang G, Wang Y, Wang J (2018) Electronic noses as a powerful tool for assessing meat quality: a mini review. Food Anal Methods 11:2916–2924CrossRefGoogle Scholar
  45. Konduru T, Rains GC, Li C (2015) Detecting sour skin infected onions using a customized gas sensor array. J Food Eng 160:19–27CrossRefGoogle Scholar
  46. Kovács Z, Dalmadi I, Lukács L, Sipos L, Szántai-Kőhegyi K, Kókai Z, Fekete A (2010) Geographical origin identification of pure Sri Lanka tea infusions with electronic nose, electronic tongue and sensory profile analysis. J Chemom 24:121–130CrossRefGoogle Scholar
  47. Lee W-H, Choi S, Oh I-N, Shim J-Y, Lee K-S, An G, Park J-T (2017) Multivariate classification of the geographic origin of Chinese cabbage using an electronic nose-mass spectrometry. Food Sci Biotechnol 26:603–609CrossRefGoogle Scholar
  48. Li YH, Bao JQ, Zhou QS, Yu SY, Ren Q, Huang W (2014). Evaluation of freshness of Carassius Auratus based on electronic nose. Sci Tech Food Ind 35(19): 284–287.Google Scholar
  49. Lei L. (2011) Study on detection methods freshness of meat. Biol. Agric. Engine. Inst. Jilin: Jilin UniversityGoogle Scholar
  50. Li H, Chen Q, Zhao J, Ouyang Q (2014) Non-destructive evaluation of pork freshness using a portable electronic nose (E-nose) based on a colorimetric sensor array. Anal Methods 6:6271–6277Google Scholar
  51. Li J, Zheng FX, Jiang JH, Lin H, Hui GH (2015) Chinese bayberry (Myrica rubra Sieb. et Zucc.) quality determination based on an electronic nose and non-linear dynamic model. Anal Methods 7:9928–9939CrossRefGoogle Scholar
  52. Lippolis V, Pascale M, Cervellieri S, Damascelli A, Visconti A (2014) Screening of deoxynivalenol contamination in durum wheat by MOS-based electronic nose and identification of the relevant pattern of volatile compounds. Food Control 37:263–271CrossRefGoogle Scholar
  53. Liu W, Hui G (2015) Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose. Bioengineered 6:53–61CrossRefGoogle Scholar
  54. Longobardi F, Casiello G, Ventrella A, Mazzilli V, Nardelli A, Sacco D, Catucci L, Agostiano A (2015) Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries. Food Chem 170:90–96CrossRefGoogle Scholar
  55. Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JBB (2015) Electronic noses for food quality: a review. J Food Eng 144:103–111CrossRefGoogle Scholar
  56. Luo D, Chen J, Gao L, Liu Y, Wu J (2017) Geographical origin identification and quality control of Chinese chrysanthemum flower teas using gas chromatography–mass spectrometry and olfactometry and electronic nose combined with principal component analysis. Int J Food Sci Technol 52:714–723CrossRefGoogle Scholar
  57. Ma YY, Guo BL, Wei YM, Wei S, Zhao H (2014) The feasibility and stability of distinguishing the kiwi fruit geographical origin based on electronic nose analysis. Food Sci Technol Res 20:1173–1181CrossRefGoogle Scholar
  58. Neha M, Pratiksha K, Minal B (2016) Review on fruit disease direction using color, texture analysis and ANN with E-nose. Imperial J Interdiscip Res 2:759–763Google Scholar
  59. Oliveira A, Bambrick ST, Pfund LY, Diamandopoulos P, Gong X, Liang X, Mowery MD, Zheng J (2019) Analysis of thermolabile residual solvents in a spray dried dispersion using static headspace gas chromatography. J Sep Sci 42(6):1222–1229CrossRefGoogle Scholar
  60. Pan L, Zhang W, Zhu N, Mao S, Tu K (2014) Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry. Food Res Int 62:162–168CrossRefGoogle Scholar
  61. Peris M, Escuder-Gilabert L (2009) A 21st century technique for food control: electronic noses. Anal Chim Acta 638:1–15CrossRefGoogle Scholar
  62. Peris M, Escuder-Gilabert L (2013) On-line monitoring of food fermentation processes using electronic noses and electronic tongues: a review. Anal Chim Acta 804:29–36CrossRefGoogle Scholar
  63. Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299:352–355CrossRefGoogle Scholar
  64. Priddy KL, Keller PE (2005) Artificial neural networks: an introduction. SPIE Press, BellinghamCrossRefGoogle Scholar
  65. Qian L, Lu H, Lu B (2016) Classification and recognition of bionic electronic nose for rice with geographical indications. J Chinese Oils Assoc 31:132–139Google Scholar
  66. Qiu S, Wang J, Gao L (2015) Qualification and quantisation of processed strawberry juice based on electronic nose and tongue. Food Sci Technol 60:115–123Google Scholar
  67. Raigar RK, Upadhyay R, Mishra HN (2017) Storage quality assessment of shelled peanuts using non-destructive electronic nose combined with fuzzy logic approach. Postharvest Biol Technol 132:43–50CrossRefGoogle Scholar
  68. Ren YM, Ramaswamy HS, Li Y, Yuan CL, Reny XL (2018) Classification of impact injury of apples using electronic nose coupled with multivariate statistical analyses. J Food Process Eng:41(5):12698–12705Google Scholar
  69. Russo M, Di SR, Cafaly V (2013) Non-destructive flavor evaluation of red onion (Allium cepa L) ecotypes: an electronic-nose-based approach. Food Chem 141(2):896–899Google Scholar
  70. Rutolo MF, Clarkson JP, Covington JA (2018) The use of an electronic nose to detect early signs of soft-rot infection in potatoes. Biosys Eng 167:137–143Google Scholar
  71. Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M (2014) Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA, and SVM). CZECH J Food Sci 32:538–548Google Scholar
  72. Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M (2016) Application of MOS based electronic nose for the prediction of banana quality properties. Measurement 82:105–114CrossRefGoogle Scholar
  73. Sanaeifar A, ZakiDizaji H, Jafari A, Guardia MDL (2017) Early detection of contamination and defect in foodstuffs by electronic nose: a review. TrAC Trends Anal Chem 97:257–271CrossRefGoogle Scholar
  74. Sharma M, Ghosh D, Bhattacharya N (2013) Electronic nose—a new way for predicting the optimum point of fermentation of black tea. Int J Eng Sci Invent 2:56–60Google Scholar
  75. Sharma P, Ghosh A, Tudu B, Sabhapondit S, Baruah BD, Tamuly P, Bhattacharyya N, Bandyopadhyay R (2015) Monitoring the fermentation process of black tea using QCM sensor based electronic nose. Sensors Actuators B Chem 219:146–157CrossRefGoogle Scholar
  76. Shi ZB, Tong YY, Chen DH (2009) Identification of beef freshness with electronic nose. Trans Chinese Soc Agric Mach 40:184–188Google Scholar
  77. Śliwińska M, Wiśniewska P, Dymerski T, Namieśnik J, Wardencki W (2014) Food analysis using artificial senses. J Agric Food Chem 62:1423–1448CrossRefGoogle Scholar
  78. Song XQ, Ren YM, Zhang YY, Li Y, Peng GY, Ma T (2014) Prediction of kiwifruit quality during cold storage by electronic nose. Food Sci 35:230–235Google Scholar
  79. Sun J, Wang Q, Huang J (2013) Influence of heating temperature on the development of volatile compounds in bigeye tuna meat (Thunnus obesus ) as assessed by e-nose and SPME-GC/MS. Int Food Res J 20:3077–3083Google Scholar
  80. Sun T, Yue X, Zhang P (2014) Prediction of freshness of beef at ice temperature storage by electronic nose. Food Fermentation Ind 4:185–189Google Scholar
  81. Sung J, Kim B, Kim B, Kim Y (2014) Mass spectrometry-based electric nose system for assessing rice quality during storage at different temperatures. J Stored Prod Res 59:204–208CrossRefGoogle Scholar
  82. Tian XY, Cai Q, Zhang YM (2012) Rapid classification of hairtail fish and pork freshness using an electronic nose based on the PCA method. Sensors 12:260–277CrossRefGoogle Scholar
  83. Tian XJ, Wang J, Cui SQ (2013) Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors. J Food Eng 119:744–749Google Scholar
  84. Tohidi M, Ghasemi-Varnamkhasti M, Ghafarinia V, Saeid Mohtasebi S, Bonyadian M (2018) Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory system: a novel method. Measurement 124:120–129CrossRefGoogle Scholar
  85. Wang J, Cui S, Chen X, Hong X, Qiu S (2013a) Advanced technology and new application in electronic nose. Trans Chinese Soc Agric Mach 44:160–167Google Scholar
  86. Wang J, Xue Z, Wang Y (2013b) Discrimination of storage time for using metal oxide semiconductor-type e-nose. Sensor Mater 25:257–268Google Scholar
  87. Wang S, Li WJ, Wei LH, Liu Y, Bing FL (2015) Detection of organic lapsang souchong black tea based on electronic nose. Food Sci Technol 40:292–295Google Scholar
  88. Wang Q, Li L, Ding W, Zhang DQ, Wang JY, Reed K, Zhang B (2019) Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Control 98:431–438Google Scholar
  89. Wei X, Shao X, Wei Y, Cheong L, Pan L, Tu K (2018a) Rapid detection of adulterated peony seed oil by electronic nose. J Food Sci Technol 55:2152–2159CrossRefGoogle Scholar
  90. Wei X, Zhang YC, Wu D, Wei ZB, Chen KS (2018b) Rapid and non-destructive detection of decay in peach fruit at the cold environment using a self-developed handheld electronic-nose system. Food Anal Methods 11:2990–3004CrossRefGoogle Scholar
  91. Wen T, Zheng LZ, Dong S, Gong ZL (2019) Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Technol 147:156–165CrossRefGoogle Scholar
  92. Wu SG, Zhang YH, Meng Y, Chen JD, Zhang YM, Zhang YL (2015) Analysis of chicken quality deterioration by electronic nose. Food Sci Technol Int 36:53–56Google Scholar
  93. Wu H, Wang J, Yue T, Yuan Y (2017) Variety-based discrimination of apple juices by an electronic nose and gas chromatography-mass spectrometry. Int J Food Sci Technol 52:2324–2333CrossRefGoogle Scholar
  94. Xu HH (2016) Study on nondestructive detection of freshness of post-harvest spinach based on machine vision and electronic nose. Jiangsu UniversityGoogle Scholar
  95. Xu S, Zhou ZY, Lu HZ, Luo X, Lan Y (2014a) Improved algorithms for the classification of rough rice using a bionic electronic nose based on PCA and the wilks distribution. Sensors 14:5486–5501CrossRefGoogle Scholar
  96. Xu S, Zhou ZY, Luo XW (2014b) Classification and recognition of hybrid and inbred rough rice based on bionic electronic nose. Trans Chinese Soc Agric Eng 30:133–139Google Scholar
  97. Xu J, Zhao X, Sun K, Wang Z, Tu K (2016) Determination on freshness of strawberry based on electronic nose and ethanol sensor. Food Mach 32:117–121Google Scholar
  98. Xu S, Sun X, Lu H, Yang H, Ruan Q, Huang H, Chen M (2018a) Detecting and monitoring the flavor of tomato (Solanum lycopersicum) under the impact of postharvest handlings by physicochemical parameters and electronic nose. Sensors 18:1847–1861Google Scholar
  99. Xu S, Zhou Z, Tian L, Lu H, Luo X, Lan Y (2018b) Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose. Comput Electron Agric 152:19–25CrossRefGoogle Scholar
  100. Xu M, Wang J, Gu S (2019) Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy. J Food Eng 241:10–17CrossRefGoogle Scholar
  101. Xue DW, Yang CL (2014) Quality detection of Mao Feng tea in mount Huangshan based on electronic nose technology. J Hubei Eng Univ 34:64–67Google Scholar
  102. Yan MY, Lu YQ, Chen DW (2015) Application of electronic nose in freshness evaluation of tilapia fillets as affected by ozone treatment. Food Sci Technol 36:265–269Google Scholar
  103. Yin Y, Hao YF, Yu HC (2016) Identification method for different moldy degrees of maize using electronic nose coupled with multi-features fusion. Trans Chinese Soc Agric Eng 32:254–260Google Scholar
  104. Ying X, Liu W, Hui G (2015) Litchi freshness rapid non-destructive evaluating method using electronic nose and non-linear dynamics stochastic resonance model. Bioengineered 6:218–221CrossRefGoogle Scholar
  105. Yu HC, Meng MJ, Yin Y (2013) The research on the application of electronic nose in discriminate the rice varieties. Proceedings of the 2013 International Conference on Advanced Mechatronic Systems, Luoyang, China September: 25–27Google Scholar
  106. Yu H, Zhang Y, Zhao J, Tian H (2018) Taste characteristics of Chinese bayberry juice characterized by sensory evaluation, chromatography analysis, and an electronic tongue. J Food Sci Technol 55:1624–1631CrossRefGoogle Scholar
  107. Zakaria A, Shakaff AYM, Masnan MJ, Saad FSA, Adom AH, Ahmad MN, Jaafar MN, Abdullah AH, Kamarudin LM (2012) Improved maturity and ripeness classifications of Magnifera Indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. Sensors 12: 6023Google Scholar
  108. Zhang LY, Wen LJ, Zhou F, Zhang S, Yang PY (2003) Electronic nose for the determination of formaldehyde in air. Chem J Chin Univ 24:184–188Google Scholar
  109. Zhang W, Pan L, Zhao X (2016) A study on soluble solids content assessment using electronic nose: persimmon fruit picked on different dates. Int J Food Properties 19:53–62Google Scholar
  110. Zhao X, Wu H, Pan L, Tu K (2014) Nondestructive prediction of postharvest strawberry quality by electronic nose. Food Sci 35:105–110Google Scholar
  111. Zhao MX, Ding XM, Cao R (2015) Identification of lateolabrax japonicus freshness by electronic nose. Food Sci 34:143–145Google Scholar
  112. Zhou CL, Mi L, Hu XY, Zhu BH (2017) Evaluation of three pumpkin species: correlation with physicochemical, antioxidant properties and classification using SPME-GC–MS and E-nose methods. J Food Sci Technol 54:3118–3131CrossRefGoogle Scholar
  113. Zhu N, Mao S, Zhu L (2013) Early detection of fungal disease infection in strawberry fruits by e-nose during postharvest storage. Trans Chinese Soc Agric Eng 29:266–273Google Scholar
  114. Zhu J, Chen F, Wang L, Niu Y, Xiao Z (2017) Evaluation of the synergism among volatile compounds in oolong tea infusion by odour threshold with sensory analysis and E-nose. Food Chem 221:1484–1490CrossRefGoogle Scholar
  115. Zou Y, Wan H, Zhang X, Ha D, Wang P (2015) Electronic nose and electronic tongue. In: Wang P, Liu Q, Wu C, Hsia KJ (eds) Bioinspired smell and taste sensors. Springer, Netherlands, pp 19–44CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Research Center for Agricultural Standards and TestingBeijing Academy of Agriculture and Forestry ScienceBeijingPeople’s Republic of China
  2. 2.Risk Assessment Lab for Agro-products (Beijing)Ministry of AgricultureBeijingPeople’s Republic of China
  3. 3.Beijing Municipal Key Laboratory of Agriculture Environment MonitoringBeijingPeople’s Republic of China
  4. 4.State Key Laboratory of Water Environment Simulation, School of EnvironmentBeijing Normal UniversityBeijingChina

Personalised recommendations