Commercial Instant Coffee Classification Using an Electronic Nose in Tandem with the ComDim-LDA Approach

  • Gustavo Yasuo Figueiredo Makimori
  • Evandro BonaEmail author


Coffee is an important commodity for Brazil and ensuring product quality is a priority. An electronic nose (E-nose), with seven MOS sensors, was used to analyze 53 samples of six different commercial instant coffees produced by the same industry. Thereafter, chemometric tools such as common dimension analysis (ComDim) and linear discriminant analysis (LDA) were applied to classify the samples. ComDim is an unsupervised multiblock analysis able to reduce large data dimensions from different tables. A block for each E-nose sensor with the first derivative of the transient signal was used as ComDim input. Four common dimensions (CDs) were necessary to represent the E-nose data, which accumulated a total variance of 99.86%. Salience tables indicate a relation in CD1 between sensors S1, S3, S5, S6, and S8. Sensors S7 and S9 have more influence on CD2. The scores from the first four CDs were used as input to construct LDA classifiers. All models reached a sensitivity and specificity of 100% in the leave-one-out cross-validation. Thus, the proposed approach was able to classify correctly the aromatic pattern of different commercial instant coffees.


Multiblock analysis Chemometrics Smellprint Quality control Coffee 


Funding Information

This work is financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Compliance with Ethical Standards

Conflict of Interest

Gustavo Yasuo Figueiredo Makimori declares that he has no conflict of interest. Evandro Bona declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.


  1. ABICS (2017) Relatório do Café Soluvél do Brasil: Novembro de 2017. São PauloGoogle Scholar
  2. Alamprese C, Casale M, Sinelli N, Lanteri S, Casiraghi E (2013) Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy. LWT Food Sci Technol 53:225–232. Google Scholar
  3. Bishop CM (2006) Pattern recognition and machine learning, 1st edn. Springer, New YorkGoogle Scholar
  4. Bona E, da SRS d SF, Borsato D, Bassoli DG (2012) Self-organizing maps as a chemometric tool for aromatic pattern recognition of soluble coffee. Acta Sci Technol 34:111–119. Google Scholar
  5. Bona E, da Silva RSDSF, Borsato D, Bassoli DG (2011) Optimized neural network for instant coffee classification through an electronic nose. Int J Food Eng 7:1–21. Google Scholar
  6. Bona E, dos Santos Ferreira da Silva RS (2016) Coffee and the electronic nose. In: Méndez MLR (ed) Electronic noses and tongues in food science, 1st edn. Elsevier, New York, pp 31–38Google Scholar
  7. Bona E, Março PH, Valderrama P (2018) Chemometrics applied to food control. In: Holban AM, Grumezescu AM (eds) Handbook of food bioengineering: food control and biosecurity, 1st edn. Elsevier, London, pp 105–133Google Scholar
  8. Brereton RG (2018) Chemometrics: data driven extractionfor science, 2nd edn. Wiley, HobokenGoogle Scholar
  9. Buratti S, Sinelli N, Bertone E, Venturello A, Casiraghi E, Geobaldo F (2015) Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis. J Sci Food Agric 95:2192–2200. Google Scholar
  10. Cariou V, Qannari EM, Rutledge DN, Vigneau E (2018) ComDim: from multiblock data analysis to path modeling. Food Qual Prefer 67:27–34. Google Scholar
  11. Carmel L, Levy S, Lancet D, Harel D (2003) A feature extraction method for chemical sensors in electronic noses. Sensors Actuators B Chem 93:67–76. Google Scholar
  12. Choopun S, Hongsith N, Wongrat E (2012) Metal-oxide nanowires for gas sensors, nanowires - recent advances. Met -Oxide Nanowires Gas Sensors:3–24.
  13. Claeys-Bruno M, Béal A, Rutledge DN, Sergent M (2016) Use of the common components and specific weights analysis to interpret supersaturated designs. Chemom Intell Lab Syst 152:97–106. Google Scholar
  14. Colzi I, Taiti C, Marone E, Magnelli S, Gonnelli C, Mancuso S (2017) Covering the different steps of the coffee processing: can headspace VOC emissions be exploited to successfully distinguish between Arabica and Robusta? Food Chem 237:257–263. Google Scholar
  15. 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 210:62–75. Google Scholar
  16. Diniz PHGD, Pistonesi MF, Alvarez MB, Band BSF, de Araújo MCU (2015) Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA). J Food Compos Anal 39:103–110. Google Scholar
  17. Distante C, Leo M, Siciliano P, Persaud KC (2002) On the study of feature extraction methods for an electronic nose. Sensors Actuators B Chem 87:274–288. Google Scholar
  18. Dong W, Hu R, Long Y, Li H, Zhang Y, Zhu K, 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 272:723–731. Google Scholar
  19. Dong W, Zhao J, Hu R, Dong Y, Tan L (2017) Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics. Food Chem 229:743–751. Google Scholar
  20. Dragonieri S, Brinkman P, Mouw E, Zwinderman AH, Carratú P, Resta O, Sterk PJ, Jonkers RE (2013) An electronic nose discriminates exhaled breath of patients with untreated pulmonary sarcoidosis from controls. Respir Med 107:1073–1078. Google Scholar
  21. El Ghaziri A, Cariou V, Rutledge DN, Qannari EM (2016) Analysis of multiblock datasets using ComDim: overview and extension to the analysis of (K + 1) datasets. J Chemom 30:420–429. Google Scholar
  22. Farah A (2012) Coffee constituents. Coffee Emerg Heal Eff Dis Prev 21–58.
  23. Ferreira MMC (2015) Quimiometria - Conceitos, Métodos e Aplicações. Editora da UNICAMP, Campinas, SPGoogle Scholar
  24. Ferreiro-González M, Barbero GF, Palma M, Ayuso J, Álvarez J, Barroso C (2017) Characterization and differentiation of petroleum-derived products by E-nose fingerprints. Sensors (Switzerland) 17:1–10. Google Scholar
  25. Flambeau KJ, Lee W-J, 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 26:1245–1254. Google Scholar
  26. Fuchs RHB, Ribeiro RP, Bona E, Kitzberger CSG, de Souza C, Matsushita M (2018) Sensory characterization of Nile tilapia croquettes enriched with flaxseed flour using free-choice profiling and common components and specific weights analysis. J Sens Stud 33:e12324. Google Scholar
  27. 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 10:3424–3433. Google Scholar
  28. Hai Z, Wang J (2006) Electronic nose and data analysis for detection of maize oil adulteration in sesame oil. Sensors Actuators B Chem 119:449–455. Google Scholar
  29. Hui G, Jin J, Deng S, Ye X, Zhao M, Wang M, Ye D (2015) Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose. Food Chem 170:484–491. Google Scholar
  30. Kalschne DL, Viegas MC, De Conti AJ et al (2018) Steam pressure treatment of defective Coffea canephora beans improves the volatile profile and sensory acceptance of roasted coffee blends. Food Res Int 105:393–402. Google Scholar
  31. Kamal M, Karoui R (2017) Monitoring of mild heat treatment of camel milk by front-face fluorescence spectroscopy. LWT Food Sci Technol 79:586–593. Google Scholar
  32. Konduru T, Rains G, Li C (2015) A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization. Sensors 15:1252–1273. Google Scholar
  33. Li J, Zheng F, Jiang J, Lin H, Hui G (2015) Chinese bayberry (Myrica rubra Sieb. et Zucc.) quality determination based on an electronic nose and non-linear dynamic model. Anal Methods 7:9928–9939. Google Scholar
  34. Lihuan S, Liu W, Xiaohong Z, Guohua H, Zhidong Z (2017) Fabrication of electronic nose system and exploration on its applications in mango fruit (M. indica cv. Datainong) quality rapid determination. J Food Meas Char 11:1969–1977. Google Scholar
  35. Marquetti I, Link JV, Lemes ALG, Scholz MBS, Valderrama P, Bona E (2016) Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee. Comput Electron Agric 121:313–319. Google Scholar
  36. Martínez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233Google Scholar
  37. Melucci D, Bendini A, Tesini F, Barbieri S, Zappi A, Vichi S, Conte L, Gallina Toschi T (2016) Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics. Food Chem 204:263–273. Google Scholar
  38. Monakhova YB, Hohmann M, Christoph N, Wachter H, Rutledge DN (2016) Improved classification of fused data: synergetic effect of partial least squares discriminant analysis (PLS-DA) and common components and specific weights analysis (CCSWA) combination as applied to tomato profiles (NMR, IR and IRMS). Chemom Intell Lab Syst 156:1–6. Google Scholar
  39. Mumyakmaz B, Karabacak K (2015) An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations. Turk J Electr Eng Comput Sci 23:729–740. Google Scholar
  40. Pearce TC, Schiffman SS, Nagle HT, Gardner JW (2003) Handbook of machine olfaction: electronic nose technology. WILEY-VCH Verlag GmbH & CoGoogle Scholar
  41. Peng Q, Tian R, Chen F, Li B, Gao H (2015) Discrimination of producing area of Chinese Tongshan kaoliang spirit using electronic nose sensing characteristics combined with the chemometrics methods. Food Chem 178:301–305. Google Scholar
  42. Qannari EM, Wakeling I, Courcoux P, MacFie HJ (2000) Defining the underlying sensory dimensions. Food Qual Prefer 11:151–154. Google Scholar
  43. Qannari EM, Wakeling I, MacFie HJH (1995) A hierarchy of models for analysing sensory data. Food Qual Prefer 6:309–314. Google Scholar
  44. Radi RM, Purnomo MH (2016) Study on electronic-nose-based quality monitoring system for coffee under roasting. J Syst Comput 25:1650116. Google Scholar
  45. 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 132:43–50. Google Scholar
  46. Rehman A, Member S, Bermak A (2018) Drift-insensitive features for learning artificial olfaction in E-nose system. IEEE Sensors J 18:7173–7182. Google Scholar
  47. Rosa LN, de Figueiredo LC, Bonafé EG, Coqueiro A, Visentainer JV, Março PH, Rutledge DN, Valderrama P (2017) Multi-block data analysis using ComDim for the evaluation of complex samples: characterization of edible oils. Anal Chim Acta 961:42–48. Google Scholar
  48. Souto UTDCP, Barbosa MF, Dantas HV et al (2015) Identification of adulteration in ground roasted coffees using UV–Vis spectroscopy and SPA-LDA. LWT Food Sci Technol 63:1037–1041. Google Scholar
  49. Toci AT, Farah A (2014) Volatile fingerprint of Brazilian defective coffee seeds: corroboration of potential marker compounds and identification of new low quality indicators. Food Chem 153:298–314. Google Scholar
  50. Tormena MML, de Medeiros LT, de Lima PC, Possebon G, Fuchs RHB, Bona E (2017) Application of multi-block analysis and mixture design with process variable for development of chocolate cake containing yacon (Smallanthus sonchifolius) and maca (Lepidium meyenii). J Sci Food Agric 97:3559–3567. Google Scholar
  51. Verma P, Yadava RDS (2015) Polymer selection for SAW sensor array based electronic noses by fuzzy c-means clustering of partition coefficients: model studies on detection of freshness and spoilage of milk and fish. Sensors Actuators B Chem 209:751–769. Google Scholar
  52. Villalón-lópez N, Serrano-contreras JI, Téllez-medina DI, Zepeda LG (2018) An 1H NMR-based metabolomic approach to compare the chemical profiling of retail samples of ground roasted and instant coffees. Food Res Int 106:263–270. Google Scholar
  53. Vorobioff J, Videla E, Boggio N, Salomón OD, Lamagna A, Rinaldi CA (2018) Laser Vaporization e-Nose method for the detection of transmitter of Chagas disease. Sensors Actuators B Chem 257:200–206. Google Scholar
  54. Westad F, Marini F (2015) Validation of chemometric models – a tutorial. Anal Chim Acta 893:14–24. Google Scholar
  55. Yan J, Guo X, Duan S, Jia P, Wang L, Peng C, Zhang S (2015) Electronic nose feature extraction methods: a review. Sensors 15:27804–27831. Google Scholar
  56. Yang N, Liu C, Liu X, Degn TK, Munchow M, Fisk I (2016) Determination of volatile marker compounds of common coffee roast defects. Food Chem 211:206–214. Google Scholar
  57. Ying X, Liu W, Hui G (2015a) Litchi freshness rapid non-destructive evaluating method using electronic nose and non-linear dynamics stochastic resonance model. Bioengineered 6:218–221. Google Scholar
  58. Ying X, Liu W, Hui G, Fu J (2015b) E-nose based rapid prediction of early mouldy grain using probabilistic neural networks. Bioengineered 6:222–226. Google Scholar
  59. Zhang X, Zhou H, Chang L, Lou X, Li J, Hui G, Zhao Z (2018) Study of golden pompano (Trachinotus ovatus) freshness forecasting method by utilising Vis/NIR spectroscopy combined with electronic nose. Int J Food Prop 21:1257–1269. Google Scholar
  60. Zhiyi H, Chenchao H, Jiajia Z, Jian L, Guohua H (2017) Electronic nose system fabrication and application in large yellow croaker (Pseudosciaena crocea) fressness prediction. J Food MeasCharact 11:33–40. Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Post-Graduate Program of Food Technology (PPGTA)Federal University of Technology – Paraná (UTFPR)Campo MourãoBrazil

Personalised recommendations