Commercial Instant Coffee Classification Using an Electronic Nose in Tandem with the ComDim-LDA Approach
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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.
KeywordsMultiblock analysis Chemometrics Smellprint Quality control Coffee
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- ABICS (2017) Relatório do Café Soluvél do Brasil: Novembro de 2017. São PauloGoogle Scholar
- Bishop CM (2006) Pattern recognition and machine learning, 1st edn. Springer, New YorkGoogle Scholar
- 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. https://doi.org/10.4025/actascitechnol.v34i1.10892 Google Scholar
- 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
- 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
- Brereton RG (2018) Chemometrics: data driven extractionfor science, 2nd edn. Wiley, HobokenGoogle Scholar
- 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. https://doi.org/10.1002/jsfa.6933 Google Scholar
- Choopun S, Hongsith N, Wongrat E (2012) Metal-oxide nanowires for gas sensors, nanowires - recent advances. Met -Oxide Nanowires Gas Sensors:3–24. https://doi.org/10.5772/54385
- 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. https://doi.org/10.1016/j.foodchem.2017.05.071 Google Scholar
- 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. https://doi.org/10.1016/j.jfca.2014.11.012 Google Scholar
- 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. https://doi.org/10.1016/j.foodchem.2018.08.068 Google Scholar
- Farah A (2012) Coffee constituents. Coffee Emerg Heal Eff Dis Prev 21–58. https://doi.org/10.1002/9781119949893.ch2
- Ferreira MMC (2015) Quimiometria - Conceitos, Métodos e Aplicações. Editora da UNICAMP, Campinas, SPGoogle Scholar
- 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. https://doi.org/10.1007/s10068-017-0168-1 Google Scholar
- 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. https://doi.org/10.1111/joss.12324 Google Scholar
- 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. https://doi.org/10.1016/j.compag.2015.12.018 Google Scholar
- Martínez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233Google Scholar
- 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. https://doi.org/10.1016/j.foodchem.2016.02.131 Google Scholar
- 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. https://doi.org/10.1016/j.chemolab.2016.05.006 Google Scholar
- Pearce TC, Schiffman SS, Nagle HT, Gardner JW (2003) Handbook of machine olfaction: electronic nose technology. WILEY-VCH Verlag GmbH & CoGoogle Scholar
- 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. https://doi.org/10.1016/j.aca.2017.01.019 Google Scholar
- 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. https://doi.org/10.1002/jsfa.8211 Google Scholar
- 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. https://doi.org/10.1016/j.snb.2014.11.149 Google Scholar