Technological innovation tools for specialty coffee from quality analysis instrumentation: A systematic review
Article Sidebar
Main Article Content
Abstract
In the coffee value addition chain, the ideal process should be like a continuous and predictable process. This is not possible for the coffee agribusiness because each harvesting, processing and storage process is different and influences the final product. In addition, the automation of the roasting process is limited because sensory perceptions such as smell, taste and coffee color are not yet fully adjustable from different instrumental sources. Further research on this topic is needed. This article presents the results of the systematic mapping study aimed at evaluating the tools of the instrumented systems for the analysis of the quality of specialty and origin coffee. 172 documents were consolidated to study the advances in innovation and technological change for the coffee sector, and the results indicate that through the technique of fusion of data from the E-Nose and E-Tongue, and E-Eye systems, coffees were identified and classified by their roasting color, aroma and flavor, and its organoleptic attributes were quantified, offering a competitiveness tool as a complement to a traditional quality assessment process carried out by a panel of expert tasters, which can be supported by the analysis of multivariate data, the treatment and processing of data and visual, olfactory and signals and of flavor, to represent a diagnosis of the quality of roasted coffee as a tool of innovation for coffee communities. A scheme of integration of the technologies instrumented for the evaluation of the quality of coffee as an element of technology transfer of industry 4.0 in the coffee agro-chain and its role in competitiveness and rural coffee development was proposed
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All the texts published in this magazine are distributed under a Creative Commons License «Attribution-Non-Commercial-Share the same»
References
Afoakwa, E. O. (2016). Chocolate Science and Technology: Second Edition. In Chocolate Science and Technology: Second Edition. wiley. https://doi.org/10.1002/9781118913758
Alessio, P., Leopoldo Constantino, C. J., Daikuzono, C. M., Riul Jr, A., & de Oliveira Jr, O. N. (2016). Chapter 17 - Analysis of Coffees Using Electronic Tongues A2 - Méndez, María Luz Rodríguez BT - Electronic Noses and Tongues in Food Science (pp. 171–177). Academic Press. https://doi.org/http://dx.doi.org/10.1016/B978-0-12-800243-8.00017-2
Amorim, A. C. L., Hovell, A. M. C., Pinto, A. C., Eberlin, M. N., Arruda, N. P., Pereira, E. J., Bizzo, H. R., Catharino, R. R., Morais Filho, Z. B., & Rezende, C. M. (2009). Green and roasted Arabica coffees differentiated by ripeness, process and cup quality via electrospray ionization mass spectrometry fingerprinting. Journal of the Brazilian Chemical Society, 20(2), 313–321.
Apetrei, C., Apetrei, I. M., Villanueva, S., de Saja, J. A., Gutierrez-Rosales, F., & Rodriguez-Mendez, M. L. (2010). Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Analytica Chimica Acta, 663(1), 91–97. https://doi.org/10.1016/j.aca.2010.01.034
Arana, V. A., Medina, J., Alarcon, R., Moreno, E., Heintz, L., Schäfer, H., & Wist, J. (2015). Coffee’s country of origin determined by NMR: The Colombian case. Food Chemistry, 175, 500–506. https://doi.org/10.1016/j.foodchem.2014.11.160
Askim, J. R., & Suslick, K. S. (2017). Colorimetric and Fluorometric Sensor Arrays for Molecular Recognition BT - Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier. https://doi.org/http://dx.doi.org/10.1016/B978-0-12-409547-2.12616-2
Bakker, J., & Clarke, R. J. (2011). Wine Flavour Chemistry, Second Edition. In Wine Flavour Chemistry, Second Edition. Wiley-Blackwell. https://doi.org/10.1002/9781444346022
Banerjee(Roy), R., Chattopadhyay, P., Tudu, B., Bhattacharyya, N., Bandyopadhyay, R., Banerjee, R., Chattopadhyay, P., Tudu, B., Bhattacharyya, N., & Bandyopadhyay, R. (2014). Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach. Journal of Food Engineering, 142, 87–93. https://doi.org/10.1016/j.jfoodeng.2014.06.004
Banerjee, R., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2016). A review on combined odor and taste sensor systems. Journal of Food Engineering, 190, 10–21. https://doi.org/10.1016/j.jfoodeng.2016.06.001
Barbin, D. F., Felicio, A. L. de S. M., Sun, D.-W., Nixdorf, S. L., Hirooka, E. Y., de Souza Madureira Felicio, A. L., Sun, D.-W., Nixdorf, S. L., Hirooka, E. Y., Felicio, A. L. de S. M., Sun, D.-W., Nixdorf, S. L., & Hirooka, E. Y. (2014). Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Research International, 61, 23–32. https://doi.org/https://doi.org/10.1016/j.foodres.2014.01.005
Berk, Z. (2009). Food Process Engineering and Technology. In Food Process Engineering and Technology. Elsevier Inc. https://doi.org/10.1016/B978-0-12-373660-4.X0001-4
Berna, A. (2010). Metal oxide sensors for electronic noses and their application to food analysis. Sensors, 10(4), 3882–3910. https://doi.org/10.3390/s100403882
Blumberg, S., Frank, O., & Hofmann, T. (2010). Quantitative studies on the influence of the bean roasting parameters and hot water percolation on the concentrations of bitter compounds in coffee brew. Journal of Agricultural and Food Chemistry, 58(6), 3720–3728. https://doi.org/10.1021/jf9044606
Bolivar, J. T. C., Pérez, W. R., Salazar, J. C. S., Espinosa, C. M. O., Cano, G. A. V, Tatiana, J., Bolivar, C., Adolfo, G., Cano, V., Bolivar, J. T. C., Pérez, W. R., Salazar, J. C. S., Espinosa, C. M. O., & Cano, G. A. V. (2017). Minority compounds and sensory analysis evaluation of Coffea arabica var. caturra cultivated in three different altitudinal ranges. Acta Agronomica, 66(2), 221–227.
Bona, E., Da Silva, R. S. D. S. F., & dos Santos Ferreira da Silva, R. S. (2016). Coffee and the Electronic Nose. In M. L. R. Méndez (Ed.), Electronic Noses and Tongues in Food Science (pp. 31–38). Academic Press. https://doi.org/10.1016/B978-0-12-800243-8.00004-4
Bona, E., da Silva, R. S. dos S. F., Borsato, D., & Bassoli, D. G. (2011). Optimized Neural Network for Instant Coffee Classification through an Electronic Nose. International Journal of Food Engineering, 7(6). https://doi.org/10.2202/1556-3758.2002
Borém, F. M., Ribeiro, F. C., Figueiredo, L. P., Giomo, G. S., Fortunato, V. A., & Isquierdo, E. P. (2013). Evaluation of the sensory and color quality of coffee beans stored in hermetic packaging. Journal of Stored Products Research, 52, 1–6. https://doi.org/10.1016/j.jspr.2012.08.004
Borr??s, E., Ferr??, J., Boqu??, R., Mestres, M., Ace??a, L., & Busto, O. (2015). Data fusion methodologies for food and beverage authentication and quality assessment - A review. Analytica Chimica Acta, 891, 1–14. https://doi.org/10.1016/j.aca.2015.04.042
Borràs, E., Ferré, J., Boqué, R., Mestres, M., Aceña, L., Busto, O., Borr??s, E., Ferr??, J., Boqu??, R., Mestres, M., Ace??a, L., & Busto, O. (2015). Data fusion methodologies for food and beverage authentication and quality assessment - A review. Analytica Chimica Acta, 891, 1–14. https://doi.org/10.1016/j.aca.2015.04.042
Bosselmann, A. S., Dons, K., Oberthur, T., Olsen, C. S., Ræbild, A., & Usma, H. (2009). The influence of shade trees on coffee quality in small holder coffee agroforestry systems in Southern Colombia. Agriculture, Ecosystems and Environment, 129(1–3), 253–260. https://doi.org/10.1016/j.agee.2008.09.004
Bressanello, D., Liberto, E., Cordero, C., Rubiolo, P., Pellegrino, G., Ruosi, M. R., & Bicchi, C. (2017). Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC–MS to describe the sensory properties in cup. Food Chemistry, 214, 218–226. https://doi.org/http://dx.doi.org/10.1016/j.foodchem.2016.07.088
Brudzewski, K., Osowski, S., & Dwulit, A. (2012). Recognition of coffee using differential electronic nose. IEEE Transactions on Instrumentation and Measurement, 61(6), 1803–1810. https://doi.org/10.1109/TIM.2012.2184011
Buratti, S, Benedetti, S., & Giovanelli, G. (2017). Application of electronic senses to characterize espresso coffees brewed with different thermal profiles. European Food Research and Technology, 243(3), 511–520. https://doi.org/10.1007/s00217-016-2769-y
Buratti, Susanna, Malegori, C., Benedetti, S., Oliveri, P., & Giovanelli, G. (2018). E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach. Talanta, 182, 131–141. https://doi.org/10.1016/j.talanta.2018.01.096
Buratti, Susanna, 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. Journal of the Science of Food and Agriculture, 95(11), 2192–2200. https://doi.org/10.1002/jsfa.6933
Cagliani, L. R., Pellegrino, G., Giugno, G., & Consonni, R. (2013). Quantification of Coffea arabica and Coffea canephora var. robusta in roasted and ground coffee blends. Talanta, 106, 169–173. https://doi.org/10.1016/j.talanta.2012.12.003
Caligiani, A., Cirlini, M., Palla, G., & Arlorio, M. (2008). Chiral markers: A useful tool for the quality control of cocoa beans and related products. Agro Food Industry Hi-Tech, 19(3 SUPPL.), 20–21.
Casas, M. I., Vaughan, M. J., Bonello, P., McSpadden Gardener, B., Grotewold, E., & Alonso, A. P. (2017). Identification of biochemical features of defective Coffea arabica L. beans. Food Research International, 95, 59–67. https://doi.org/10.1016/j.foodres.2017.02.015
Castro, J. B., Ramanathan, A., & Chennubhotla, C. S. (2013). Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization. PLoS ONE, 8(9). https://doi.org/10.1371/journal.pone.0073289
Ceto, X., Gutierrez-Capitan, M., Calvo, D., & Valle, M. Del. (2013). Beer classification by means of a potentiometric electronic tongue. Food Chemistry, 141(3), 2533–2540. https://doi.org/10.1016/j.foodchem.2013.05.091
Chambers IV, E., Sanchez, K., Phan, U. X. T., Miller, R., Civille, G. V, & Di Donfrancesco, B. (2016). Development of a “living” lexicon for descriptive sensory analysis of brewed coffee. Journal of Sensory Studies, 31(6), 465–480. https://doi.org/10.1111/joss.12237
Charles, M., Romano, A., Yener, S., Barnabà, M., Navarini, L., Märk, T. D., Biasoli, F., & Gasperi, F. (2015). Understanding flavour perception of espresso coffee by the combination of a dynamic sensory method and in-vivo nosespace analysis. Food Research International, 69, 9–20. https://doi.org/10.1016/j.foodres.2014.11.036
Chatterjee, D., Bhattacharjee, P., & Bhattacharyya, N. (2014). Development of methodology for assessment of shelf-life of fried potato wedges using electronic noses: Sensor screening by fuzzy logic analysis. Journal of Food Engineering, 133, 23–29. https://doi.org/10.1016/j.jfoodeng.2014.02.009
Chen, Q., Zhang, D., Pan, W., Ouyang, Q., Li, H., Urmila, K., & Zhao, J. (2015). Recent developments of green analytical techniques in analysis of tea’s quality and nutrition. Trends in Food Science and Technology, 43(1), 63–82. https://doi.org/10.1016/j.tifs.2015.01.009
Cheng, B., Furtado, A., Smyth, H. E., & Henry, R. J. (2016). Influence of genotype and environment on coffee quality. In Trends in Food Science and Technology. https://doi.org/10.1016/j.tifs.2016.09.003
Cho, J.-S., Bae, H.-J., Cho, B.-K., & Moon, K.-D. (2017). Qualitative properties of roasting defect beans and development of its classification methods by hyperspectral imaging technology. Food Chemistry, 220, 505–509. https://doi.org/https://doi.org/10.1016/j.foodchem.2016.09.189
Cole, M., Covington, J. A., & Gardner, J. W. (2011). Combined electronic nose and tongue for a flavour sensing system. Sensors and Actuators, B: Chemical, 156(2), 832–839. https://doi.org/10.1016/j.snb.2011.02.049
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 Chemistry, 237, 257–263. https://doi.org/https://doi.org/10.1016/j.foodchem.2017.05.071
Consonni, R., Cagliani, L. R., & Cogliati, C. (2012). NMR based geographical characterization of roasted coffee. Talanta, 88, 420–426. https://doi.org/https://doi.org/10.1016/j.talanta.2011.11.010
Correa, E. C., Barreiro, P., Hills, B., Bongaers, E., Jimenez, T., Melado, A., Diezma, B., Diaz-Barcos, V., Meneses, B., & Oteros, R. (2011). Prospective of innovative technologies for quality supervision and classification of roasted coffee beans. 6th International CIGR Technical Symposium - Towards a Sustainable Food Chain: Food Process, Bioprocessing and Food Quality Management, Valorial l’Aliment de Demain; CNRS; PONAN-Pole A.
Costa, A. M. S., Sobral, M. M. C., Delgadillo, I., & Rudnitskaya, A. (2014). Electronic tongue as a rapid tool for the assessment of coffee flavour and chemical composition. IEEE SENSORS 2014 Proceedings, 2014-Decem(December), 2126–2129. https://doi.org/10.1109/ICSENS.2014.6985458
Costa, D. C., Costa, H. S., Albuquerque, T. G., Ramos, F., Castilho, M. C., & Sanches-Silva, A. (2015). Advances in phenolic compounds analysis of aromatic plants and their potential applications. Trends in Food Science & Technology, 45(2), 336–354. https://doi.org/https://doi.org/10.1016/j.tifs.2015.06.009
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. Chemometrics and Intelligent Laboratory Systems, 144, 63–70. https://doi.org/10.1016/j.chemolab.2015.03.010
De Oliveira, E. M., Leme, D. S., Barbosa, B. H. G., Rodarte, M. P., & Alvarenga Pereira, R. G. F. (2016). A computer vision system for coffee beans classification based on computational intelligence techniques. Journal of Food Engineering, 171, 22–27. https://doi.org/10.1016/j.jfoodeng.2015.10.009
De Toledo, P.R.A.B., De Melo, M. M. R., Pezza, H. R., Toci, A. T., Pezza, L., & Silva, C. M. (2016). Control of coffee samples quality: Geographic and roasting factors. In Coffee: Production, Consumption and Health Benefits (pp. 51–65).
de Toledo, Paulo R A B, de Melo, M. M. R., Pezza, H. R., Toci, A. T., Pezza, L., & Silva, C. M. (2017). Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products. Food Control, 73, 164–174. https://doi.org/10.1016/j.foodcont.2016.08.001
Di Rosa, A. R., 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. Journal of Food Engineering, 210, 62–75. https://doi.org/10.1016/j.jfoodeng.2017.04.024
Diezma, T., & Cristina, E. (2011). UN BUEN CAFÉ: UNA SIMBIOSIS DE COLOR Y SABOR. In Actas del IV Congreso de Estudiantes Universitarios de Ciencia, Tecnología e Ingeniería Agronómica.
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 Chemistry. https://doi.org/10.1016/j.foodchem.2017.02.149
Dunkel, A., Steinhaus, M., Kotthoff, M., Nowak, B., Krautwurst, D., Schieberle, P., & Hofmann, T. (2014). Nature’s chemical signatures in human olfaction: A foodborne perspective for future biotechnology. Angewandte Chemie - International Edition, 53(28), 7124–7143. https://doi.org/10.1002/anie.201309508
Dutta, A., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2011). Black tea quality evaluation using electronic nose: An Artificial Bee Colony approach. 2011 IEEE Recent Advances in Intelligent Computational Systems, 143–146. https://doi.org/10.1109/RAICS.2011.6069290
Fabbri, A., Cevoli, C., Alessandrini, L., & Romani, S. (2011). Numerical modeling of heat and mass transfer during coffee roasting process. Journal of Food Engineering, 105(2), 264–269. https://doi.org/10.1016/j.jfoodeng.2011.02.030
Fadai, N. T., Please, C. P., & Van Gorder, R. A. (2019). Modelling structural deformations in a roasting coffee bean. International Journal of Non-Linear Mechanics, 110(October 2018), 123–130. https://doi.org/10.1016/j.ijnonlinmec.2018.12.006
Federación Nacional de Cafeteros. (n.d.). Denominación de Origen Café de Cauca | Café de Colombia.
Feria-Morales, A. M. (2002). Examining the case of green coffee to illustrate the limitations of grading systems/expert tasters in sensory evaluation for quality control. Food Quality and Preference, 13(6), 355–367. https://doi.org/10.1016/S0950-3293(02)00028-9
Ferreira, H. A., Liska, G. R., Cirillo, M. A., Borem, F. M., Ribeiro, D. E., Cortez, R. M., & Guiraldeli, C. H. C. (2016). Selecting A Probabilistic Model Applied To The Sensory Analysis Of Specialty Coffees Performed With Consumer. In IEEE Latin America Transactions (Vol. 14, Issue 3, pp. 1507–1512). https://doi.org/10.1109/TLA.2016.7459642
Fitzgerald, J. E., Bui, E. T. H., Simon, N. M., & Fenniri, H. (2017). Artificial Nose Technology: Status and Prospects in Diagnostics. Trends in Biotechnology, 35(1), 33–42. https://doi.org/10.1016/j.tibtech.2016.08.005
Franca, A. S., & Oliveira, L. S. (2011). Potential uses of fourier transform infrared spectroscopy (FTIR) in food processing and engineering. In Food Engineering. Nova Science Publishers, Inc.
Gabriel-Guzmán, M., Rivera, V. M., Cocotle-Ronzón, Y., García-Díaz, S., & Hernandez-Martinez, E. (2017). Fractality in coffee bean surface for roasting process. Chaos, Solitons & Fractals, 99, 79–84. https://doi.org/https://doi.org/10.1016/j.chaos.2017.03.056
Ghasemi-Varnamkhasti, M., Mohtasebi, S. S., & Siadat, M. (2010). Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements. Journal of Food Engineering, 100(3), 377–387. https://doi.org/10.1016/j.jfoodeng.2010.04.032
Gloess, A. N., Vietri, A., Wieland, F., Smrke, S., Sch??nb??chler, B., L??pez, J. A. S., Petrozzi, S., Bongers, S., Koziorowski, T., & Yeretzian, C. (2014). Evidence of different flavour formation dynamics by roasting coffee from different origins: On-line analysis with PTR-ToF-MS. International Journal of Mass Spectrometry, 365–366, 324–337. https://doi.org/10.1016/j.ijms.2014.02.010
Gordillo-Delgado, F., Bedoya, A., & Marín, E. (2017). Study of the Pigments in Colombian Powdered Coffee Using Photoacoustic Spectroscopy. International Journal of Thermophysics, 38(1). https://doi.org/10.1007/s10765-016-2144-z
Grosch, W. (2007). Quality Control: Analytical Methods: Key Odorants of Food Identified by Aroma Analysis. In Flavourings: Production, Composition, Applications, Regulations, Second Edition (pp. 704–743). https://doi.org/10.1002/9783527611454.ch6b
Guevara Barreto, R. A., & Castaño Castrillón, J. J. (2005). Caracterización granulométrica del café Colombiano tostado y molido. Cenicafé, 56(1), 13. http://biblioteca.cenicafe.org/bitstream/10778/122/1/arc056(01)005-018.pdf
Gupta, H., Sharma, A., Kumar, S., & Roy, S. K. (2010). E-tongue: a tool for taste evaluation. Recent Patents on Drug Delivery & Formulation, 4(1), 82–89. https://doi.org/10.2174/187221110789957309
Gutiérrez, J., & Horrillo, M. C. (2014). Advances in artificial olfaction: Sensors and applications. Talanta, 124, 95–105. https://doi.org/10.1016/j.talanta.2014.02.016
Gutiérrez, J. M., Haddi, Z., Amari, A., Bouchikhi, B., Mimendia, A., Cetó, X., & Del Valle, M. (2013). Hybrid electronic tongue based on multisensor data fusion for discrimination of beers. Sensors and Actuators, B: Chemical, 177, 989–996. https://doi.org/10.1016/j.snb.2012.11.110
Gutiérrez, N., & Barrera, O. (2015). Selección y entrenamiento de un panel en análisis sensorial de café Coffea arabica L. Cienc. Agr. Julio -Diciembre, 32(2), 77–87. https://doi.org/10.22267/rcia.153202.15
Haddi, Z., Mabrouk, S., Bougrini, M., Tahri, K., Sghaier, K., Barhoumi, H., El Bari, N., Maaref, A., Jaffrezic-Renault, N., & Bouchikhi, B. (2014). E-Nose and e-Tongue combination for improved recognition of fruit juice samples. Food Chemistry, 150, 246–253. https://doi.org/10.1016/j.foodchem.2013.10.105
Hall, D. L. D. L., Member, S., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23. https://doi.org/10.1109/5.554205
Hall, D. L., & Llinas, J. (2008). Handbook of Multisensor Data Fusion. In Handbook of Multisensor Data Fusion. https://doi.org/10.1201/9781420053098
Hauptmann, P., Borngraeber, R., Schroeder, J., & Auge, J. (2000). Application of novel sensor electronics for quartz resonators innartificial tongue. Proceedings of the 2000 IEEE/EIA International Frequency Control Symposium and Exhibition (Cat. No.00CH37052), 100–105. https://doi.org/10.1109/FREQ.2000.887337
He?imovi?, I., Belš?ak-Cvitanovi?, A., Horži?, D., & Komes, D. (2011). Comparative study of polyphenols and caffeine in different coffee varieties affected by the degree of roasting. Food Chemistry, 129(3), 991–1000. https://doi.org/10.1016/j.foodchem.2011.05.059
Hong, X., & Wang, J. (2014). Detection of adulteration in cherry tomato juices based on electronic nose and tongue: Comparison of different data fusion approaches. Journal of Food Engineering, 126, 89–97. https://doi.org/10.1016/j.jfoodeng.2013.11.008
Ishiwaki, T. (2013). Application of taste sensor to blending of coffee. In Biochemical Sensors: Mimicking Gustatory and Olfactory Senses (pp. 83–90). https://doi.org/10.4032/9789814303422
Jackson, R. (2008). Wine Science. In Wine Science. Academic Press. https://doi.org/10.1016/B978-0-12-373646-8.X5001-X
Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016a). A portable electronic nose as an expert system for aroma-based classification of saffron. Chemometrics and Intelligent Laboratory Systems, 156, 148–156. https://doi.org/10.1016/j.chemolab.2016.05.013
Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016b). Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review. In Journal of Applied Research on Medicinal and Aromatic Plants (Vol. 3, Issue 1). https://doi.org/10.1016/j.jarmap.2015.12.002
Kiani, S., Minaei, S., & Ghasemi-Varnamkhasti, M. (2016c). Fusion of artificial senses as a robust approach to food quality assessment. In Journal of Food Engineering (Vol. 171). https://doi.org/10.1016/j.jfoodeng.2015.10.007
Kodani, Y., Miyakawa, T., Komatsu, T., & Tanokura, M. (2017). NMR-based metabolomics for simultaneously evaluating multiple determinants of primary beef quality in Japanese Black cattle. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-01272-8
Le Quéré, J.-L. (2010). Advanced Analytic Methodology. In Handbook of Fruit and Vegetable Flavors (pp. 177–193). https://doi.org/10.1002/9780470622834.ch11
Lee, L. W., Cheong, M. W., Curran, P., Yu, B., & Liu, S. Q. (2015). Coffee fermentation and flavor - An intricate and delicate relationship. Food Chemistry, 185, 182–191. https://doi.org/10.1016/j.foodchem.2015.03.124
Leme, D. S., da Silva, S. A., Barbosa, B. H. G., Borém, F. M., & Pereira, R. G. F. A. (2019). Recognition of coffee roasting degree using a computer vision system. Computers and Electronics in Agriculture, 156(November 2018), 312–317. https://doi.org/10.1016/j.compag.2018.11.029
López-Darias, J., Anderson, J. L., Pino, V., & Afonso, A. M. (2011). Developing qualitative extraction profiles of coffee aromas utilizing polymeric ionic liquid sorbent coatings in headspace solid-phase microextraction gas chromatography-mass spectrometry. Analytical and Bioanalytical Chemistry, 401(9), 2965–2976. https://doi.org/10.1007/s00216-011-5394-4
Luca, S. De, Filippis, M. De, Bucci, R., Magrì, A. D. A. L., Magrì, A. D. A. L., Marini, F., De Luca, S., De Filippis, M., Bucci, R., Magrì, A. D. A. L., Magrì, A. D. A. L., Marini, F., Luca, S. De, Filippis, M. De, Bucci, R., Magrì, A. D. A. L., Magrì, A. D. A. L., Marini, F., De Luca, S., … Marini, F. (2016). Characterization of the effects of different roasting conditions on coffee samples of different geographical origins by HPLC-DAD, NIR and chemometrics. Microchemical Journal, 129, 348–361. https://doi.org/https://doi.org/10.1016/j.microc.2016.07.021
Lvova, L., Pudi, R., Galloni, P., Lippolis, V., Di Natale, C., Lundström, I., & Paolesse, R. (2015). Multi-transduction sensing films for Electronic Tongue applications. Sensors and Actuators, B: Chemical, 207(PB). https://doi.org/10.1016/j.snb.2014.10.086
Ma, J., Qu, J.-H., & Sun, D.-W. (2017). Developing hyperspectral prediction model for investigating dehydrating and rehydrating mass changes of vacuum freeze dried grass carp fillets. Food and Bioproducts Processing, 104, 66–76. https://doi.org/10.1016/j.fbp.2017.04.007
Magdalena, S., Wisniewska, P., Dymerski, T., Namiesnik, J., & Wardencki, W. (2014). Food Analysis Using Artificial Senses ?. Journal of Agricultural and Food Chemistry, 62, 1423–1448. https://doi.org/DOI: 10.1021/jf403215
McEwan, M. J. (2015). Direct analysis mass spectrometry. In Ion/Molecule Attachment Reactions: Mass Spectrometry. Springer US. https://doi.org/10.1007/978-1-4899-7588-1_8
Mendez, M. L. R. (2016). Electronic noses and tongues in food science.
Mestdagh, F., Davidek, T., Chaumonteuil, M., Folmer, B., & Blank, I. (2014). The kinetics of coffee aroma extraction. Food Research International, 63, 271–274. https://doi.org/10.1016/j.foodres.2014.03.011
Monica Lee, K.-Y., Paterson, A., Piggott, J. R., & Richardson, G. D. (2001). Origins of flavour in whiskies and a revised flavour wheel: A review. Journal of the Institute of Brewing, 107(5), 287–313.
Moreira, R. F. A., Trugo, L. C., De Maria, C. A. B., Matos, A. G. B., Santos, S. M., & Leite, J. M. C. (2001). Discrimination of Brazilian arabica green coffee samples by chlorogenic acid composition. Archivos Latinoamericanos de Nutricion, 51(1), 95–99.
Moroney, K. M., Lee, W. T., O?Brien, S. B. G., Suijver, F., Marra, J., Brien, S. B. G. O., Suijver, F., & Marra, J. (2015). Modelling of coffee extraction during brewing using multiscale methods: An experimentally validated model. Chemical Engineering Science, 137, 216–234. https://doi.org/https://doi.org/10.1016/j.ces.2015.06.003
Nansen, C., Singh, K., Mian, A., Allison, B. J., & Simmons, C. W. (2016). Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes. Journal of Food Engineering, 190, 34–39. https://doi.org/10.1016/j.jfoodeng.2016.06.010
NEUHAUS NEOTEC. (2017). Industrie 4.0 - Smart Coffee Factory - Die Zukunft des Kaffeeröstens - NEUHAUS NEOTEC. https://www.neuhaus-neotec.de/en/neuhaus-neotec-coffee/products/innovations-tv/innovations-tv/detail//show/industry-40-smart-coffee-factory-the-future-of-coffee-roasting/
Nolvachai, Y., Kulsing, C., & Marriott, P. J. (2017). Multidimensional gas chromatography in food analysis. TrAC Trends in Analytical Chemistry. https://doi.org/https://doi.org/10.1016/j.trac.2017.05.001
Oblitas Cruz, J. F., & Castro Silupu, W. (2014). Computer Vision System for the Optimization of the Color Generated by the Coffee Roasting Process According to Time, Temperature and Mesh Size. Ingenieria y Universidad, 18(2), 355. https://doi.org/10.11144/Javeriana.IYU18-2.cvso
Okamura, M., Soga, M., Yamada, Y., Kobata, K., & Kaneda, D. (2021). Development and evaluation of roasting degree prediction model of coffee beans by machine learning. Procedia Computer Science, 192, 4602–4608. https://doi.org/10.1016/j.procs.2021.09.238
Oliveira, L. S., & Franca, A. S. (2011). Applications of near infrared spectroscopy (NIRS) in food quality evaluation. In Food Quality: Control, Analysis and Consumer Concerns. Nova Science Publishers, Inc.
Oliveri, P., Casolino, M. C., & Forina, M. (2010). Chemometric Brains for Artificial Tongues. In Advances in Food and Nutrition Research (Vol. 61, Issue C, pp. 57–117). https://doi.org/10.1016/B978-0-12-374468-5.00002-7
Omatu, S., Ikeda, Y., & Yano, M. (2015). Odor measurement and intelligent classification. 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. https://doi.org/10.1109/ASCC.2015.7244578
Omatu, S., & Yano, M. (2016). E-nose system by using neural networks. Neurocomputing, 172, 394–398. https://doi.org/10.1016/j.neucom.2015.03.101
Park, T. H. (2014). Bioelectronic Nose. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). https://doi.org/10.1007/978-94-017-8613-3
Páscoa, R. N. M. J., Sarraguça, M. C., Magalhães, L. M., Santos, J. R., Rangel, A. O. S. S., & Lopes, J. A. (2014). Use of Near-Infrared Spectroscopy for Coffee Beans Quality Assessment. In Coffee in Health and Disease Prevention. Elsevier Inc. https://doi.org/10.1016/B978-0-12-409517-5.00103-0
Patel, H. K., Austin, R. H., Barber, J., & Patel, H. K. (2014). The Electronic Nose: Artificial Olfaction Technology. In Biological and Medical Physics, Biomedical Engineering. https://doi.org/10.1007/978-81-322-1548-6
Pearce, T. C., Schiffman, S. S., Nagle, H. T., & Gardner, J. W. (2016). Handbook of Machine Olfaction. In Advanced Composite Materials for Aerospace Engineering. https://doi.org/10.1016/B978-0-08-100037-3.09001-5
Peponi, L., Arrieta, M. P., Mujica-Garcia, A., & López, D. (2017). 6 - Smart Polymers A2 - Jasso-Gastinel, Carlos F. (J. M. B. T.-M. of P. P. Kenny (ed.); pp. 131–154). William Andrew Publishing. https://doi.org/http://dx.doi.org/10.1016/B978-0-323-44353-1.00006-3
Peris, M., & Escuder-Gilabert, L. (2013). On-line monitoring of food fermentation processes using electronic noses and electronic tongues: A review. Analytica Chimica Acta, 804, 29–36. https://doi.org/10.1016/j.aca.2013.09.048
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/j.infsof.2015.03.007
Piccino, S., Boulanger, R., Descroix, F., & Sing, A. S. C. (2014). Aromatic composition and potent odorants of the “specialty coffee” brew “Bourbon Pointu” correlated to its three trade classifications. Food Research International. https://doi.org/10.1016/j.foodres.2013.07.034
Pigani, L., Vasile Simone, G., Foca, G., Ulrici, A., Masino, F., Cubillana-Aguilera, L., Calvini, R., & Seeber, R. (2018). Prediction of parameters related to grape ripening by multivariate calibration of voltammetric signals acquired by an electronic tongue. Talanta, 178(June 2017), 178–187. https://doi.org/10.1016/j.talanta.2017.09.027
Poisson, L., Blank, I., Dunkel, A., & Hofmann, T. (2017). Chapter 12 - The Chemistry of Roasting—Decoding Flavor Formation A2 - Folmer, Britta BT - The Craft and Science of Coffee (pp. 273–309). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-803520-7.00012-8
Policastro, C. A., Delbem, A. C. B., Mattoso, L. H. C., Minatti, E., Ferreira, E. J., Borato, C. E., & Zanus, M. C. (2007). A Hybrid Case Based Reasoning Approach for Wine Classification. Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 395–400. https://doi.org/10.1109/ISDA.2007.4389640
Portugal-Zambrano, C. E., Gutierrez-Caceres, J. C., Ramirez-Ticona, J., & Beltran-Castanon, C. A. (2017). Computer vision grading system for physical quality evaluation of green coffee beans. Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016. https://doi.org/10.1109/CLEI.2016.7833383
Puerta, G. (2011). Composición química de una taza de café. Avances En Química, 414, 1–12.
Puerta, Gloria. (2009). Los catadores de café. Avances Tecnicos Cenicafé, 381, 12.
Puerta Q, G., González R, F., Correa P, A., Álvarez L, I., Ardila C, J., Girón O, O., Ramírez Q, C., Baute B, J., Sánchez A, P., Santamaría B, M., & Montoya, D. (2016). Diagnóstico de la calidad del café según altitud suelos y beneficio en varias regiones de Colombia. Cenicafé, 67(2), 15–51.
Puerta Quintero, G. I. (2016). Calidad física del café de varias regiones de Colombia según altitud, suelos y buenas prácticas de beneficio. Cenicafé, 67(1), 7–40.
Radi, Rivai, M., & Purnomo, M. H. (2016). Study on electronic-nose-based quality monitoring system for coffee under roasting. Journal of Circuits, Systems and Computers, 25(10). http://dx.doi.org/10.1142/S0218126616501164
Rattanawarinchai, P., Krongkrachang, P., Chodjarusawad, T., & Phromyothin, D. (2017). Electrochemical sensor: Preparation technique based on electronic tongue in fragrance. Materials Today: Proceedings, 4(5), 6410–6414. https://doi.org/10.1016/j.matpr.2017.06.146
Ribeiro, J. S., Augusto, F., Salva, T. J. G., & Ferreira, M. M. C. (2012). Prediction models for Arabica coffee beverage quality based on aroma analyses and chemometrics. Talanta, 101, 253–260. https://doi.org/https://doi.org/10.1016/j.talanta.2012.09.022
Rodrigues, C. I. I., Hanson, C. M., & Nogueira, J. M. F. (2012). Coffees and industrial blends aroma profile discrimination according to the chromatic value . Coffee Science, 7(2), 167–176.
Rodríguez, J., Durán, C., & Reyes, A. (2010). Electronic nose for quality control of Colombian coffee through the detection of defects in “Cup Tests”. Sensors (Basel, Switzerland), 10(1), 36–46. https://doi.org/10.3390/s100100036
Romani, S., Cevoli, C., Fabbri, A., Alessandrini, L., & Dalla Rosa, M. (2012). Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control. Journal of Food Science, 77(9), 1–6. https://doi.org/10.1111/j.1750-3841.2012.02851.x
Rosa, J. S. da, Freitas-Silva, O., Rouws, J. R. C., Moreira, I. G. da S., Novaes, F. J. M., Azevedo, D. de A., Schwab, N., Godoy, R. L. de O., Eberlin, M. N., & Rezende, C. M. de. (2016). Mass spectrometry screening of Arabica coffee roasting: A non-target and non-volatile approach by EASI-MS and ESI-MS. Food Research International, 89, 967–975. https://doi.org/10.1016/j.foodres.2016.03.021
Ruiz-Altisent, M., Ruiz-Garcia, L., Moreda, G. P., Lu, R., Hernandez-Sanchez, N., Correa, E. C., Diezma, B., Nicolaï, B., & García-Ramos, J. (2010). Sensors for product characterization and quality of specialty crops-A review. In Computers and Electronics in Agriculture (Vol. 74, Issue 2, pp. 176–194). https://doi.org/10.1016/j.compag.2010.07.002
Sáenz-Navajas, M.-P., Avizcuri, J. M., Echávarri, J. F., Ferreira, V., Fernández-Zurbano, P., & Valentin, D. (2016). Understanding quality judgements of red wines by experts: Effect of evaluation condition. Food Quality and Preference, 48, Part A, 216–227. https://doi.org/https://doi.org/10.1016/j.foodqual.2015.10.001
Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., & Siadat, M. (2014). Application of an electronic nose system coupled with artificial neural network for classification of banana samples during shelf-life process. Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014, 753–757. https://doi.org/10.1109/CoDIT.2014.6996991
Santos, A. F. L. O. M., & Ribeiro Da Silva, M. A. V. (2011). A combined experimental and computational thermodynamic study of the isomers of pyrrolecarboxaldehyde and 1-methyl- pyrrolecarboxaldehyde. Journal of Physical Chemistry B, 115(43), 12549–12557. https://doi.org/10.1021/jp2068057
Santos, J. R., Viegas, O., Páscoa, R. N. M. J., Ferreira, I. M. P. L. V. O., Rangel, A. O. S. S., & Lopes, J. A. (2016). In-line monitoring of the coffee roasting process with near infrared spectroscopy: Measurement of sucrose and colour. Food Chemistry, 208, 103–110. https://doi.org/https://doi.org/10.1016/j.foodchem.2016.03.114
Sberveglieri, V., Núñez, E., Zappa, D., Comini, E., & Pulvirenti, A. (2014). Classification of Different Roasting Processes by MOX Nanowire. Procedia Engineering, 87, 572–575. https://doi.org/10.1016/j.proeng.2014.11.553
Sberveglieri, V., Pulvirenti, A., Comini, E., & Carmona, E. N. (2014). What happens at the aroma of coffee beans after roasting?: MOX nanowire technology by Novel Electronic Nose to discover the finger print. Proceedings of the International Conference on Sensing Technology, ICST, 2014-Janua, 81–84.
SCAA. (2015). SCAA Protocols Cupping Specialty Coffee. Specialty Coffee Association of America, 1–10. http://www.scaa.org/?page=resources&d=coffee-protocols
?emen, S., Mercan, S., Yayla, M., & Aç?kkol, M. (2017). Elemental composition of green coffee and its contribution to dietary intake. Food Chemistry, 215, 92–100. https://doi.org/10.1016/j.foodchem.2016.07.176
Shi, B., Zhao, L., Zhi, R., & Xi, X. (2013). Optimization of electronic nose sensor array by genetic algorithms in Xihu-Longjing Tea quality analysis. Mathematical and Computer Modelling, 58(3–4), 746–752. https://doi.org/10.1016/j.mcm.2012.12.029
Sims, G. K., & O’Loughlin, E. J. (1989). Degradation of pyridines in the environment. Critical Reviews in Environmental Control, 19(4), 309–340. https://doi.org/10.1080/10643388909388372
Singh, R. (2002). An intelligent system for odour discrimination. In Electronic Design, Test and Applications, 2002. Proceedings. The First IEEE International Workshop on (pp. 489–491). https://doi.org/10.1109/DELTA.2002.994681
?liwi?ska, M., Wi?niewska, P., Dymerski, T., Wardencki, W., & Namie?nik, J. (2016). 8 - Advances in Electronic Noses and Tongues for Food Authenticity Testing A2 - Downey, Gerard BT - Advances in Food Authenticity Testing. In Woodhead Publishing Series in Food Science, Technology and Nutrition (pp. 201–225). Woodhead Publishing. https://doi.org/http://dx.doi.org/10.1016/B978-0-08-100220-9.00008-4
Son, M., Lee, J. Y., Ko, H. J., & Park, T. H. (2017). Bioelectronic Nose: An Emerging Tool for Odor Standardization. Trends in Biotechnology, 35(4), 301–307. https://doi.org/10.1016/j.tibtech.2016.12.007
Spyros, A. (2016). Application of NMR in food analysis. In Nuclear Magnetic Resonance (Vol. 45, pp. 269–307). https://doi.org/10.1039/9781782624103-00269
Steinhart, H., Stephan, A., & Bücking, M. (2000). Advances in flavor research. Journal of Separation Science, 23(7–8), 489–496.
Sunarharum, W. B., Williams, D. J., & Smyth, H. E. (2014). Complexity of coffee flavor: A compositional and sensory perspective. Food Research International, 62, 315–325. https://doi.org/10.1016/j.foodres.2014.02.030
Sundic, T., Marco, S., Samitier, J., & Wide, P. (2000). Electronic Tongue and Electronic Nose Data Fusion in Classification with Neural Networks and Fuzzy Logic Based Models. Ieee, 1474–1479.
Sung, E. H., Shin, S. M., & Kang, Y.-H. (2017). Physicochemical quality characteristics and antioxidant activity of wasabi (Wasabia japonica) leaf and petiole extracts. Journal of the Korean Society of Food Science and Nutrition, 46(3), 335–342. https://doi.org/10.3746/jkfn.2017.46.3.335
Suslick, B. A., Feng, L., & Suslick, K. S. (2010). Discrimination of complex mixtures by a colorimetric sensor array: Coffee aromas. Analytical Chemistry, 82(5), 2067–2073. https://doi.org/10.1021/ac902823w
Tahara, Y., & Toko, K. (2013). Electronic tongues-a review. IEEE Sensors Journal, 13(8), 3001–3011. https://doi.org/10.1109/JSEN.2013.2263125
Tan, T. T., Loubet, F., Labreche, S., & Amine, H. (1997). Quality control of coffee using the FOX4000 electronic nose. In Industrial Electronics, 1997. ISIE ’97., Proceedings of the IEEE International Symposium on (Vol. 1, pp. SS140-SS145 vol.1). https://doi.org/10.1109/ISIE.1997.651750
Thepudom, T., Sricharoenchai, N., & Kerdcharoen, T. (2013). Classification of instant coffee odors by electronic nose toward quality control of production. 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2013, 4–7. https://doi.org/10.1109/ECTICon.2013.6559482
Toci, A T, & Farah, A. (2014). Volatile fingerprint of Brazilian defective coffee seeds: Corroboration of potential marker compounds and identification of new low quality indicators. Food Chemistry, 153, 298–314. https://doi.org/10.1016/j.foodchem.2013.12.040
Toci, Aline T, & Farah, A. (2008). Volatile compounds as potential defective coffee beans’ markers. Food Chemistry, 108(3), 1133–1141. https://doi.org/https://doi.org/10.1016/j.foodchem.2007.11.064
Tolessa, K., Rademaker, M., Baets, B. De, Boeckx, P., De Baets, B., & Boeckx, P. (2016). Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans. Talanta, 150, 367–374. https://doi.org/10.1016/j.talanta.2015.12.039
Toshio, M., Gallagher, M., George, P., & Wise, P. M. (2009). Odor detection of mixtures of homologous carboxylic acids and coffee aroma compounds by humans. Journal of Agricultural and Food Chemistry, 57(21), 9895–9901. https://doi.org/10.1021/jf901453r
Tran, P. D., Van de Walle, D., De Clercq, N., De Winne, A., Kadow, D., Lieberei, R., Messens, K., Tran, D. N., Dewettinck, K., & Van Durme, J. (2015). Assessing cocoa aroma quality by multiple analytical approaches. Food Research International, 77, Part 3, 657–669. https://doi.org/http://dx.doi.org/10.1016/j.foodres.2015.09.019
Tudu, B., Ghosh, S., Bag, A. K., Ghosh, D., Bhattacharyya, N., & Bandyopadhyay, R. (2015). Incremental FCM Technique for Black Tea Quality Evaluation Using an Electronic Nose. Fuzzy Information and Engineering, 7(3), 275–289. https://doi.org/http://dx.doi.org/10.1016/j.fiae.2015.09.002
Upadhyay, R., Sehwag, S., & Mishra, H. N. (2017). Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis. In Food Chemistry (Vol. 221). https://doi.org/10.1016/j.foodchem.2016.10.089
Vargas-Elías, G. A., Corrêa, P. C., De Souza, N. R., Baptestini, F. M., & Melo, E. D. C. (2016). Kinetics of mass loss of arabica coffee during roasting process. Engenharia Agricola, 36(2), 300–308. https://doi.org/10.1590/1809-4430-Eng.Agric.v36n2p300-308/2016
Várvölgyi, E., Dénes, L. D., Kovács, Z., Szöllosi, D., & Fekete, A. (2013). Analysis of differently roasted Arabica coffee samples by electronic tongue. American Society of Agricultural and Biological Engineers Annual International Meeting 2013, ASABE 2013, 4, 2932–2937.
Várvölgyi, E., Kozits, S., Soós, J., Szöllosi, D., Kovács, Z., & Fekete, A. (2012). Application of electronic tongue for distinguishing coffee samples and predicting sensory attributes. Progress in Agricultural Engineering Sciences, 8(1), 49–63. https://doi.org/10.1556/Progress.8.2012.5
Várvölgyi, E., Werum, T., Dénes, L. D., Soós, J., Szabó, G., Felföldi, J., Esper, G. J., & Kovács, Z. (2014). Vision system and electronic tongue application to detect coffee adulteration with barley. Acta Alimentaria, 43, 197–205. https://doi.org/10.1556/AAlim.43.2014.Suppl.27
Wang, X., & Lim, L.-T. (2015a). Chapter 27 - Physicochemical Characteristics of Roasted Coffee. In V. R. Preedy (Ed.), Coffee in Health and Disease Prevention (pp. 247–254). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-409517-5.00027-9
Wang, X., & Lim, L.-T. (2015b). Chapter 27 - Physicochemical Characteristics of Roasted Coffee A2 - Preedy, Victor R. BT - Coffee in Health and Disease Prevention (pp. 247–254). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-409517-5.00027-9
Wang, Y., & Kays, S. J. (2003). Analytically directed flavor selection in breeding food crops. Journal of the American Society for Horticultural Science, 128(5), 711–720.
Wasilewski, T., Gebicki, J., & Kamysz, W. (2017). Bioelectronic nose: Current status and perspectives. In Biosensors and Bioelectronics. https://doi.org/10.1016/j.bios.2016.08.080
Wasilewski, T., G?bicki, J., & Kamysz, W. (2017). Bioelectronic nose: Current status and perspectives. Biosensors and Bioelectronics, 87(August), 480–494. https://doi.org/10.1016/j.bios.2016.08.080
Wei, F., Furihata, K., Miyakawa, T., & Tanokura, M. (2014). A pilot study of NMR-based sensory prediction of roasted coffee bean extracts. Food Chemistry, 152, 363–369. https://doi.org/https://doi.org/10.1016/j.foodchem.2013.11.161
Wu, D., & Sun, D. W. (2013). Colour measurements by computer vision for food quality control - A review. Trends in Food Science and Technology, 29(1), 5–20. https://doi.org/10.1016/j.tifs.2012.08.004
Yang, N., Liu, C., Liu, X., Degn, T. K., Munchow, M., & Fisk, I. (2016). Determination of volatile marker compounds of common coffee roast defects. Food Chemistry, 211, 206–214. https://doi.org/10.1016/j.foodchem.2016.04.124
Yasuura, M., & Toko, K. (2015). Review of development of sweetness sensor. IEEJ Transactions on Sensors and Micromachines, 135(2), 51–56. https://doi.org/10.1541/ieejsmas.135.51
Zajdenwerg, C., Branco, G. F., Alamed, J., Decker, E. A., & Castro, I. A. (2011). Correlation between sensory and chemical markers in the evaluation of Brazil nut oxidative shelf-life. European Food Research and Technology, 233(1), 109–116. https://doi.org/10.1007/s00217-011-1493-x
Zhang, L., & Tian, F. C. (2014). A new kernel discriminant analysis framework for electronic nose recognition. Analytica Chimica Acta, 816, 8–17. https://doi.org/10.1016/j.aca.2014.01.049
Zhang, X., Cheng, J., Wu, L., Mei, Y., Jaffrezic-Renault, N., & Guo, Z. (2018a). An Overview of an artificial nose system. Talanta. https://doi.org/10.1016/j.talanta.2018.02.113
Zhang, X., Cheng, J., Wu, L., Mei, Y., Jaffrezic-Renault, N., & Guo, Z. (2018b). An Overview of an artificial nose system. Talanta. https://doi.org/10.1016/j.talanta.2018.02.113