Identification of Coffee Types Using an Electronic Nose with the Backpropagation Artificial Neural Network

Roza Susanti - Politeknik Negeri Padang, Padang, West Sumatera, Indonesia
Zaini Zaini - Universitas Andalas, Padang, West Sumatera, Indonesia
Anton Hidayat - Politeknik Negeri Padang, Padang, West Sumatera, Indonesia
Nadia Alfitri - Politeknik Negeri Padang, Padang, West Sumatera, Indonesia
Muhammad Ilhamdi Rusydi - Universitas Andalas, Padang, West Sumatera, Indonesia

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Coffee is one of the famous plants’ commodities in the world. There are some coffee powders such as Arabica dan Robusta. This study aimed to identify two various coffee powders, Arabica and Robusta based on the blended aroma profiles, employing the backpropagation Artificial Neural Network (ANN). Four taste sensors were employed, namely TGS 2602, 2610, 2611, and 2620, to capture the diverse coffee aroma. These detectors were combined with the aroma sensors having transducers integrated with signal amplifiers or processors, which featured a load of 10 KΩ resistance. Three aroma types were investigated, namely Arabica coffee, Robusta coffee, and without coffee beans. The neural network architecture consisted of four inputs from all sensors, with one hidden layer housing eight neurons. Two neuron outputs were employed for classification, with 70 samples used for training ANN for each type. During the training phase, the developed neural network showed an impressive accuracy rate of 91.90%. TGS 2602 and 2611 sensors showed the most significant differences among the three aroma types. When analyzing ground Robusta coffee, TGS 2602 and 2611 sensors recorded 2.967 volts and 1.263 volts, with a gas concentration of 17.92 ppm and 2441.8 ppm. Similarly, the sensors for ground Arabica coffee displayed 3.384 volts and 1.582 volts with a gas concentration of 20.445 ppm and 3058.5 ppm in both TGS 2602 and 2611, respectively. The implemented ANN with aroma sensor as input successfully identify the coffee powders.


Coffee Ground; Aroma; Electronic Tasting; Gas Sensor; Neural Network

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S. Anita and Albarda, “Classification Cherry’s Coffee using k-Nearest Neighbor (KNN) and Artificial Neural Network (ANN),†in 2020 International Conference on Information Technology Systems and Innovation, ICITSI 2020 - Proceedings, Oct. 2020, pp. 117–122, doi: 10.1109/ICITSI50517.2020.9264927.

F. T. Boro, I. Riyanto, and K. Adiyarta, “Automatic Coffee Grinding and Brewing Process with NUC140 Microcontroller,†Autom. Coffee Grind. Brew. Process with NUC140 Microcontroller, 2017.

S. Gadre and D. S. Joshi, “E- Nose System using Artificial Neural Networks (ANN) to Detect Pollutant Gases,†IEEE, no. 978-1-5090-3704–9/17/$31.00, 2017.

C. . Calderon et al., “Smartphone-based monitoring system of a coffee roaster machine, applied to small industry,†IEEE, 2018.

H. A. Tinoco et al., “Experimental Assessment of the Elastic Properties of Exocarp–Mesocarp and Beans of Coffea arabica L. var. Castillo Using Indentation Tests,†Agric., vol. 12, no. 4, Apr. 2022, doi: 10.3390/agriculture12040502.

C. G. Viejo, E. Tongson, and S. Fuentes, “Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity,†2021.

R. Susanti, Z. Ressy Aidha, M. Yuliza, and S. Yondri, “Artificial Neural Network Application for Aroma Monitoring on The Coffe Beans Blending Process,†Joiv, vol. 2, 2018.

S. Abdul Muttalib, J. Nugraha WK, and N. Bintoro, “Analisis Kadar Air dan Aroma Blending Kopi Arabika (Coffea arabica L) dan Robusta(Coffea canephora L) Selama Penyimpanan Dengan Principal Component Analisys (PCA),†J. Agrotek Ummat, vol. 6, no. 1, p. 23, 2019, doi: 10.31764/agrotek.v6i1.955.

S. Palaniappan, N. A. Hameed, A. Mustapha, and A. Samsudin, “Classification of Alcohol Consumption among Secondary School Students,†joiv, vol. 1, 2017.

A. B. M. Wijaya, D. S. Ikawahyuni, R. Gea, and F. Maedjaja, “Role Comparison between Deep Belief Neural Network and Neuro Evolution of Augmenting Topologies to Detect Diabetes,†JOIV, vol. 5, 2021.

N. Mohanasundaram, “Non Linear Predictive Modelling for IC Engine Using Artificial Neural Network,†in Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020, Oct. 2020, pp. 801–807, doi: 10.1109/I-SMAC49090.2020.9243342.

S. N. Kane, A. Mishra, and A. K. Dutta, “Preface: International Conference on Recent Trends in Physics (ICRTP 2016),†J. Phys. Conf. Ser., vol. 755, no. 1, 2016, doi: 10.1088/1742-6596/755/1/011001.

F. J. P. Montalbo and A. A. Hernandez, “An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional Neural Networks,†Proc. - 2020 16th IEEE Int. Colloq. Signal Process. its Appl. CSPA 2020, no. Cspa, pp. 213–218, 2020, doi: 10.1109/CSPA48992.2020.9068683.

E. Science, “Superior varieties of robusta coffee adapted to high elevation based on farmer selection Superior varieties of robusta coffee adapted to high elevation based on farmer selection,†2018, doi: 10.1088/1755-1315/418/1/012020.

A. Keidel, D. Von Stetten, C. Rodrigues, C. Máguas, and P. Hildebrandt, “Discrimination of green arabica and Robusta coffee beans by Raman spectroscopy,†J. Agric. Food Chem., vol. 58, no. 21, pp. 11187–11192, 2010, doi: 10.1021/jf101999c.

L.-M. Caracostea, “Determination of Caffeine Content in Arabica and Robusta Green Coffee of Indian Origin,†vol. 8705, no. June, pp. 69–79, 2021.

M. H. Samimi and H. D. Ilkhechi, “Survey of different sensors employed for the power transformer monitoring,†vol. c, 2019, doi: 10.1049/iet-smt.2019.0103.

Syafriandi, A. Lubis, R. Fadhil, and O. Paramida, “Characteristics of roasting arabica and robusta coffee beans with rotary cylinder tube roast machine with electric heat source,†IOP Conf. Ser. Earth Environ. Sci., vol. 1116, no. 1, 2022, doi: 10.1088/1755-1315/1116/1/012032.

A. Indah Puspa Rini, A. Agung Suryawan Wiranatha, I. Wayan Gd Sedana Yoga, M. Jurusan Teknologi Industri Pertanian, F. Teknologi Pertanian Unud, and D. Teknologi Industri Pertanian, “The Influence of Broken Coffee Beans in Roasting on Taste of Robusta Coffee at Pucak Sari Village, Buleleng, Bali,†2017.

Y. Y. Broza, R. Vishinkin, O. Barash, M. K. Nakhleh, and H. Haick, Chem Soc Rev organic compounds for non-invasive medical evaluation †. Royal Society of Chemistry, 2018.

T. Thepudom, N. Sricharoenchai, and T. Kerdcharoen, “Classification of instant coffee odors by electronic nose toward quality control of production,†2013 10th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol. ECTI-CON 2013, pp. 4–7, 2013, doi: 10.1109/ECTICon.2013.6559482.

S. Xu et al., “Detecting and monitoring the flavor of tomato (Solanum lycopersicum) under the impact of postharvest handlings by physicochemical parameters and electronic nose,†Sensors (Switzerland), vol. 18, no. 6, pp. 1–15, 2018, doi: 10.3390/s18061847.

Y. Arimurti, K. Triyana, and S. Anggrahini, “Portable Electronic Nose for Discrimination of Indonesian Robusta Coffee Aroma with Varied Roasting Temperature Correlated with Gas Chromatography Mass Spectrometry,†2019.

D. B. Magfira and R. Sarno, “CLASSIFICATION OF ARABICA AND ROBUSTA COFFEE USING ELECTRONIC NOSE,†Int. Conf. Inf. Commun. Technol. (ICOIACT)978-1-5386-0954-5/18/$31.00 ©2018 IEEE645, 2018.

H. Li, Q. Chen, J. Zhao, and Q. Ouyang, “Non-destructive evaluation of pork freshness using a portable electronic nose (E-nose) based on a colorimetric sensor array,†Anal. Methods, vol. 6, no. 16, pp. 6271–6277, 2014, doi: 10.1039/c4ay00014e.

A. R. Di Rosa, F. Leone, F. Cheli, and V. Chiofalo, “Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment – A review,†J. Food Eng., vol. 210, pp. 62–75, 2017, doi: 10.1016/j.jfoodeng.2017.04.024.

M. Śliwińska, P. Wiśniewska, T. Dymerski, J. Namieśnik, and W. Wardencki, “Food analysis using artificial senses,†J. Agric. Food Chem., vol. 62, no. 7, pp. 1423–1448, 2014, doi: 10.1021/jf403215y.

Z. Haddi et al., “E-Nose and e-Tongue combination for improved recognition of fruit juice samples,†Food Chem., vol. 150, pp. 246–253, 2014, doi: 10.1016/j.foodchem.2013.10.105.

O. Busto, “Analytica Chimica Acta Data fusion methodologies for food and beverage authentication and quality assessment e A review s a , Joan Ferr e Eva Borr a,†vol. 891, 2015, doi: 10.1016/j.aca.2015.04.042.

D. H. Kim, Y. J. Kim, and D. S. Hur, “Artificial neural network based breakwater damage estimation considering tidal level variation,†Ocean Eng., vol. 87, pp. 185–190, 2014, doi: 10.1016/j.oceaneng.2014.06.001.

T. M. Mitchell, Machine Learning. .

P. Dixit and S. Londhe, “Prediction of extreme wave heights using neuro wavelet technique,†Appl. Ocean Res., vol. 58, pp. 241–252, 2016, doi: 10.1016/j.apor.2016.04.011.

R. Susanti, R. Nofendra, M. Syaiful, and M. Ilhamdi, “The Use of Artificial Neural Networks in Agricultural Plants The Use of Artificial Neural Networks in Agricultural,†vol. 2, pp. 62–68, 2022.

M. I. Rusydi, A. Anandika, B. Rahmadya, K. Fahmy, and A. Rusydi, “Implementation of grading method for gambier leaves based on combination of area, perimeter, and image intensity using backpropagation artificial neural network,†Electron., vol. 8, no. 11, 2019, doi: 10.3390/electronics8111308.

S. Goyal and G. K. Goyal, “Machine Learning ANN Models for Predicting Sensory Quality of Roasted Coffee Flavoured Sterilized Drink,†ADCAIJ Adv. Distrib. Comput. Artif. Intell. J., vol. 2, no. 3, pp. 09–13, 2013, doi: 10.14201/adcaij201426913.

Y. H. Jung, B. Park, J. U. Kim, and T. Kim, “Bioinspired Electronics for Artificial Sensory Systems,†vol. 1803637, pp. 1–22, 2018, doi: 10.1002/adma.201803637.

J. Tan and J. Xu, “Arti fi cial Intelligence in Agriculture Applications of electronic nose ( e-nose ) and electronic tongue ( e-tongue ) in food quality-related properties determination : A review,†Artif. Intell. Agric., vol. 4, pp. 104–115, 2020, doi: 10.1016/j.aiia.2020.06.003.