Prediction of Acidity Levels of Fresh Roasted Coffees Using E-nose and Artificial Neural Network

As for the coming automation age, development of the sense of robot including sight, smelling, hearing and touch is vital for robots to complement complex tasks of human. Recently, increasing interests in robotic chef and barista call for the development of “digital deliciousness” technology, in order for the robots to have a capability of food tasting. This work demonstrates a preliminary development of gourmet robot by using electronic nose (e-nose) technology to determine the scoring and cupping of the quality of coffees as compared to the human testers. In this study, it was mainly focused on the acidity levels of fresh roasted coffee. Array of different eight semiconductor gas sensors was used to smell the coffee’s aroma. Electronic nose can clearly classify the acidity levels of different roasting degrees of the roasted coffee and has nearly the same results of scoring as obtained by using artificial neural network and the human’s scoring. Thus, the e-nose has shown its capability for integration into the gourmet robot according to this study.

[1]  Spyros G. Tzafestas Mobile Robots at Work , 2014 .

[2]  Răzvan Gabriel Boboc,et al.  An Educational Humanoid Laboratory Tour Guide Robot , 2014 .

[3]  Ted R. Lingle,et al.  Cupping and Grading—Discovering Character and Quality , 2017 .

[4]  Genci Capi,et al.  Development of a new mobile humanoid robot for assisting elderly people , 2012 .

[5]  Andrzej Szczurek,et al.  Relationship between odour intensity assessed by human assessor and TGS sensor array response , 2005 .

[6]  Ben Goertzel,et al.  A Software Architecture for Generally Intelligent Humanoid Robotics , 2014, BICA.

[7]  Luís Roberto Batista,et al.  Coffee: Types and Production , 2016 .

[8]  Yu Xing,et al.  A prediction method for the wax deposition rate based on a radial basis function neural network , 2017 .

[9]  Fredericka Brown,et al.  Calculating the optimum temperature for serving hot beverages. , 2008, Burns : journal of the International Society for Burn Injuries.

[10]  Kaori Kobayashi,et al.  Tasting Robot with an Optical Tongue: Real Time Examining and Advice Giving on Food and Drink , 2007, HCI.

[11]  Carme Torras,et al.  Efficient interactive decision-making framework for robotic applications , 2017, Artif. Intell..

[12]  Spyros G. Tzafestas Mobile Robot Sensors , 2014 .

[13]  Jie Sun,et al.  A Review on 3D Printing for Customized Food Fabrication , 2015 .

[14]  Vincenzo Lippiello,et al.  Tracking elastic deformable objects with an RGB-D sensor for a pizza chef robot , 2017, Robotics Auton. Syst..

[15]  Giovanni Pilato,et al.  Exploiting interactive genetic algorithms for creative humanoid dancing , 2016, BICA 2016.

[16]  Thara Seesaard,et al.  Development of Fabric-Based Chemical Gas Sensors for Use as Wearable Electronic Noses , 2015, Sensors.

[17]  Teerakiat Kerdcharoen,et al.  Classification of instant coffee odors by electronic nose toward quality control of production , 2013, 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[18]  Martin Fischer,et al.  Service Robotics and Human Labor: A first technology assessment of substitution and cooperation , 2017, Robotics Auton. Syst..