Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Chemical Characterization of Capsule-Brewed Espresso Coffee Aroma from the Most Widespread Italian Brands by HS-SPME/GC-MS

(2020) Veronica Lolli et al.   MOLECULES

Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality

(2020) Claudia Gonzalez Viejo et al.   SENSORS AND ACTUATORS B-CHEMICAL

Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability

(2020) Anh-Duc Pham et al.   JOURNAL OF CLEANER PRODUCTION

Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data

(2020) Sigfredo Fuentes et al.   SENSORS

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

(2020) Sigfredo Fuentes et al.   SENSORS

Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

(2019) Sigfredo Fuentes et al.   SENSORS

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling

(2019) Pablo Inocêncio Monteiro et al.   JOURNAL OF FOOD SCIENCE

Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters

(2018) S. Fuentes et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS

(2018) Nicola Caporaso et al.   FOOD RESEARCH INTERNATIONAL

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers

(2018) Claudia Gonzalez Viejo et al.   JOURNAL OF FOOD SCIENCE

Machine learning for the structure–energy–property landscapes of molecular crystals

(2018) Félix Musil et al.   Chemical Science

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

(2018) Wenjiang Dong et al.   FOOD CHEMISTRY

Impact of consumer behavior on furan and furan-derivative exposure during coffee consumption. A comparison between brewing methods and drinking preferences

(2018) Anja Rahn et al.   FOOD CHEMISTRY

Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection

(2018) Huixiang Liu et al.   SENSORS

Discrimination and geographical origin prediction of washed specialty Bourbon coffee from different coffee growing areas in Rwanda by using electronic nose and electronic tongue

(2017) Kayihura Joseph Flambeau et al.   FOOD SCIENCE AND BIOTECHNOLOGY

Effect of reversed coffee grinding and roasting process on physicochemical properties including volatile compound profiles

(2017) Su Jeong Lee et al.   Innovative Food Science & Emerging Technologies

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

(2017) Claudia Gonzalez Viejo et al.   JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE

Electronic noses: Powerful tools in meat quality assessment

(2017) Wojciech Wojnowski et al.   MEAT SCIENCE

A portable electronic nose as an expert system for aroma-based classification of saffron

(2016) Sajad Kiani et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Furan, 2-methylfuran and 3-methylfuran in coffee on the Canadian market

(2016) A. Becalski et al.   JOURNAL OF FOOD COMPOSITION AND ANALYSIS

The influence of different types of preparation (espresso and brew) on coffee aroma and main bioactive constituents

(2015) Giovanni Caprioli et al.   INTERNATIONAL JOURNAL OF FOOD SCIENCES AND NUTRITION

Changes in the Aromatic Profile of Espresso Coffee as a Function of the Grinding Grade and Extraction Time: A Study by the Electronic Nose System

(2015) C. Severini et al.   JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY

Classification and regression of ELM, LVQ and SVM for E-nose data of strawberry juice

(2015) Shanshan Qiu et al.   JOURNAL OF FOOD ENGINEERING

Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality

(2012) B. Bertrand et al.   FOOD CHEMISTRY

Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control

(2012) Santina Romani et al.   JOURNAL OF FOOD SCIENCE

Optimized Neural Network for Instant Coffee Classification through an Electronic Nose

(2011) Evandro Bona et al.   International Journal of Food Engineering

Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

(2011) Quansheng Chen et al.   SENSORS AND ACTUATORS B-CHEMICAL

Gas Chromatography/Olfactometry and Electronic Nose Analyses of Retronasal Aroma of Espresso and Correlation with Sensory Evaluation by an Artificial Neural Network

(2010) Tomomi Michishita et al.   JOURNAL OF FOOD SCIENCE

Influence of storage conditions on aroma compounds in coffee pads using static headspace GC–MS

(2009) Meike Bröhan et al.   FOOD CHEMISTRY

Quality grade identification of green tea using E-nose by CA and ANN

(2007) Huichun Yu et al.   LWT-FOOD SCIENCE AND TECHNOLOGY