ArticleSensors (Basel, Switzerland)2021
Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Tracking Aromatic Volatile Biomarkers Through Coffee Bean Postharvest Stages.Molecules (Basel, Switzerland) · 2026Review
- Impacts of Roasting Intensity and Cultivar on Date Seed Beverage Quality Traits and Volatile Compounds Using Digital Technologies.Foods (Basel, Switzerland) · 2025Article
- Spaceward senses: examining retronasal aroma and mouthfeel perception in simulated space-microgravity environments.NPJ science of food · 2025Article
- Effect of climate, growing region, country of origin, and post-harvest processing on the of content chlorogenic acids (CGAs) and aromatic compounds in roasted coffee beans.Scientific reports · 2025Article
- The Use of Coffee Aroma for Stress Reduction in Postgraduate Dental Students.International dental journal · 2024Article
- Optimization of Temperature Modulation for Gas Classification Based on Bayesian Optimization.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- Electronic Nose and Gas Chromatograph Devices for the Evaluation of the Sensory Quality of Green Coffee Beans.Foods (Basel, Switzerland) · 2023Article
- Novel Contactless Sensors for Food, Beverage and Packaging Evaluation.Sensors (Basel, Switzerland) · 2023Article
- Physicochemical features partially explain olfactory crossmodal correspondences.Scientific reports · 2023Article
- Composition of Minerals and Volatile Organic Components of Non-Centrifugal Cane Sugars from Japan and ASEAN Countries.Foods (Basel, Switzerland) · 2023Article
- Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Early Detection ofSensors (Basel, Switzerland) · 2022Article
- Evaluation of the Olfactory Quality of Roasted Coffee Beans Using a Digital Nose.Sensors (Basel, Switzerland) · 2022Article
- Real Time Monitoring of Wine Vinegar Supply Chain through MOX Sensors.Sensors (Basel, Switzerland) · 2022Article
- Analysis of Lipids in Green Coffee by Ultra-Performance Liquid Chromatography-Time-of-Flight Tandem Mass Spectrometry.Molecules (Basel, Switzerland) · 2022Article
- Characterization of the volatile organic compounds produced from green coffee in different years by gas chromatography ion mobility spectrometry.RSC advances · 2022Article
- Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies.Foods (Basel, Switzerland) · 2022Article
- Article
- Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on-time and ensure high-quality products.
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