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

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 six 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.

Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence

“…The outputs were then analyzed using a supervised automatic code written in Matlab ® R2020b (Mathworks, Inc., Natick, MA, USA). This code is able to automatically recognize features of curves (starting and end of stable signals) and create 10 subdivisions of each curve from the e-nose sensors from the stable signals to calculate 10 mean values [30]. This is done to increase variability of the data to further develop the ML models.…”

Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling

“…The e-nose was calibrated for ~30 s prior to recording each measurement to ensure all sensors reached the baseline and then placed inside the tent on top of the plants to record data for 1.5 min; each tent was measured in triplicates. The output data (Volts) were then analyzed using a code written in MATLAB ® R2020a (Mathworks Inc., Natick, MA, USA) to extract the mean values of ten segments from the highest peak of the curves as described by Gonzalez Viejo et al [ 37 ].…”

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling

“…Data from the e-nose was acquired using a customised code written in MATLAB ® R2020a (MathWorks, Inc., Natick, MA, USA) to identify the stable signals from when the e-nose was placed on the beaker containing the sample until just before it was removed. Following this, the data was automatically divided into 10 subdivisions to extract the average values per sensor, as previously detailed by Gonzalez Viejo et al [ 22 ]. The average values were then used as inputs for machine learning modelling.…”

“…In addition, sensory evaluation using a trained panel is often employed to assess wine quality [ 5 , 21 ]. However, this form of assessment requires the recruitment and training of a large number of participants, which can be expensive and time-consuming, and the results may be subject to bias due to individual variability of the participants, which may affect their taste and smell [ 5 , 21 , 22 ]. There is, therefore, a need for a rapid, cost-effective method for assessing the volatile aromatic compound and sensory qualities of wine that winemakers can use in the winery [ 18 ].…”