###### 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. c 2021 2  
and released, providing a coffee that is weak and low in aromatics [9]. Regarding water hardness, [PERSON] et al. [10] found that coffee brewed with medium water hardness (Total dissolved solids = TDS = 141 mg L\({}^{-1}\)) provides a product with more aromas compared to soft (TDS = 60 mg L\({}^{-1}\)) and high water hardness (TDS = 424 mg L\({}^{-1}\)). [PERSON] et al. [11] mentioned several studies in which different aroma profiles have been found for distinct preparation methods such as boiled coffee brewing and pressurized espresso brewing. To ensure that the final products (brewed coffee) will be acceptable by consumers, different techniques are used to assess their quality traits. Traditional methods to assess aromas in coffee consist of either instrumental techniques using gas-chromotography/mass-spectroscopy (GC/MS) [12; 13], or most commonly through sensory analysis using descriptive sensory panels [14] and methods such as quantitative descriptive analysis (QDA(r)), or expert panels. The latter tend to be less reliable as sensory panels are often subjected to more biases, such as the habituation error [15]. Furthermore, these techniques tend to be time-consuming, are destructive, and require larger sample sizes; they involve higher costs and high expertise for data acquisition, analysis and interpretation [5; 6; 16; 17; 18; 19; 20]. Electronic noses (e-noses) were first designed and proposed in the early 1980s by [PERSON] and [PERSON] [21], who developed an e-nose using semiconductor transducers and finding that this was able to discriminate a broad range of odors. Following this, some researchers have either developed or used commercial e-noses as an alternative to traditional methods to assess aromas or other chemometrics in food and beverages such as beer [17; 22; 23; 24], wine [16; 25], meat [26], juices [27; 28], saffron [29], and tea [30; 31; 32], among others. Likewise, e-noses have been used in coffee to assess aromas and predict sensory descriptors using artificial neural networks (ANN) [33], to predict the geographical origin using discriminant factorial analysis [4], to discriminate between civet and non-civet coffee [5; 34], and to predict the roasting degree using ANN [35], among others. However, most of the e-noses used in the aforementioned studies are non-portable, and most of them, despite having lower costs than GC/MS, are still cost-prohibitive for small and medium companies.