## 1 Introduction  
Coffee is one of the most popular beverages in the world [1]. It accounted for 65% of the hot drinks market worldwide in 2019 [2]. Caffeine is a key compound in both green and roasted coffee beans as it contributes 1.1 wt% for Arabica and 2.2 wt% for Robusta coffee beans [1]. Caffeine can block adenosine, thereby on the one hand increasing alertness and arousal, but on the other hand reducing sleep quality [3]. Caffeine also has some adverse effects on gastrointestinal disturbance, palpitation and increasing blood pressure [4]. Some consumers who are sensitive to the stimulating effect of caffeine prefer avoiding caffeine and choose decaffeinated coffee instead. The consumption of decaffeinated coffee was around 5% and 20% in Germany and the US, respectively, between 2009 and 2014 [1]. The global decaffeinated coffee market size was USD 1.65 billion in 2019 and is expected to grow in the near future [5]. The maximum caffeine content in decaffeinated coffee products is different around the world. Most European countries, the US, and Canada restrict a maximum of 0.1 wt% caffeine in green and roasted coffee [1]. Since 1908, the first successful decaffeination process was patented, and the classical sequence of pre-wetting the green coffee beans with water, extracting caffeine, and drying the beans has been applied on an industrial scale. There are three caffeine extraction approaches, using organic solvents, water, and liquid or supercritical carbon dioxide (LiCO\({}_{2}\) or scCO\({}_{2}\)). Commonly used organic solvents are methylene chloride (DCM) and ethyl acetate (EA) [1]. Decaffeination by supercritical CO\({}_{2}\) has become a trend because of its advantages, such as it being harmless, non-flammable, and having outstanding selectivity. Removing the caffeine from coffee bean cells can cause unwanted side effects such as mass loss and solvent residues. The most direct effect to decaffeinated coffee drinkers is the relatively plain and thin taste [6]. This is due to the fact that the roasting process, which produces aroma compounds, is conducted after the decaffeination process [1]. Some aroma precursors may be co-extracted with caffeine during decaffeination. Therefore, the aroma cannot be vastly formed during roasting. A previous study reported a lower content of alkylpyrazines in decaffeinated coffee compared to regular coffee, which may be a result of the decaffeination process [7]. The earth aroma significantly contributed by the presence of pyrazines was reduced as well [1; 8]. Few papers have been published regarding the influence of the decaffeination process on coffee flavor. However, the flavor change of decaffeinated tea has been well-investigated in both green and black tea. It has been observed that the more caffeine was removed, the more volatile compounds were reduced. Some aroma-active compounds even disappeared after the decaffeination process [4; 9; 10]. Conventional chemometric methods such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least squares-discriminant analysis (PLS-DA) have been extensively used in food analytical chemistry [11] in general, and in particular in the study of the aroma profile of coffee [12; 13; 14]. However, in recent years, multiple works have pointed out the perks of using machine-learning (ML) algorithms such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF) to assess food quality and authenticity. They have also reported their advantages compared to conventional statistical tools [15]. These ML algorithms have yielded promising results when used in detecting food fraud, i.e., substituting original ingredients with cheaper ingredients [16], investigating food origin [17], and detecting food additives [18]. Although, CART and RF have provided valuable results in the area of food chemistry, they remain more frequently used in \"omics\"-related fields. SVM is the most commonly used ML algorithm in food analysis [15].