## 1 Introduction  
A fuel cell is an electrochemical cell that converts the chemical energy of the fuel and an oxidising agent into electricity. This conversion typically makes use of hydrogen as a fuel and oxygen as an oxidising agent. Hydrogen fuel cells have long been regarded as a more environmentally friendly alternative to traditional fossil fuels. Because water is the only direct byproduct of their energy production, they will significantly reduce waste and man-made greenhouse gases by oxidising molecular hydrogen [1, 2]. However, a group of American researchers believes that fuel cells may have a negative impact on the environment. [PERSON] of the California Institute of Technology and colleagues used atmospheric simulations to show that the unavoidable pollution emitted by fuel cell technology could cause significant harm to the ozone layer [3]. Among non-fossil feedbacks such as ethanol, methanol, and others, ethanol has many advantages as a chemical (H\({}_{2}\)) source for fuel cells due to its storage capabilities, ease of handling, and stable transportation due to its low toxicity and volatility. The production of (H\({}_{2}\)) from renewable resources is a critical stage in the evolution of technology. The production of (H\({}_{2}\)) from bioethanol derived from agroindustrial wastes, in particular, is an appealing option because bioethanol derived from residual biomass is less expensive than bioethanol derived from food crops and does not jeopardise food safety [4, 5]. Ethanol steam reforming (ESR) is a promising reaction for producing carbon-neutral hydrogen in a long-term, safe manner for this purpose, according to [6; 7]. ESR primarily generates hydrogen (H\({}_{2}\)), with byproducts such as carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), and methane (CH\({}_{4}\)). The purification of this syngas is critical and is defined by the final use of Hydrogen (H\({}_{2}\)), so the quantification of syngas components is of interest to the researcher [8; 9]. It appears that using a (CeO\({}_{2}\)) catalyst in a system is promising for removing the majority of the (CO) from syngas [10]. Modifying the support nanostructure of (CeO\({}_{2}\)), such as polyhedra, rods, and cubes, has been identified as a novel technique for developing more active, selective, and stable catalysts [11]. Fourier-transform infrared spectroscopy (FTIR) is used for characterization because it is a fast, accurate, and inexpensive technique. For example, FTIR has been used to characterise MnOx-CeO\({}_{2}\) catalysts for low-temperature selective oxidation [12]. FTIR looks into the conditions that occur during CO oxidation over Ru(x)-CeO\({}_{2}\) catalysts [13]. FTIR describe CeO\({}_{2}\) catalysts for diesel pollutant removal [14]. FTIR describes the preparation of copper-promoted CeO\({}_{2}\) catalysts [15]. FTIR investigates the total oxidation of formaldehyde over MnOX-CeO\({}_{2}\) catalysts [16] and so forth. Because FTIR produces high-dimensional data with a small sample size, partial least squares (_PLS_) regression is considered a viable option for modelling the spectrum data [17]. [PERSON] et al. recently used _PLS_ to predict the antibacterial activity based on FTIR data [18]. Based on spectroscopic data, _PLS_ assisted in predicting catalyst activity [19] and it aids in determining the composition of bio-oil using infrared spectroscopy [20]. Based on FTIR data, we predicted ESR products such as hydrogen (H\({}_{2}\)) with side products such as carbon dioxide (CO\({}_{2}\)), carbon monoxide (CO), and methane (CH\({}_{4}\)) in this article. Variable selection measures are required for _PLS_-based characterization. For this purpose, _PLS_ is equipped with loading weight, regression coefficients, variable importance on projection [21] and selectivity ratio [22; 23] type filter measures exist [24]. These measures' performance still needs to be improved [25]. For this purpose, we have introduced the Johnsen Index [26; 27] based variable selection in _PLS_.