The use of multivariate curve resolution-alternating least-squares (MCR-ALS) in liquid chromatography-infrared detection (LC-IR) is troublesome due to the intense background absorption changes during gradient elution. Its use has been facilitated by previous removal of a significant part of the solvent background IR contributions due to common mobile phase systems employed during reversed phase gradient applications. Two straightforward background correction approaches based on simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and principal component analysis (PCA) are proposed and evaluated on reversed phase gradient LC-IR data sets obtained during the analysis of carbohydrate and nitrophenol mixtures. After subtraction of the calculated background signal, MCR-ALS provided improved signal-to-noise ratios, removed remaining mobile phase and background signal contributions, and resolved overlapping chromatographic peaks. The present approach tends to enable easy-to-use background correction to facilitate the use of MCR-ALS in online LC-IR, even in challenging situations when gradient conditions are employed and only poor chromatographic resolution is achieved. It, therefore, shows great potential to facilitate the full exploitation of the advantages of simultaneous quantification and identification of a vast amount of analytes employing online IR detection, making new exciting applications more accessible.