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Papanicolaou (Pap) stain is used to stain cells to visually assess lesions or determine if they are benign or malignant in cytological examinations. In digital cytology imaging, Pap stain unmixing has been proposed to enable objective interpretation of stain abundance for the diagnosis based on quantitative measurement. Using sparsity-based regularization, our previously proposed method allowed stain unmixing for RGB images with fewer channels than Pap staining dyes. Its effectiveness was demonstrated with simulated RGB images converted from multispectral data. In this study, we validate the robustness of stain quantification against color variations induced by different imaging systems, proving this method works in actual RGB systems. The robustness is achieved by extracting the stain matrix from single stain images and normalizing with the robust maximum values of stain abundance. Additionally, we apply this technique to classify cytoplasmic mucin in lobular endocervical glandular hyperplasia and normal endocervical cells. The results yield high accuracy while allowing the colors to be expressed quantitatively through the amounts of dyes, which can serve as the criterion for judgment. The cell classification is achieved using RGB images obtained from a practical whole slide image scanner, in which the classifier is trained with RGB images generated from multispectral data.Clinical Relevance-This establishes the efficacy of Papanicolaou stain unmixing for the robust detection of lobular endocervical glandular hyperplasia cells in RGB images obtained from various imaging systems.