Chemical Condition Assessment of Historical Paper Using Segmented pH Modelling Within the ARTEMISA Framework: A Conservation Use Case

Historical paper collections held in archives and libraries represent an irreplaceable component of cultural heritage; yet, their systematic chemical assessment is often constrained by limited measurement datasets, heterogeneous material composition, and restricted access for sampling. This paper presents an extended version of the ARTEMISA framework (Intelligent Surface Assessment), a web-based decision-support application designed to assist conservators and archivists in assessing the chemical condition and degradation dynamics of paper-based materials under real-world constraints. The framework characterises the chemical state of paper through surface pH measurement—a non-destructive, practically accessible proxy for acidity-driven cellulose degradation. Rather than aiming at deterministic lifetime prediction, ARTEMISA emphasises chemically consistent segmentation of samples into homogeneous pH regimes, followed by regime-specific statistical modelling. For long-term trend exploration, the system integrates three regression approaches: log-transformed linear, polynomial, and exponential models. For short-term time-series analysis within restricted chemical regimes, an ARIMA model is applied. Model performance was evaluated using standard statistical metrics: Mean squared error (MSE), Root mean squared error (RMSE), Mean absolute error (MAE) and the coefficient of determination (R2). The framework was applied to a case study comprising twelve book titles from the Public Library in Trenčín (Slovak Republic), published between 1959 and 2016. Results indicate that log-transformed linear and exponential regression models provide physically consistent representations of long-term pH trends, while ARIMA modelling captures short-term dynamics directly actionable for conservation planning. Exponential regression achieved R2 = 0.783, linear 0.826, polynomial 0.911; however, the polynomial model produced physically implausible extrapolations and is retained for comparative purposes only. The Arrhenius principle is incorporated as an interpretative component, contextualising pH-derived degradation trends within temperature-dependent reaction kinetics. ARTEMISA offers an accessible, open-architecture tool for the prioritisation of conversation interventions under conditions of data scarity. This is applicable to broader material degradation studies with limited measurements.