Determination of organic additives in mortars by near-IR spectroscopy. A novel approach to designing a sample set with high-variability components.
Industrial mortars consist primarily of a mixture of cement and an aggregate plus a small amount of additives that are used to modify specific properties. Using too high or too low additive rates usually results in the loss of desirable properties in the end product. This entails carefully controlling the amounts of additives added to mortar in order to ensure correct dosing and/or adequate homogeneity in the final mixture. Near-IR (NIR) spectroscopy has proved effective for this purpose as it requires no sample pretreatment and affords expeditious analyses. The purpose of this work was to determine two organic additives (viz. Ad1 and Ad2) in mortars by using partial least squares regression multivariate calibration models constructed from NIR spectroscopic data. The additives are used to expedite setting and increase cohesion between particles in the mortar. In order to ensure that the sample set contained natural variability in the samples, we used a methodology based on experimental design to construct a representative set of samples. This novel design is based on a hexagonal antiprism that encompasses the concentration ranges spanned by the analytes and the variability inherent in each additive. The D-optimality criterion was used to obtain various combinations between Ad1 and Ad2 additive classes. The partial least squares calibration models thus constructed for each additive provided accurate predictions: the intercept and the slope of the plots of predicted values versus reference values for each additive were close to 0 and 1, respectively, and their confidence ranges included the respective value. The ensuing analytical methods were validated by using an external sample set.