Inline process analytical technology sensors are the key elements to enable continuous manufacturing. They facilitate real-time monitoring of critical quality attributes of both intermediate materials and finished products. The aim of this study was to demonstrate method development and validation for inline and offline calibration strategies to determine the blend content during tablet compression via Raman spectroscopy. An inline principal component regression model was developed from Raman spectra collected in the feed frame. At the same time, an offline study was conducted over a small amount of the calibration blends using an in-house moving powder setup to simulate the environment of the feed frame. The model developed offline was able to predict the active ingredient content after a bias correction and used only a fraction of the material. The offline method can serve as a simple method to facilitate calibration development when the time and access to the press is limited. The study takes into consideration, the necessary components of method development and offers perspectives on the validation of an inline process analytics method. Method testing and validation was performed for the inline process analytical technology method. The established Raman method was demonstrated as suitable for the determination of bulk assay of the active ingredient in powders inside the feed frame for use during batch and continuous manufacturing processes.