The 5 Stats That Actually Matter for NIR Model Evaluation (R² is Not One of Them) Stop relying on R² to evaluate your NIR calibration. Learn RMSEP, RPD, Bias, RMSEC/RMSECV ratio, and what thresholds to actually use. <p>Every time I see a calibration report that leads with R² = 0.98, I brace myself. Not because 0.98 is necessarily wrong — but because R² alone tells you almost nothing useful about whether a model will actually perform in production. I've seen R² = 0.99 models that fell apart on new samples and R² = 0.91 models that ran a grain testing program reliably for eight years. The difference is in the statistics that actually measure what matters.</p> <p>Here are the five statistics I use when evaluating any NIR calibration. I'll also explain why R² doesn't make the list — and why you should be suspicious of any report that leads with it.</p> <p>RMSEP is the most important single number in your calibration report. It measures the average prediction error on an independent validation set — samples the model has never seen during development. The formula is:</p> <h2>1. RMSEP — Root Mean Square Error of Prediction</h2> <h2>2. RPD — Ratio of Performance to Deviation</h2> ← Back to NIR Spectroscopy Blog