NIR Calibration Models for Food & Feed: 7 Practices to Prevent Drift and Failed Predictions Build NIR calibration models that deliver consistent results in food and feed. Covers sample diversity, PLS development, validation, and ongoing maintenance. <p>A feed mill in Ukraine had a protein model performing beautifully through autumn harvest — then January arrived, the moisture profile of incoming corn shifted, and prediction accuracy collapsed inside a week. The lab spent two months rebuilding what should have been built once, properly, with summer and winter samples in the original set. That's not a calibration problem. That's a sample set problem, and it's the single most common reason I get called in to fix a model that's gone sideways.</p> <p>The practices below separate calibrations that hold up on the production floor from those that demand constant rework. Sample diversity, reference data quality, model development, validation, and ongoing maintenance — get these right once and your model earns its keep for years.</p> <p>Your calibration is only as strong as the samples sitting behind it. That means pulling material from different batches, seasons, suppliers, and production conditions — not whatever happened to be on the receiving deck the week the project started. A wheat operation should include samples from multiple growing regions and harvest periods so the model accounts for both regional and seasonal variation.</p> <h2>Building a Solid Calibration Set</h2> <h2>Sample Diversity: The One Thing That Kills a Calibration</h2> ← Back to NIR Spectroscopy Blog