Learn to diagnose NIR calibration drift, outlier alarms, and prediction failures. The NIR Spectroscopy Fundamentals course includes a full troubleshooting module for food, grain, feed, and dairy labs.
When a near-infrared instrument starts producing unexpected results, the difference between a quick fix and a prolonged investigation is knowing where to look first. This page describes the troubleshooting content in the NIR Spectroscopy Fundamentals course — a 32-lesson online program for food, grain, feed, and dairy professionals.
Lesson 31 walks through a systematic diagnostic sequence for calibration failures: checking the reference method first, evaluating instrument performance, reviewing calibration scope, and identifying whether the problem is raw-material-side or model-side. Topics include Mahalanobis distance alarms, H-statistic thresholds, T-statistic bias detection, and outlier leverage in calibration sets. Real-world failure cases include soybean protein variance at grain intake, fat prediction failures during pet food reformulations, and moisture alarms during silage season.
Feed mill QC technicians, grain elevator operators, and dairy lab staff who already operate NIR instruments but lack the statistical framework to diagnose prediction failures independently. The troubleshooting module builds on calibration and validation foundations taught in Weeks 5 and 6 of the course — covering PLS regression, RMSEP, RPD, and cross-validation before moving into field diagnostics. Enroll in the NIR Spectroscopy Fundamentals course — $99 lifetime access.