PLS Regression for NIR: Step-by-Step Guide for Food and Feed Calibration Learn about PLS regression for NIR calibration in food and feed. This guide offers practical steps for building accurate models. <p>Quality managers often ask me why their NIR predictions drift the moment a new crop year hits the floor. Nine times out of ten, the answer traces back to how the PLS calibration was built — not to the instrument itself. Partial Least Squares regression is the mathematical engine behind most commercial NIR calibration models in grain receiving, feed milling, and oilseed processing, and understanding it at a practical level will save you from chasing ghosts in your data.</p> <p>PLS regression is a statistical method that finds the relationship between two data matrices: your NIR spectra and your reference chemistry values. Think of it like teaching a technician to recognize a regular customer's voice on the phone — they don't analyze every individual frequency of sound; they pick up on patterns that consistently identify that person. PLS does the same thing with spectral data, pulling out the patterns — called latent variables — that actually correlate with protein, moisture, fat, or whatever constituent you're chasing.</p> <p>During plant visits, I've watched teams struggle with traditional multiple regression on NIR data, and it always falls apart for the same reason: NIR wavelengths are massively correlated with each other. A change at 1940 nm almost always moves 1960 nm too. PLS handles that collinearity without breaking a sweat, which is exactly why it became the standard method for food and feed calibration.</p> <h2>What is PLS Regression in NIR Calibration?</h2> <h2>How Does PLS Regression Work in Practice?</h2> ← Back to NIR Spectroscopy Blog