Partial least square regression (PLS-R) calibrations based on near infrared (NIR) spectroscopic data were developed in order to predict mechanical and physical properties of agro-based particleboards. The panels were manufactured using Eucalyptus and Pinus wood particles and sugar cane bagasse. The following panel properties were evaluated according to standard methods: modulus of elasticity (MOE), modulus of rupture (MOR), internal bonding (IB) strength, water absorption (WA24H), and thickness swelling (TS24H) after 24 hours of immersion. NIR spectra information was measured on samples cut from each particleboard and correlated with their physical and mechanical properties by PLS-R to build predictive NIR models. The NIR models for IB, WA24H and TS24H presented satisfactory coefficient of determination (0.73; 0.72 and 0.75, respectively.) The key role of resins (adhesives), cellulose, and lignin for NIRS calibrations of mechanical and physical properties of the particleboards is shown. These models can be useful to quickly verify such properties in unknown agro-based particleboards.