You are searching for recent advanced chemometric methods to get better calibration models for NIR? Methods and algorithms like:

• Artificial Neural Networks (ANN)
• General Regression Neural Networks (GR-NN)
• RBF Neural Networks (RBF-NN)
• Support Vector Machines (SVM)
• Multiway Partial Least Squares (MPLS),
• Orthogonal PLS (OPLS), (O-PLS), OPLS-AA, OPLS-ANN
• R-PLS, UVE-PLS, RUVE-PLS, LOCAL PLS
• Hierarchical Kernel Partial Least Squares (HKPLS)
• Random Forest (RF)
• etc.

and data pre-processing methods like

• Extended Multiplicative Signal Correction (EMSC)
• Orthogonal Signal Correction (OSC)
• Dynamic Orthogonal Projection (DOP)
• Error Removal by Orthogonal Subtraction (EROS)
• External Parameter Orthogonalization (EPO)
• etc.

that are partly available as modules for software packages like Matlab, Octave, R-Project, etc. Why invest a lot of time and money with new tools? Have you tried it really hard to optimize your calibrations with standard chemometrics methods like Partial Least Squares (PLS), Principal Component Regression (PCR) and Multiple Linear Regression (MLR) which are available in all chemometric software packages? Are you sure you have tried all the good rules and optimization possibilities? Get it done right with the compatible standard methods, we are specialized in optimization and development of NIR calibrations, let us help you, give us a try!