Abstract
A broad-band optical sensor analyzer, based on a set of light-emitting diodes (LED), for milk fat and protein analysis has been simulated and optimized using full-spectrum data in the wavelength range 400–1100nm obtained in a designed experiment. Genetic Algorithm (GA) has been adapted to find an optimal set of wavelength intervals to be used for analysis in order to get the best prediction accuracy. Weighting and averaging of the spectral variables within the chosen intervals has been applied to take the LED emission spectra and integrating diode detection into account. Partial least-squares (PLS) regression models built on seven and six selected intervals for fat and protein, respectively, exhibit no performance loss compared to the corresponding full-spectrum models.Suggested approach is universal and can be used to customize any LED-based or similar optical sensor system for a specific analytical problem prior to the construction. The GA-based algorithm of searching optimal de-resolved spectral intervals can be used as a general variable selection method for multivariate calibration.
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