Selecting optimal wavelength intervals for an optical sensor: A case study of milk fat and total protein analysis in the region 400-1100 nm

Vladislav Galyanin, Anastasiia Melenteva, Andrey Bogomolov

Research output

15 Citations (Scopus)

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 400-1100 nm 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.

Original languageEnglish
Pages (from-to)97-104
Number of pages8
JournalSensors and Actuators, B: Chemical
Volume218
DOIs
Publication statusPublished - 22 May 2015
Externally publishedYes

Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Surfaces, Coatings and Films
  • Condensed Matter Physics
  • Metals and Alloys
  • Materials Chemistry
  • Electrical and Electronic Engineering

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