Feature extraction of SERS spectrum of honey using Principal Component Analysis

Honey purity can be identified using combinations of Surface-Enhanced Raman Spectroscopy (SERS), digital signal processing and Principal Component Analysis. This paper focuses on the revelation of honey composition and extraction of honey features. The honey was diluted before it was passed to Surface Enhanced Raman Spectroscopy to enhance its spectrum. After the spectrum was processed by removing background interferences, the honey features were then extracted and analyzed using Principal Component Analysis. The significant features were selected for future classification using eigenvalue one criterion. It was found that coefficients 1 to 9 of all honey samples are significant and can be used as input to a classifier.