Signal enhancement evaluation of laser-induced breakdown spectroscopy of extracted animal fats using principal component analysis approach

In this work, principal component analysis (PCA) was utilized to analyze laser-induced breakdown spectroscopy (LIBS) signals of the extracted chicken fat, lamb fat, beef fat, and lard froze using two different freezing methods. The frozen samples were ablated using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with a wavelength of 1064 nm, 170 mJ pulse energy, and 6 ns pulse duration to produce plasma on target surfaces. The samples were ablated using 30–60 shots of the laser beam at different spots. Stronger LIBS signals from the extracted chicken fat and lamb fat were obtained with liquid nitrogen (LN2) method. However, LIBS signals obtained from the freezer freezing method were found to be stronger for extracted beef fat and lard. The PCA was then used to visualize the LIBS spectra of extracted animal fats into a score plot. Data points of each extracted animal fat were divided into three groups representing LIBS spectra collected at the early, middle, and end part of the ablation process. The score plot revealed that the data points of the three groups of frozen extracted animal fats using the LN2 method were more closely clustered than those frozen in the freezer. Good discrimination with 97% of the variance was achieved between the extracted chicken fat, lamb fat, beef fat, and lard using the LN2 method in the three-dimensional score plot. LIBS signals of the extracted animal fats produced from the LN2 method were found to be more stable than those from the freezer method.

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