Open AccessAccuracy and stability improvement for meat species identification using multiplicative scatter correction and laser-induced breakdown spectroscopy
Author(s) -
Yan Chu,
Shisong Tang,
Shi Xiang,
Yang Yu,
Zhongqi Hao,
Yang Min Guo,
Lian Bo Guo,
Yong Feng Lu,
Xianlin Zeng
Publication year - 2018
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.010119
Subject(s) - laser induced breakdown spectroscopy , stability (learning theory) , multiplicative function , spectroscopy , optics , materials science , identification (biology) , laser , analytical chemistry (journal) , biological system , mathematics , physics , chemistry , computer science , chromatography , biology , mathematical analysis , botany , quantum mechanics , machine learning
An efficient method has been developed to identify meat species by using laser-induced breakdown spectroscopy (LIBS). To improve the accuracy and stability of meat species identification, multiplicative scatter correction (MSC) was adopted to first pretreat the spectrum for correction of spectrum scatter. Then the corrected spectra were identified by using the K-nearest neighbor (KNN) model. The results showed that the identification rate improved from 94.17% to 100% and the prediction coefficient of variance (CV) decreased from 5.16% to 0.56%. This means that the accuracy and stability of meat species identification using MSC and LIBS simultaneously improved. In light of the findings, the proposed method can be a valuable tool for meat species identification using LIBS.