Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers. (June 2018)

• Record Type: Journal Article
• Authors: Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying
• Is Part Of: Optics & laser technology. Volume 102(2018)
• Page Start: 233
• Page End: 239
• Publication Date: 2018-06
• Subjects: Laser-induced breakdown spectroscopy -- Tissue discrimination -- Ablation of tissue -- Multivariate analysis -- k nearest neighbor -- Support vector machine; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2018.01.028
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: LIBS plus multivariate analysis is used for discrimination of fresh soft tissues. PCA, kNN and SVM classifiers are used to build discrimination models. Fat, skin and muscle tissues can be discriminated with excellent performances. Highly similar muscle tissues can be discriminated with acceptable performances. LIBS could be used for diagnosis of tissues suffering early lesions and abnormalities. Abstract: In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.