A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy

Abstract

This work provides a modified adaptive mutation probability selection genetic algorithm to optimize the SVM model, which improved the accuracy of tea sample classification by LIBS and its recognition accuracy was higher than CV-SVM and PSO-SVM.

Atomic spectrometry update: review of advances in the analysis of clinical and biological materials, foods and beverages

“…This highlights that the technique still requires close supervision and perhaps larger datasets or more advanced statistical techniques. Yao and co-workers 106 investigated an advanced modelling approach for the classification of Chinese tea leaves. The LIBS spectra highlighted Al (396.15 nm), C 2 (516.45 nm), Ca (393.37 nm, 396.84 nm), CN (388.34 nm), Fe (517.46 nm), K (766.49 nm) Mg (279.55 nm, 280.27 nm) and Mn (279.83 nm) as key emission lines that were then referenced to C (247.86 nm) as an IS.…”

A tutorial review on methods of agricultural product sample pretreatment and target analysis by laser-induced breakdown spectroscopy

“…55 The physical properties of the sample may affect the sample preparation process, such as grinding, sieving and pressing, while different chemical properties will generate spectral and non-spectral interferences. [56][57][58] Recently, a sample pyrolysis pretreatment method has been developed and applied to agricultural products. The pyrolysis of agricultural samples is a possible method to improve the physicochemical properties of samples, reduce matrix effects and enhance the LIBS spectral signal.…”

“…55 The physical properties of the sample may affect the sample preparation process, such as grinding, sieving and pressing, while different chemical properties will generate spectral and non-spectral interferences. 56–58…”

Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning

“…A support vector machine (SVM) [43] is a classifier that classifies data based on supervised learning with parameters. This is an efficient method of discrimination, better than other traditional algorithms [2].…”