Laser-induced breakdown spectroscopy; Support vector machine; Principal component analysis; Tissue classification;BIOMEDICAL APPLICATIONS; LIBS; IDENTIFICATION; ELEMENTS; IMPROVEMENT; PROGRESS; PORK; MN

To improve the classification accuracy of fresh meat species using laser. induced breakdown spectroscopy (LIBS), the support vector machine (SVM) and principal component analysis (PCA) were combined to classify fresh meat species (including pork, beef, and chicken). A simple sample preparation to flatten fresh meat by glass slides was proposed. For each meat sample, 150 spectra were recorded and randomly arranged. The first 75 spectra were used to train a model while the others were used for model validation. By analyzing the 49 normalized spectral lines (K, Ca, Na, Mg, Al, H, O, etc.) in the different tissues, the classification model was built. The results showed that the dimensionality of input variables was decreased from 49 to 10 and modeling time was reduced from 89. 11 s to 55. 52 s using PCA, thus improving the modeling efficiency. The mean classification accuracy of 89. 11% was achieved. The method and reference data are provided for further study of fresh meat classification by laser-induced breakdown spectroscopy technique.

Laser induced breakdown spectroscopy; Chinese cabbage; Cadmiun; Variable screening;HEAVY-METALS; LIBS

Heavy metal residue in vegetables is a big concern in the whole world. The aim of this work is to explore the effect of multivariable selection on analyzing Cd in Chinese cabbage polluted in lab by collecting the spectra of laser induced breakdown spectroscopy (LIBS) from the samples. At the same time, the actual Cd content in samples was obtained by anodic stripping voltammetry (ASV). The LIBS spectral range in partial least square (PLS) model was screened by standard normal variable transformation (SNV), first derivative (FD), second derivative (SD) and center treatment (CT) for preprocessing spectra and the optimized method was used for the analysis of interval partial least square (iPLS) and synergy interval partial least square (SiPLS). The results indicated that the method of CT was the best as a comparison with PLS, iPLS and SiPLS. And the intervals of wavelength were 214.72-215.82 nm, 215.88-216.97 nm and 225.08 -226.35 nm by utilizing the optimized SiPLS. Here the root mean square error of cross validation (RMSECV) between real content and predicted ones was 1.487, the root mean squared error of prediction (RMSEP) was 1.094, the correlation coefficient (R) was 0.9942, and the average relative error (ARE) was 11.60%. The results displayed that LIBS could predict Cd in vegetables by multivariable selection of SiPLS and the accuracy could meet the requirement of rapid and green analysis of Cd in vegetables.

Laser-induced breakdown spectroscopy; Instrument; Review;IN-SITU ANALYSIS; INDUCED PLASMA; QUANTITATIVE-ANALYSIS; MICROCHIP-LASER; SPECTROCHEMICAL ANALYSIS; GEOLOGICAL SAMPLES; RAMAN-SPECTROSCOPY; MATRIX CONVERSION; RAPID DETECTION; LIBS SYSTEM

Laser-induced breakdown spectroscopy (LIBS) has become a very attractive technique in the field of chemical analysis. The technique utilizes a laser focused on a small spot to create a micro-plasma on the sample surface. In recent years, LIBS is a frontier analysis technique in spectrometric analysis, with the advantages such as real-time, online and non-contact analysis. The research and development of various instruments based on this technology has attracted great attention of analysts. In this article, we reviewed the research progress of LIBS instrument from aspects of the components and field equipment in recent years, including portable LIBS, handheld LIBS and remote LIBS.