MEAT; PLANT

Meat can be polluted by heavy metals during the feeding of livestock and processing of meat. It is necessary to monitor the concentration of toxic metals in meat. The element chromium (Cr) in pork was selected as a determination target for this work. Fresh pork was polluted in a Cr solution to create a different content level, then dried and pressed into pellets to eliminate the effect of water and improve the stability and sensitivity of laser-induced breakdownspectroscopy (LIBS). The spectra of pressed pellets were collected at optimized LIBS experimental parameters. After that, the real content of samples was obtained by atomic absorption spectroscopy. Characteristic lines Cr I 425.43, Cr I 427.48, and Cr I 428.97 were verified, and a model comparing the LIBS intensity of the line peak and the actual concentration of Cr was constructed. The results showed that the model has better predicted precision and accuracy, especially applying the line Cr I 425.43 for calibration. This work makes it obvious that LIBS has food safety potential for detecting residue of heavy metals in meat. (C) 2016 Optical Society of America

Laser induced breakdown spectroscopy; Rice husk; Coarse rice; Cr;X-RAY-FLUORESCENCE; SPECTROMETRY; CU

To validate the feasibility of laser induced breakdown spectroscopy (LIBS) in detecting Cr in rice of polluted areas of Poyang Lake in Jiangxi province, rice husk samples, coarse rice samples and polished rice samples in the area were selected randomly as targets. In this work, these samples were tested with LIBS and the content of Cr was acquired by flame atomic absorption (AAS) in fresh samples. LIBS spectra showed that Cr I 425. 43 nm, Cr I 427. 48 nm, Cr I 428. 97 nm and abundant mineral elements such as Fe and Si were detected clearly, but they were undetectable directly in coarse rice and polished rice. The results of AAS shows that Cr in rice husk is more than the corresponding coarse rice and the Cr in polished rice does not exceed the limited content of 1 mg . kg(-1) according to qualified national standards GB 2762-2012. The ratio between rice husk and coarse rice is about 78. 95, it displayed the distribution is clear. This shows that compared with coarse rice, the concentration enrichment ratio of Cr in rice husk is higher than the coarse rice. The results demonstrate that it is feasible to detect Cr in rice husk and coarse rice. Therefore, it is helpful to predict the distribution of heavy metals in agricultural products by means of green method without pollution.

DATA NORMALIZATION; DOMINANT FACTOR; CLASSIFICATION; MODEL; LIBS; REGRESSION; SAMPLES; STEEL; RAMAN; NIR

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis. (C) 2017 Optical Society of America

RISK-ASSESSMENT; PLANT MATERIALS; LIBS; PELLETS

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdownspectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics. (C) 2017 Optical Society of America

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.