PLASMA SPECTROSCOPY; ELEMENTAL ANALYSIS; LIBS

Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction, set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.

Laser induced breakdown spectroscopy; Coffee beans; Caffeine; Partial least squares; BP neural network;LIBS

The feasibility of fast detection of caffeine content in coffee beans based on laser induced breakdown spectroscopy (LIES) combined with chemometrics methods was studied. The coffee beans were grinded. 0.5 g of powerd material was transformed into a disk by manual tableting machine. 60 disks of coffee bean material were prepared for LIBS data acquiring. The samples were pretreated by acid wet digestion, and actual caffeine content of each sample was obtained by automic absorption spectrometer (AAS). Baseline correction was applied on the original spectral data to eliminate the negative values. Wavelet transform (WT) was used to reduce the noise, wavelet basis function is Daubechies 5 (db5) and decomposition level is 10. Normalization were employed to deal with variations caused by matrix effects and experimental conditions. The partial least squares (PLS) model on full data appeared to over-fitting. Regression coefficients and principal component analysis (PCA) were used to select characteristic variables, respectively. PLS models and back propagation (BP) neural network model were built by the variables selected. In the PLS model on the variables selected by regression coefficients, correlation coefficient of calibration set (R-c) was 0.96, correlation coefficient of prediction set (R-p) was 0.91. In the PLS model on the variables selected by PCA, R-c=0.94, R-p=0.90. In the BP neural network model on variables selected by PCA, R-c=0.96, R-p=0.96. The characteristic variables selected by two methods correspond to C, H, O, N, Na, Mg, Ca, Fe, Mn. The PLS models on the variables selected by regression coefficients and PCA both performed well on prediction samples. It demonstrated the certain relationship existing between the elements and caffeine content and the selected variables were effective. But the precise relationship bwtween C, H, O, N, Na, Mg, Ca, Fe, Mn and caffeine content needs further study. The BP neural network model on variables selected by PCA performed better than the PLS model, which demonstrated the selected variables were suitable for different modeling methods. The study showed LIBS could be applied to fast determination of caffeine content within coffee bean combined with chemometrics methods. The method of caffeine content detection presented by this study is innovative.