MOISTURE-CONTENT; LIBS; CLASSIFICATION; CHEMOMETRICS; PLANTS

Tobacco mosaic virus (TMV) is one of the most devastating viruses to crops, which can cause severe production loss and affect the quality of products. In this study, we have proposed a novel approach to discriminate TMV-infected tobacco based on laser-induced breakdown spectroscopy (LIBS). Two different kinds of tobacco samples (fresh leaves and dried leaf pellets) were collected for spectral acquisition, and partial least squared discrimination analysis (PLS-DA) was used to establish classification models based on full spectrum and observed emission lines. The influences of moisture content on spectral profile, signal stability and plasma parameters (temperature and electron density) were also analysed. The results revealed that moisture content in fresh tobacco leaves would worsen the stability of analysis, and have a detrimental effect on the classification results. Good classification results were achieved based on the data from both full spectrum and observed emission lines of dried leaves, approaching 97.2% and 88.9% in the prediction set, respectively. In addition, support vector machine (SVM) could improve the classification results and eliminate influences of moisture content. The preliminary results indicate that LIBS coupled with chemometrics could provide a fast, efficient and low-cost approach for TMV-infected disease detection in tobacco leaves.

Chemometrics; Classification; Laser-induced breakdown spectroscopy; Variety; Walnut powder;JUGLANS-REGIA L.; SUPPORT VECTOR MACHINE; CLASSIFICATION; LIBS; CHEMOMETRICS; VARIABILITY; EMISSION; SAMPLE; OILS

Rapid classification of varieties of walnut powder is important to protect consumers against potential fraud because different varieties of walnut powder vary dramatically in quality, taste, and price. Laser-induced breakdown spectroscopy (LIBS) is a promising technology for on-line application that has the advantages of no or little sample preparation, fast response (usually seconds to minutes), and remote detection capability. In this research, LIBS and multi-variable analysis methods were applied to classify varieties of walnut powder for the first time. Before LIBS analysis, a fairly flat surface was provided to reduce fluctuation, and no other sample preparation was needed. After acquiring the LIBS spectra, principal component analysis (PCA) was used to make qualitative discrimination, and classification models were developed with partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM). It was shown that three origins as well as different varieties from the same origin could be differentiated successfully by LIBS and chemometrics, while the discrimination of six different varieties with a single ""global"" model was not very successful. SVM could help to handle complex LIBS dataset, which improved the classified rate. The presented results provide the first proof-of-principle data for on-line application of the LIBS technique for classification of varieties of walnut powder.

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.

Laser-induced breakdown spectroscopy; Matcha; Green tea powder; Linear discriminant analysis (LDA);LIBS

The feasibility of using laser-induced breakdown spectroscopy (LIBS) combined with chemometrics methods for fast identification of matcha and green tea powder was investigated in this paper. The main differences between matcha and green tea powder are varieties of tea plants, cultivation management, growth time and processing technology. LIBS spectra between 230 to 880 nm of matcha produced by different manufacturers and green tea powder made with different fixation methods were selected and min-max normalization was the measure for preprocessing. Characteristic wavelengths were selected according to the X-variables loadings on the basis of principal component analysis (PCA), and then linear discriminant analysis (LDA) models were built based on characteristic wavelengths. The results showed that the LDA model based on characteristic wavelengths could identify matcha and green tea powder rapidly. Four characteristic wavelengths belong to C(I) 247. 94 nm, Mg(II) 279. 60 nm, Ca(II) 393. 45 nm and Fe(II) 766. 68 nm. The accuracy of the calibration and the prediction set all reached 100%. Laser-induced breakdown spectroscopy could accurately identify matcha produced by different manufacturers and green tea powder made with different fixation methods.