DOI: 10.3964/j.issn.1000-0593(2017)07-2199-06

Authors
He, Y. ; Liu, F. ; Peng, J. Y. ; Ye, L. H. ; Zhang, C.

Keywords

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

Abstract

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.

DOI: 10.13031/trans.11553

Authors
He, Y. ; Kong, W. ; Liu, F. ; Yu, K. ; Zhang, C.

Keywords

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

Abstract

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.