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.

INDUCED BREAKDOWN SPECTROSCOPY; CANEPHORA; ACID; TRIGONELLINE; VARIETIES; EMISSION; CAFFEINE; RATIOS

The elemental detection of green bean of arabica and robusta coffee from Gayo Highland, Aceh-Indonesia, has been identified by using fundamental Nd-YAG Laser at 10 Torr of surrounding air gas pressure for distinguishing the characteristics of both coffees. As the preliminary study, we have detected the elements of K 766.49 nm, Na 588.9 nm, Ca 393.3 nm, CN band at 388.3 nm, N 337.13 nm and C 247.8 nm of both coffees. It is noticed that the order of elements concentration from highest to lowest are Ca>K>CN>Na>N>C for arabica and K>Ca>CN >Na>C>N for robusta. The emission intensity of K 766.49 nm is almost same for both of coffee. However, the emission intensity of Na 588.9 nm is lower in Arabica coffee. To distinguish the Arabica coffee and Robusta Coffee, we take the ratio intensity of K/C, Na/C, CN/C, and Ca/C. It is found that the ratio intensities of CN/C and Ca/C in arabica bean are significantly different with robusta bean. That ratio intensities can be used as a marker to discriminate kind of coffee. We also noted that the arabica green bean is 1.3 harder than robusta green bean. These findings prove that the technique of laser-induced plasma spectroscopy can be used to make rapid identification of elements in coffee and can potentially be applied to measure the concentration of blended coffee for the purpose of authentication.