Author: Song, K. L.

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1 articles found

Determination of Caffeine Content in Coffee Beans Based on Laser Induced BreakdownSpectroscopy

Author: Song, K. L. ; et. al.

Journal: Spectroscopy and Spectral Analysis (2017)

Applications Chemometrics

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.

Area:

Nutrition

Material:

Coffee bean

Elements:

Emmision lines:

Co-author: Song, K. L.

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1 articles found

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

Author: Peng, J. Y. ; et. al.

Journal: Scientific Reports (2017)

Applications Chemometrics

DOI: 10.1038/srep44551

Authors
Kong, W. W. ; Liu, F. ; Shen, T. T. ; Song, K. L. ; Zhu, H. Y.

Keywords

MOISTURE-CONTENT; LIBS; CLASSIFICATION; CHEMOMETRICS; PLANTS

Abstract

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.

Area:

Agriculture

Material:

Tobacco

Elements:

Lines [nm]:

Ca I 422.67 nm Ca I 428.30 nm Ca I 428.94 nm Ca I 429.90 nm Ca I 430.25 nm Ca I 430.77 nm Ca I 431.87 nm Ca I 442.54 nm Ca I 443.57 nm Ca I 457.86 nm Ca I 458.15 nm Ca I 458.60 nm Ca I 487.81 nm Ca I 504.16 nm Ca I 518.89 nm Ca I 526.22 nm Ca I 526.56 nm Ca I 527.03 nm Ca I 558.20 nm Ca I 558.88 nm Ca I 559.45 nm Ca I 559.85 nm Ca I 560.13 nm Ca I 585.75 nm Ca I 610.27 nm Ca I 612.22 nm Ca I 616.22 nm Ca I 616.64 nm Ca I 643.91 nm Ca I 644.98 nm Ca I 646.26 nm Ca I 647.17 nm Ca I 649.38 nm Ca I 671.77 nm Ca I 714.82 nm Ca I 720.22 nm Ca II 854.21 nm Ca II 315.89 nm Ca II 317.93 nm Ca II 370.60 nm Ca II 393.37 nm Ca II 396.85 nm Ca II 849.80 nm Ca II 866.21 nm

Lines [nm]:

Mg I 277.98 nm Mg I 285.21 nm Mg I 382.94 nm Mg I 383.23 nm Mg I 383.83 nm Mg I 389.19 nm Mg I 516.73 nm Mg I 517.27 nm Mg I 518.36 nm

Lines [nm]:

N I 742.36 nm N I 744.23 nm N I 746.83 nm N I 818.49 nm N I 821.63 nm N I 824.24 nm N I 862.92 nm N I 868.03 nm

Lines [nm]:

K I 404.41 nm K I 404.72 nm K I 693.88 nm K I 766.49 nm K I 769.90 nm

Lines [nm]:

Fe I 293.69 nm Fe I 385.99 nm Fe II 253.55 nm

...

Emmision lines:
C I 247.86 nm
Si I 251.61 nm
Fe II 253.55 nm
Mg I 277.98 nm
Mg I 285.21 nm
Si I 288.16 nm
Mn I 292.87 nm
Fe I 293.69 nm
Ca II 315.89 nm
Ca II 317.93 nm
Sc II 364.38 nm
Ca II 370.60 nm
Mg I 382.94 nm
Mg I 383.23 nm
Mg I 383.83 nm
Fe I 385.99 nm
Mg I 389.19 nm
Ca II 393.37 nm
Al I 394.40 nm
Al I 396.15 nm
Ca II 396.85 nm
K I 404.41 nm
K I 404.72 nm
Sr II 407.77 nm
Sr II 421.55 nm
Ca I 422.67 nm
Ca I 428.30 nm
Ca I 428.94 nm
Ca I 429.90 nm
Ca I 430.25 nm
Ca I 430.77 nm
Ca I 431.87 nm
Ca I 442.54 nm
Ca I 443.57 nm
Ca I 457.86 nm
Ca I 458.15 nm
Ca I 458.60 nm
Sr I 460.73 nm
Ca I 487.81 nm
Ca I 504.16 nm
Mg I 516.73 nm
Mg I 517.27 nm
Mg I 518.36 nm
Ca I 518.89 nm
Ca I 526.22 nm
Ca I 526.56 nm
Ca I 527.03 nm
Ca I 558.20 nm
Ca I 558.88 nm
Ca I 559.45 nm
Ca I 559.85 nm
Ca I 560.13 nm
Ca I 585.75 nm
Na I 589.00 nm
Na I 589.59 nm
Ca I 610.27 nm
Ca I 612.22 nm
Ca I 616.22 nm
Ca I 616.64 nm
Ca I 643.91 nm
Ca I 644.98 nm
Ca I 646.26 nm
Ca I 647.17 nm
Ca I 649.38 nm
Ba I 649.88 nm
H I 656.28 nm
Li I 670.79 nm
Ca I 671.77 nm
K I 693.88 nm
Ca I 714.82 nm
Ca I 720.22 nm
N I 742.36 nm
N I 744.23 nm
N I 746.83 nm
K I 766.49 nm
K I 769.90 nm
O I 777.42 nm
N I 818.49 nm
N I 821.63 nm
N I 824.24 nm
O I 844.68 nm
Ca II 849.80 nm
Ca II 854.21 nm
N I 862.92 nm
Ca II 866.21 nm
N I 868.03 nm

Author: Song, K. L.

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Co-author: Song, K. L.

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