Determination of Luzhou-flavor liquor ages by three-dimensional fluorescence spectroscopy combined with NMF

Chao-qun Ma, Rui-yu Xu, Guo-qing Chen, Zhuo-wei Zhu

Optoelectronics Letters ›› , Vol. 14 ›› Issue (6) : 452-456.

Optoelectronics Letters ›› , Vol. 14 ›› Issue (6) : 452-456. DOI: 10.1007/s11801-018-8123-9
Article

Determination of Luzhou-flavor liquor ages by three-dimensional fluorescence spectroscopy combined with NMF

Author information +
History +

Abstract

The determination of Luzhou-flavor liquor ages is carried out by three-dimensional fluorescence spectroscopy combined with non-negative matrix factorization (NMF). 37 samples of aged liquors with weighted ages of 15, 20 and 25 years were prepared by blending three Luzhou-flavor original base liquors with storage ages of 10, 20 and 30 years in different proportions. The fluorescence spectra of the samples were measured, and then factorized into basis matrix and coefficients matrix by multiplicative iterative NMF. The fluorescence spectra, reconstructed from the basis matrix, are similar to the original spectra. The coefficients matrix is taken as the input of support vector machine (SVM) to establish a prediction model for the determination of liquor ages. Compared with the principal component analysis, the prediction model based on SVM has a predicted accuracy better than 91.7%. This method can provide helps for the market supervision on the aged liquors.

Cite this article

Download citation ▾
Chao-qun Ma, Rui-yu Xu, Guo-qing Chen, Zhuo-wei Zhu. Determination of Luzhou-flavor liquor ages by three-dimensional fluorescence spectroscopy combined with NMF. Optoelectronics Letters, , 14(6): 452‒456 https://doi.org/10.1007/s11801-018-8123-9

References

[1]
YaoF., YiB., ShenC., TaoF., LiuY., LinZ., XuP.. Scientific Reports, 2015, 5: 9553
CrossRef Google scholar
[2]
HanS., ZhangW., LiX., LiP., LiuJ.. Food Analytical Methods, 2016, 9: 2194
CrossRef Google scholar
[3]
ZhuS. M., XuM. L., RamaswamyH. S., YangM. Y., YuY.. Scientific Reports, 2016, 6: 30273
CrossRef Google scholar
[4]
ChengP., ZhouW., BurrG. S., FuY., FanY., WuS.. Scientific Reports, 2016, 6: 38381
CrossRef Google scholar
[5]
DingX., WuC., HuangJ., ZhouR.. Journal of Food Science, 2015, 80: 2373
CrossRef Google scholar
[6]
DingX., WuC., HuangJ., ZhouR.. LWT–Food Science and Technology, 2016, 66: 124
[7]
YangT., LiG., ZhuangM.. Liquor Making, 2008, 35: 33
[8]
XuM. L., YuY., RamaswamyH. S., ZhuS. M.. Scientific Reports, 2017, 7: 39671
CrossRef Google scholar
[9]
XuZ.. Liquor–Making Science and Technology, 2008, 2: 90
[10]
MaC., ChenG., GaoS., ChenC., ShiY., GuL.. Optoelectronics Letters, 2011, 7: 158
CrossRef Google scholar
[11]
ZhuZ., QueL., WuY., ChenG., XuR., ZhuT.. Chinese Journal of Lasers, 2015, 42: 0615002
CrossRef Google scholar
[12]
LeeD. D., SeungH. S.. Learning the parts of objects by non–negative matrix factorization, Nature, 1999, 401: 788
[13]
LiuY., LiaoY., TangL., TangF., LiuW.. Neurocomputing, 2016, 173: 224
CrossRef Google scholar
[14]
GuillametD., VitriaJ., SchieleB.. Pattern Recognition Letters, 2003, 24: 2447
CrossRef Google scholar
[15]
HoyerP. O.. Journal of Machine Learning Research, 2004, 5: 1457
[16]
EggermontP. P. B.. Linear Algebra and its Applications, 1990, 130: 25
CrossRef Google scholar
[17]
KroonenbergP. M., de LeeuwJ.. Psychometrika, 1980, 45: 69
CrossRef Google scholar
[18]
ZhangL., ZhouW.. Journal of Computational and Applied Mathematics, 2006, 196: 478
CrossRef Google scholar
[19]
CortesC., VapnikV.. Support–vector networks, Machine Learning, 1995, 20: 273
[20]
GrimmK. J., MazzaG. L., DavoudzadehP.. Structural Equation Modeling A Multidisciplinary Journal, 2017, 24: 246
CrossRef Google scholar

This work has been supported by the National Natural Science Foundation of China (No.61378037), the Fundamental Research Funds for the Central Universities (Nos.JUSRP51628B and 1142050205180920), and the National First-class Discipline Program of Food Science and Technology (No.JUFSTR20180302).

Accesses

Citations

Detail

Sections
Recommended

/