PMID- 18975809
OWN - NLM
STAT- MEDLINE
DCOM- 20100621
LR  - 20131121
IS  - 1000-0593 (Print)
IS  - 1000-0593 (Linking)
VI  - 28
IP  - 8
DP  - 2008 Aug
TI  - [Fast detection of sugar content in fruit vinegar using NIR spectroscopy].
PG  - 1810-3
AB  - For the fast and exact detection of sugar content of fruit vinegar, near infrared 
      (NIR) spectroscopy technique combined with least squares support vector machines 
      (LS-SVM) algorithm was used to build the prediction model of sugar content in the 
      present research. NIR spectroscopy is a nondestructive, fast and accurate 
      technique for the measurement of chemical compo nents based on overtone and 
      combination bands of specific functional groups. The pivotal step for 
      spectroscopy technique is how to extract quantitative data from mass spectral 
      data and eliminate spectral interferences. Principal component analysis (PCA) is 
      a method which has been widely used in the spectroscopic analysis, and LS-SVM is 
      a new data mining algorithm developed from the machine learning community. In the 
      present study, they were used for the spectroscopic analysis. First, the near 
      infrared transmittance spectra of three hundred samples were obtained, then PCA 
      was applied for reducing the dimensionality of the original spectra, and six 
      principal components (PCs) were selected according the accumulative reliabilities 
      (AR). The six PCs could be used to replace the complex spectral data. The three 
      hundred samples were randomly separated into calibration set and validation set. 
      Least squares support vector machines (LS-SVM) algorithm was used to build 
      prediction model of sugar content based on the calibration set, then this model 
      was employed for the prediction of the validation set. Correlation coefficient 
      (r) of prediction and root mean square error prediction (RMSEP) were used as the 
      evaluation standards, and the results indicated that the r and RMSEP for the 
      prediction of sugar content were 0.9939 and 0.363, respectively. Hence, PCA and 
      LS-SVM model with high prediction precision could be applied to the determination 
      of sugar content in fruit vinegar.
FAU - Wang, Li
AU  - Wang L
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 
      310029, China.
FAU - Li, Zeng-fang
AU  - Li ZF
FAU - He, Yong
AU  - He Y
FAU - Liu, Fei
AU  - Liu F
LA  - chi
PT  - English Abstract
PT  - Journal Article
PL  - China
TA  - Guang Pu Xue Yu Guang Pu Fen Xi
JT  - Guang pu xue yu guang pu fen xi = Guang pu
JID - 9424805
RN  - 57-50-1 (Sucrose)
RN  - Q40Q9N063P (Acetic Acid)
SB  - IM
MH  - Acetic Acid/*chemistry
MH  - Algorithms
MH  - *Fruit/chemistry
MH  - Models, Chemical
MH  - Principal Component Analysis/methods
MH  - *Spectroscopy, Near-Infrared/methods
MH  - Sucrose/*analysis
MH  - Time Factors
EDAT- 2008/11/04 09:00
MHDA- 2010/06/22 06:00
CRDT- 2008/11/04 09:00
PHST- 2008/11/04 09:00 [pubmed]
PHST- 2010/06/22 06:00 [medline]
PHST- 2008/11/04 09:00 [entrez]
PST - ppublish
SO  - Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Aug;28(8):1810-3.