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Li S.,Central South University of forestry and Technology | Zhang X.,Central South University | Li J.,Central South University of forestry and Technology | Shan Y.,Hunan Food Test and Analysis Center | Huang Z.,Central South University of forestry and Technology
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2014

Raman spectroscopy combined with chemometric methods was used to rapidly measure the content of fructose and glucose in honey. Seventy-five authentic honey samples from sixteen floral origins were obtained directly from bee-keepers in ten provinces of China from 2008 to 2010. The samples were stored at 6-8°C in the laboratory before their analysis. Honey were liquefied in a water bath at 55°C and manually stirred to ensure homogeneity before spectral measurements. Spectra of honey samples were recorded using an i-Raman spectrometer (BWS 415-785H, B&W TEK Inc., USA), which was equipped with a fiber-optic Raman probe, a thermoelectric cooled CCD detector with 2048 pixels and a 785 nm laser with a maximum output power of 495 mW in the signal range of 175-2600 cm-1. The instrumental spectral resolution was 3 cm-1. Integration time was 15 s. Seventy-four samples were divided into 55 calibration sets and 19 validation sets by Kennard-Stone algorithm. AirPLS (adaptive iteratively reweighted penalized least squares) was used to correct the baseline of spectroscopy. CARS (competitive adaptive reweighted sampling) was used to screen variables. Thirty-one and forty-six variables were obtained from 1150 variables by CARS for glucose and fructose, respectively. Quantitative calibration models were developed with linear partial least squares (PLS) regression and non-linear support vector machine (SVM) regression, respectively. These models were used to predict the validation set samples. The prediction accuracies obtained from both glucose and fructose were satisfied by PLS model and SVM model. Correlation coefficient (R)of predicted values versus HPLC measured values and root mean square error of prediction (RMSEP) were 0.902 and 1.401 obtained from SVM model for fructose, respectively, which were higher than the values obtained by PLS model (R=0.892, RMSEP=1.604). PLS model's R and RMSEP were 0.968 and 0.669 for glucose, respectively, which were higher than SVM model's values (R=0.933, RMSEP= 1.410). Raman spectroscopy combined with chemometric methods is a rapid and non-destructive method, which can be applied to measure the content of fructose and glucose in honey. Source


Shan Y.,Central South University | Shan Y.,Hunan Food Test and Analysis Center | Zhu X.-R.,Hunan Food Test and Analysis Center | Xu Q.-S.,University of South China | Liang Y.-Z.,Central South University
Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | Year: 2010

A new chemometric method for determining the contents of fat and protein in milk powder was established by using near infrared (NIR) spectroscopy combined with chemometric methods. The calibration and prediction sets were partitioned by Kernard-Stone algorithm. Wavelet transform (WT) was used for de-noising and compressing signals. The radical basis function neural networks (RBFNN) model for the contents of fat and protein was built by combining with the reconstruction spectral signal. Three parameters, i. e., the spread value of RBF network, the wavelet functions, and decomposition levels were discussed in detail. The results show that the precision of the prediction model is the best when wavelet function, compression level and spread value are db2,4, and 3.5 for fat. In the same way, the precision is the best when wavelet functin, compression level and spread value are db8,4, and 6 for protein. Correlation coefficients (Rp) of prediction set for the correction model of fat and protein are 0.990 and 0.994, and root mean square error prediction (RMSEP) is 0.007 or 0.004, respectively. The results also show that the model is easy and robust, and the prediction accuracy is improved by using RBFNN combined with WT for building NIR models. This method is suitable for determining the content of fat and protein in milk powder rapidly and nondestructively. Source


Su D.,Hunan Academy of Agricultural science | Su D.,Central South University | Su D.,Hunan Food Test and Analysis Center | Dai S.,Hunan Agricultural University | And 6 more authors.
Journal of Chinese Institute of Food Science and Technology | Year: 2015

On the basis of the single factors, a phosphorylated modification of citrus pectin was researched by Box-Behnken Design with phosphate content of the product as the response value, the variable of composite phosphoric reagent concentration, temperature, time and pH. The experiment results indicated that the effects of phosphate content on phosphorylation of citrus pectin's primary and secondary factors were reaction temperature > phosphorylation reagent concentration > reaction time > pH; Statistical analysis showed that the obtained model was highly significant (P<0.0001), and at the level of 93.1%, this model can fit the experimental data, it was able to clearer interpret the relationships of content of phosphate products and reaction conditions. After optimization, the optimal preparation conditions are the phosphorylation reagent concentration 0.11 g/mL, the reaction temperature 78℃, reaction time 4.2 h, and pH 8. Under the optimized conditions, it can be included that the phosphate content (1.31±0.03)% (n=3), which were close to the estimated value (1.36%) attained by using optimized process. At the same time, conventional physical and chemical properties of pectin were measured respectively before and after modification. Therefore, the preparation conditions by the Box-Behnken Design was true and reliable, and this condition was able to provide a theoretical basis and some reference significance to the diversification of pectin products and the applications in functional foods or medicines, etc. ©, 2015, Chinese Institute of Food Science and Technology. All right reserved. Source


Xu D.,Central South University | Fan W.,Central South University | Lv H.,Hunan Food Test and Analysis Center | Liang Y.,Central South University | And 4 more authors.
Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy | Year: 2014

The use of wavelength selection before partial least squares regression (PLSR) for simultaneous determination of divalent metal ions, cadmium, zinc and cobalt by UV-Vis spectrometry was investigated in this paper. The number of wavelengths selected by competitive adaptive reweighted sampling (CARS) for cadmium, zinc, and cobalt were 21, 13 and 7, respectively, from the 916 original wavelength points. The analytical system was based on the formation of the complexes with 2-(5-bromo-2-pyridylazo)-5-(diethylamino)phenol (Br-PADAP) in surfactant media. Compared with the results of full spectra calibration, the root mean squared error of prediction (RMSEP) reduced to 0.0110, 0.0098 and 0.0031 for cadmium, zinc and cobalt, respectively. Moreover, by using the selective wavelengths instead of the 916 original wavelengths, the latent variables of PLS models reduced to 3, 3 and 4. The results indicated that the PLS model established by selected wavelength could be used for simultaneous determination of divalent metal ions. © 2013 Elsevier B.V. All rights reserved. Source


Li S.,Central South University of forestry and Technology | Shan Y.,Hunan Food Test and Analysis Center | Zhu X.,Hunan Food Test and Analysis Center | Li Z.,Central South University of forestry and Technology
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

Near infrared spectroscopy combined with chemometrics methods has been used to detect the geographical origin of honey samples. The samples were divided into the training set and the test set by kennard-Stone algorithm. After being pre-treated with first derivative and autoscaling, the spectral data were compressed and de-noised using wavelet transform (WT). The radical basis function neural networks (RBFNN) and partial least squares-line discriminant analysis (PLS-LDA) were applied to develop classification models, respectively. The performances of different wavelet functions and decomposition levels were evaluated in relation to the total prediction accuracy for the test set. For apple honey samples, when wavelet function was db1 and decomposition level was 2, both WT-RBFNN model and WT-PLS-LDA model produced the largest total prediction accuracy of 96.2%. For rape honey samples, when wavelet function was db4 and decomposition level was 1, WT-RBFNN model made the largest total prediction accuracy of 85.7%; while when wavelet function was db9 and decomposition level was also 1, WT-PLS-LDA model got the largest total prediction accuracy of 90.5%; The results indicated that linear WT-PLS-LDA model was more suitable for geographical classification of honey samples than no-linear WT-RBFNN model. Near infrared spectroscopy technique have a potential for quickly detecting geographical classification of honey samples. Source

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