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Zhu D.,Nation Engineering Research Center for Information Technology in Agriculture | Zhu D.,Center for Monitoring Research | Ma Z.,Nation Engineering Research Center for Information Technology in Agriculture | Ma Z.,Center for Monitoring Research | And 8 more authors.
Sensor Letters | Year: 2010

The detection of apple juice is very important for its quality and safety assurance. The developing of fast, non-invasive detection system is beneficial to the improvement of producing efficiency and cost. In this study, the homemade near infrared (NIR) spectrometer was applied to detect the soluble solid contents (SSC) and conductivity of apple juice, and the performance of homemade NIR analyzer and the feasibility of detecting conductivity by NIR spectra were investigated. Three varieties of apples (totally 120 samples) were collected, including Fuji apple, American green apple, and Chinese green apple. The fresh pellucid apple juice was made and their spectra were measured by a homemade charge coupled device (CCD) NIR spectrometer. The result showed that homemade CCD NIR spectrometer could accurately predict the SSC of apple juice in the wavelength range of 780-1100 nm combined with partial-least square regression. The correlation coefficient of calibration was r = 0.96, the standard deviation of prediction was SEP = 0.45°Brix, the relative of SEP was SEP% = 3.75%. The results indicated that the homemade NIR analyzer applied in this study had good performance, and the predictive ability of model was as good as that obtained by FT-NIR spectrometer. NIR spectra and the conductivity of apple juice had some correlation (r = 0.77), but the prediction accuracy was low. The prediction result was SEP = 0.30, SEP% = 15.14%. From the analysis of regression coefficients of PLS model, it can be concluded that the conductivity and NIR spectra may have some indirect relation through the ionization of organic acid. Copyright © 2010 American Scientific Publishers All rights reserved.


Shi B.,Shanghai University | Shi B.,China National Institute of Standardization | Zhao L.,China National Institute of Standardization | Wang H.,China National Institute of Standardization | And 4 more authors.
Sensor Letters | Year: 2010

The near infrared spectra of apples with peel and peeled were collected by FT-NIR spectrometer. In order to reduce the influence on the model of apple firmness, the useful wavelengths were selected and the outlier samples were deleted. To analyze the difference between the models with peel and peeled, the calibration and prediction samples were divided for ten times and were used to build the ten different firmness models for both peel and peeled. In the models of peeled apple, the correlation coefficient (r) and the relative standard deviation of prediction (RSDp) were all around 0.84 and 12.6%, respectively, indicating that the peeled models were stable. For the peel model, r and RSDp were 0.74-0.86 and 13.5-17.7%, respectively, suggesting that the stability of the peel model was not very good. The t-test for ten groups of RSDp showed that the difference was significant between the peel and peeled models (P < 0.0001). In addition, the results from Magness-Taylor puncture test only represented the firmness of flesh. However, the near infrared diffuse reflectance spectra of peel apples contained the information of pectin substance from both peel and flesh. The pectin substance content was closely related to the apple's firmness. Therefore, the spectra and reference firmness data were not corresponded and the absorbance spectra of peel became the interference for the near infrared spectra of peel apple. It was obtained the reason for the prediction ability of peel apple model for the firmness reducing. Copyright © 2010 American Scientific Publishers All rights reserved.


Tu Z.,China Agricultural University | Tu Z.,China Food Industry Promotion Center | Zhu D.,Nation Engineering Research Center for Information Technology in Agriculture | Ji B.,China Agricultural University | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

Near infrared spectroscopy combined with pattern recognition methods was used to discriminate the unadulterated and adulterated honey samples. Various crude honey samples from different area in China were collected, and the adulterated honey were prepared according to typical adulteration method, adulteration substance and construction in the market. FT-NIR spectrometer was used to measure the trans-reflectance spectra of honey. The differentiation models for adulteration of honey were constructed by four kinds of pattern recognition methods, including partial least squares discriminate analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), error back propagation network (BP-ANN), least-squares support vector machine (LS-SVM). The results showed that four methods could all correctly differentiate honey samples that were adulterated with high fructose syrup and fructose-plus-glucose solutions. For the adulteration of high fructose syrup, the classification accuracy of calibration set was above 95%, and the classification accuracy of prediction set was above 87%. For the adulteration of fructose-plus-glucose solutions, the classification accuracy of both calibration set was above 93%, and the classification accuracy of prediction set was above 84%. Compared with the four kinds of models, it was found that LS-SVM had the best results, the classification accuracy for both calibration set and prediction set were 100% for two kinds of adulteration. The present study indicated that the fast and accurate differentiation of the adulteration of honey by NIR spectra was feasible.


Shi B.,Shanghai University | Shi B.,China National Institute of Standardization | Zhao L.,China National Institute of Standardization | Liu W.,China National Institute of Standardization | And 3 more authors.
Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | Year: 2010

The interior quality of Shanxi 'Fuji' apple including soluble solid content (SSC), sugar content (SC), titrated acidity (TC) and firmness was determined by acousto-optic tunable filter (AOTF) near infrared (NIR) apparatus. The dubitable outlier samples were analyzed by Cook values, Mahalanobis, leverage and studentized residual. In order to avoiding falsely estimating outlier samples, twice-detection diagnosis method was applied to keep more valid samples. The estimated number of outlier samples for SSC, SC, TC and firmness were 11, 11, 11 and 6, respectively. After outlier samples elimination, the correlation coefficient (r) of SSC, SC, TC and firmness models were improved from 0.868, 0.791, 0.443, 0.693 to 0.904, 0.849, 0.501, 0.718, respectively. The RMSEC of SSC, SC, TC and firmness were decreased from 0.882 °Brix, 9.213g/L, 0.805g/L, 0.105MPa to 0.733 ° Brix, 7.300g/L, 0.687g/L, 0.097MPa,respectively. Moreover, the presented models of apple quality became more robust and stable.

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