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| Study on NIR detection non-linear model of soluble sugar in citrus leaves |
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| DOI：10.16768/j.issn.1004-874X.2016.11.007 |
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| Abstract: |
| In order to supervise the nutrional elements of citrus leaves，the soluble sugars in the leaves of citrus
were analyzed. Combined with back propagation neural network（ BPNN） and least squares support vector machine
（LS-SVM），quantitative analysis of the nonlinear model using near infrared spectroscopy was developed，at the
same time，data were compressed using principal component analysis（ PCA），the effective wavelength bands were
screened by Uninformative variable elimination（ UVE） algorithm and Successive projections algorithm（ SPA）.
These methods were adopted to optimize the input variables of the model，which improved the detection accuracy.
And spectra processing methods included Savitzke-Golay smoothing（ S-G），multiple scatter correction（ MSC），
derivative and baseline correction（ Baseline） and the combinations of these methods for data transformation，the
best method for establishing models was determined. The MSC was adopted to eliminate baseline drift and amplify
characteristic information，meanwhile amplify high frequency noise，which can be eliminated by 2th derivative.
And smoothing was adopted to eliminate the interference noise and to make the spectrum smoother. It was concluded
that the processing method was the best. The results showed that wavelength selection played an important role in
optimization model，and improved the speed of computation. The effect of model optimization by the model PCA was most obvious and the maximum of correlation coefficient（ Rp） of soluble sugar reached 0.91，the minimum of
the root mean square error of prediction（ RMSEP） reached 4.82. The results showed that the model accuracy and
robustness were significantly improved，the prediction model could meet the requirement of quantitative detection
after optimizing the input variables. Therefore，the prediction model has certain feasibility. |
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