Data fusion of near-infrared and mid-infrared spectra for identification of rhubarb

• Unofficial rhubarbs were distinguished using spectroscopy and chemometrics.
• Wavelet Compression and iPLS were used for mid-level data fusion.
• Classification accuracies of PLS-DA, SIMICA, SVM and ANN were compared.
• The use of data fusion strategies improved the classification model.

Rhubarb has different medicinal efficacy to official rhubarb and may affect the clinical medication safety. In order to guarantee the quality of rhubarb, we established a method to distinguish unofficial rhubarbs. 52 official and unofficial rhubarb samples were analyzed using near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy for classification. The feature vectors, which were selected by wavelet compression (WC) and interval partial least squares (iPLS) from NIR, MIR spectra, were fused together for identifying rhubarb samples. Partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogies (SIMCA), support vector machine (SVM) and artificial neural network (ANN) were compared for classifying rhubarb. The use of data fusion strategies improved the classification model and allowed correct classification of all the samples.

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