Origin Tracing of Red Fuji Apple Based on Near Infrared Transmission Spectrum and Various Dimension Reduction Methods
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Abstract:
In order to find a new method of tracing apple origin, in this work, 600 apple samples from three different producing areas of Aksu, Luochuan, and Yantai were used to collect the near-infrared transmission spectra within 590~1250 nm, then spectroscopically corrected spectrum were treated by eight species of spectral pretreatment such as normalization, standard normal variate transformation, multivariate scattering correction, savitzky-golay smoothing, 2nd derivative, mean centering, moving average, 1st derivative. Firstly, established full spectra classification model for pre-processed spectral data showed that 2nd derivative was the best pre-processing method. Secondly, data set preprocessed by 2nd derivative were used to combine 4 different KNN models (Euclidean, correlation, cosine, cityblock) to do pattern recognition, which was found that K-nearest neighbor method (correlation) was the best classification and recognition method. Thirdly, factor analysis, gaussian process latent variable model, linear local tangent space alignment, neighborhood components analysis, neighborhood preserving embedding, diffusion maps, t-distributed stochastic neighbor embedding, landmark isomap, laplacian eigenmaps, locally linear embedding, principal component analysis, linear discriminant analysis were used to reduce the dimension of the spectrum after 2nd derivative pretreatment, and then combining K-nearest neighbor was combined to trace the origin of apple. Results showed that an optimal identification model was obtained by using 2nd derivative-diffusion maps-KNN (correlation). The identification rates for the calibration set and prediction set were 97.3% and 92.3%, respectively. Therefore, the deep learning dimension reduction methods combined with near-infrared transmission spectroscopy could successfully and effectively discriminate the traceability of apple origin.
