Study on early rice blast diagnosis based on unpre-processed Raman spectral data.

Traditionally, the rice blast is diagnosed with the naked-eyes. There is an urgent need to provide a method that can identify the early rice blast without symptoms. In the paper, a method for the early rice blast diagnosis based on the Raman spectroscopy was proposed. Considering the compositions of the biological sample are complex, characteristic peaks are severely crossed, the biological fluorescence background and the noise are strong, and the Raman signal is weak. Different data pre-processing methods will lead to different diagnostic accuracies of Raman models, especially for biological samples. This paper proposed a method for modeling a Raman model based on data without pre-processing. In this method, the raw data are decomposed with Empirical Mode Decomposition (EMD) into several Intrinsic Mode Functions (IMF). Then, based on the self-correlation coefficient of the IMFs and the times of the IMFs crossing the zero Raman Intensity line, IMFs are filtered to get the signal components. Taking the characteristic peaks of the β-carotene, the chlorophyll, and the chitin as the initial characteristic variables, the characteristic variables of the signal components were screened based on Successive Projections Algorithm (SPA). Finally, the obtained characteristic variables were used to establish a Partial Least Squares (PLS) regression model for the rice blast classification, and the test classification accuracy was 94.12%, which was higher than that of models based on spectral data pre-processed by Moving Average Smoothing + Baseline offset, Savitzky Golay Smoothing + Baseline offset, Gaussian Filter Smoothing + Baseline offset and the dB5 wavelet, 3-layer decomposition, Stein Unbiased Risk Estimate, the modulus maximum value method +7 points, 3rd-order Polynomial Fitting.