Rapid Detection of Glucosinolates in Cauliflower Based on Visible/Near-infrared Spectroscopy
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Abstract:
In order to quickly detect the content of glucosinolates in different varieties of cauliflower (“Songhua” and “Snow-white”), in this experiment, fresh "Songhua" and "Snow-white" cauliflower samples were collected for visible/near-infrared spectrum collection, extraction and analysis. First, five methods, Baseline correction, Standard Nomal Variate transform (SNV), Median Filter (MF), Gaussion Filter (GF) and S-G smoothing (savitzky-golay), were used for the preprocessing analysis of the original spectra. Secondly, methods of extracting feature bands were respectively adopted, namely the Successive projection Algorithm (SPA) and the Regression Coefficient (RC) were used to extract the feature bands. Principal Component Analysis (PCA) was used to extract the Principal components. A Partial Least Squares Regression (PLSR) model was established based on the optimal preprocessing method. The results showed that the MF-PCA-PLS model established by the spectral data of "Songhua" cauliflower was the best, and the calibration set model parameter Rc=0.89, RMSEC=1.23, predictive set model parameters Rp=0.89, RMSEP=0.63.The MF-RC-PLS model based on the spectral data of "Snow-white" cauliflower was optimal, with calibration set model parameters Rc=0.87, RMSEC=1.31, predictive set model parameters Rp=0.73, RMSEP=0.46. It can be seen that the content of glucoside in cauliflower can be detected quickly, nondestructively and accurately by PLSR algorithm combined with visible/near-infrared spectroscopy.
