Non-destructive Detection of Total Acid Content in Pear Based on Visible-near Infrared Spectroscopy
Pear as one of the most favored fruit,its total acid content would has a great influnce on pear's taste and quality,so the application of non-destructive assessment of total acid content in pears shows promising prospects.In this study,the near-infrared spectral data of 240 mature pear samples in northern Jiangxi were collected,take 180 random pear samples as the cali-bration set and 60 unknown samples as the prediction set.The study and analysis were conducted using 1401 wavelength points in the range of 400～1800 nm,after eliminating noise at the beginning and end of the spectrum.Original spectral data were pre-processed by SG smoothing method and baseline offset correction method,through the Partial Least Squares Regression math-ematical model to determine the SG smoothing method has the most significant pretreatment of the original spectral;competitive adaptive reweighted sampling(CARS)and successive projections algorithm(SPA)are used to extract spectral characteristic wavelengths,meanwhile,combining Partial Least Squares Regression and Least Square Support Vector Machine analysis meth-ods to establish the prediction model of total acid content,among them,the CARS+LS-SVM prediction model has the best pre-diction effect on the total acid content of pear,the R2p value was 0.901,the RPD value was 2.911.Research shows that visible near-infrared spectroscopy is a method to detect the total acid content of pear,combined with the CARS+LS-SVM prediction model,the quantitative detection of pear total acid content can be realized.
国家科技期刊平台
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