The Navel Orange Sugar and Acidity Quantitative Prediction Model Optimization Research by Second Generation Wavelet Transform

Abstract

The author researches the impact of the second generation wavelet transform spectrometer data preprocessing navel orange sugar content and acidity Partial Least Squares (PLS) quantitative accuracy of the prediction model. This paper also collects the spectral date of one hundred navel oranges by visible/near-infrared diffuse reflectance detection technology and establishes the navel orange sugar content and acidity PLS prediction model using the sixty navel oranges as the establishing samples. The author contrasts changes of navel orange sugar content and acidity PLS prediction model because the spectral date of navel oranges are processed by the second generation wavelet transform, Finally conclusion: the second generation wavelet transform processing navel orange spectral data can improve the predictive ability of the sugar content and acidity PLS quantitative analysis models.