脐橙糖度近红外光谱在线检测数学模型优化研究

目的是优化脐橙糖度近红外光谱在线检测数学模型,提高检测精度.在700.28～933.79 nm光谱范围内,根据建模集样品在不同波长处的变异系数,选择基准波长点,计算样品的反射比光谱.吸光度和反射比光谱,经不同光谱预处理后,分别采用偏最小二乘法(PLS)和最小二乘支持向量回归法(LSSVR),建立脐橙糖度近红外光谱在线检...

Research on optimization of model for detecting sugar content of navel orange by online near infrared spectroscopy

The objective of the present research was to optimize the model of sugar content in navel orange for improving the detection presicion by the online near infrared spectroscopy. The reference wavelength was chosen by coefficient of variation of the different wavelengths in the calibration set in the wavelength range of 700.28 - 933.79 nm. Then the spectra were transformed into ratio specra. The absorbance and ration spectra were pretreated by different preprocessing methods. The models of sugar content were developed by partial least squares (PLS) and least squares support vector regression (LSSVR). The 30 unknown navel orange samples were applied to evaluate the performance of the models. By comparison of the predictive performances, the LSSVR model was the best among the models with the first derivative preprocessing and ration spectra. The correlation coeffiecient (R(P)) of the best model was 0.85, the root mean square error of prediction (RMSEP) was 0.41 Brix. The results suggested that it was feasible to improve the precision of online near infrared spectroscopy detecting sugar content in navel orange by the optimization of reference wavelengths, the first derivative preprocessing and LSSVR.