糖浓度是梨果内部品质的重要指标。 实验测得了梨果的近红外漫反射吸光度谱, 并且对其进行了光谱预处理, 包括多元散射校正(MSC)、 基线校正(baseline correction)、 标准正态变量变换(SNV)和平滑去噪(moving average)。 结果表明, 经过预处理后的吸光度谱在光谱归一化、 噪声消减等方面有着较为明显的优势。 使用偏最小二乘法(PLS)对原始吸光度谱和预处理后的吸光度谱分别进行处理, 得到结论: 应用平滑去噪预处理后的吸光度谱进行预测的准确度优于原始吸光度谱, 得相关系数为0.990 8, 预测标准偏差为0.019 0。

The content of sugar is an important quality index for pears. However, the traditional sugar measurement methods are time-consuming and destructive. In the present study, the authors measured the sugar content of pears using visible and near infrared diffuse reflection spectroscopy. The pretreatment methods of multiplicative scatter correction (MSC), baseline correction, standard normal variate (SNV) transformation, and moving average algorithms were used on the original absorbance spectrum. Results indicate that the absorbance spectra after pretreatment are better than the original absorbance spectra for prediction. Partial least squares (PLS) regression was also used on the original absorbance spectrum and the absorbance spectrum after moving average and baseline correction. It follows that the forecast accuracy of the absorbance spectra after moving average is higher than that of the original absorbance spectra. The models gave good predictions of the sugar content of pears, with corresponding r values of 0.990 8, and standard errors of predictions of 0.019 0.