河套蜜瓜是我国西北河套地区独具特色的果品, 一直深受消费者的喜爱。 糖度(sugar content)是衡量蜜瓜品质和成熟度重要指标。 采用Maya 2000pro便携式光谱仪和PR-101ɑ便携式数字折光仪获取“金红宝”蜜瓜光谱信息及糖度值, 研究了不同特征波长提取方法: 逐步多元线性回归(SMLR)、 间隔偏最小二乘法(iPLS)、 反向区间偏最小二乘法(biPLS)以及联合区间偏最小二乘法(siPLS))对蜜瓜样品模型精度和预测结果的影响。 结果表明: 采用biPLS特征波长提取方法将全波段光谱均匀分成20个子区间, PLS因子数为14, 当剔除其中8个子区间, 选择的波长变量数为218时, 得到的biPLS模型最佳, 对应的校正集和预测集的RMSE分别为0.996 1和1.18。 采用biPLS光谱波长筛选方法可以有效地提取蜜瓜糖度的特征波长, 提高建模预测能力, 实现蜜瓜糖度的快速检测。

Hetao muskmelon is a unique fruit in the hetao area of northwest china, which has been loved by consumers. Sugar content is the important indicator of measuring the quality and mature of muskmelons. This research uses Maya 2 000 pro portable spectrometer and PR-101ɑ portable digital refractometer to get spectrum and sugar content values of “jinhongbao” muskmelon, researches the effect of different extraction methods of characteristic wavelength(stepwise multiple linear regression(SMLR), interval partial least squares(iPLS), backward interval partial least squares(biPLS) and synergy interval partial least squares(siPLS)) on model accuracy and prediction results. The results show: using biPLS method on extraction of characteristic wavelength will the full spectrum evenly divided into 20 subintervals, the PLS factors of 14, when removing 8 subintervals, and choosing the wavelength variable numbers of 218, getting the biPLS model is best, RMSE of corresponding calibration and prediction models is 0.996 1 and 1.18. So using the biPLS method of extraction on spectrum wavelength could extract effectively the characteristic wavelengths of melon sugar content, increase the ability of model prediction, and achieve rapid detecting of sugar content about muskmelons.