研究利用近红外(924～1 720 nm)反射光谱预测了洋葱的可溶性固体物含量。 实验选取了三种不同产地和不同采收期的洋葱为样本(268)。 在重复采集光谱数据之后, 榨取对应光谱采集处洋葱块汁, 测定可溶性固体物参考值。 研究对比了Savitzky-Golay平滑、 散射校正和微分处理等预处理方法, 同时基于偏最小二乘回归方法建立了统计模型。 结果表明, 带S-G平滑的微分处理在平滑窗口为32, 跨度为10时效果最佳。 一阶微分比二阶微分的预处理效果要好, 预测复相关系数R2为0.87, 均方根误差RMSEP为2.42 °Brix。 对比显示, 无平滑处理光谱数据散射校正预处理得到的结果最好, 预测复相关系数R2为0.88(RMSEP=2.31 °Brix)。 采用交叉验证得到的PLSR模型预测复相关系数R2为0.90, RMSEP为1.84 °Brix, 其相对分析误差RPD为3。 说明加散射校正处理的近红外反射光谱可用于洋葱的可溶性固体物检测。

Onion soluble solids content (SSC) was detected using near-infrared (924～1 720 nm) reflectance spectra. Three cultivars of onions, harvested at different period, were selected for experiment and the total number of samples is 268. SSC reference value of onion juice was determined using the temperature compensated refractometer. Some pre-processing methods, such as S-G smoothing, scatter correction, and derivation, were compared to establish a statistical model based on partial least squares regression (PLSR) method. The results show that the avitzky-Golay smoothing with window 32 and span 10 is more efficient. The determination correlation coefficient of prediction R2 is 0.87 and root mean square error (RMSEP) is 2.42 ° Brix. Compared to the 2nd derivation, the 1st derivation got better prediction result, but the spectra scatter correction is the best (R2=0.88, RMSEP of=2.31 ° Brix). The optimal prediction (R2 =0.90, RMSEP=1.84 ° Brix and RPD=3) was built based on crossing validation modeling, which shows that infrared reflectance spectroscopy with scatter correction pre-processing is feasible for onions soluble solids detection.