玉米品种近红外光谱鉴别技术中的参数漂移问题研究

以13个玉米品种鉴别为研究对象，提出了一种解决光谱仪参数漂移问题的有效方法。使用同一台光谱仪分不同时间重复采集数据，用一天数据建模，其余测试，发现不同时间采集的数据有较大偏移，严重时正确识别率仅为7.69%。为此，提出一种有监督学习特征提取的多天联合建模方法，首先挑选具有代表性的多个时间段样本数据共同组成建模集，其次采用PLS+LDA特征提取算法，提取出与仪器参数漂移无关的品种特征信息, 然后采用BPR方法建立品种鉴别模型。实验结果表明，该方法对于不同时间数据的偏移均能有较好的校正效果，得到较高的识别率和稳定性。

Research on the Parameter Drift Problem of Near Infrared Spectra Based Corn Variety Discrimination Technology

Aiming to differentiate 13 varieties of corn, present paper proposes an effective approach to solving the parameter drift problem of spectrum instruments. Remarkable drift has been found among the inter-day data when using the identical spectrum instrument to acquire sample data at different times, modeling with the intra-day data, and testing with the rest. The correct recognition rate is reduced to only 7.69% in the condition of severe drift. To tackle this problem, this paper proposes a supervised feature-based inter-day combination modeling approach, at first, the representative sample data acquired at multiple times will be selected to make up the modeling set, and then the PLS+LDA algorithm will be applied to extract the feature of varieties which is independent on instrument parameter drift, and finally BPR will be used to identify the varieties. The experiment results indicate that this approach is effective to rectify the data drift at different times, can bring higher recognition rate, and also shows its stability in practice.