基于近红外光谱的油茶种子含油量定标模型构建

Models for Determining Oil Contents in Camellia oleifera Seeds by Using Near Infrared Spectroscopy

利用瑞典波通DA7200型固定光栅连续光谱近红外分析仪,对来自不同产地的150份油茶种子样品进行含油量测定。依据其吸收光谱与化学特征分析数据,建立2个种仁含油率模型,并对模型的准确性进行预测评价。结果表明:用整颗油茶种仁建立的模型与常规方法测量结果之间的相关系数为0.88,预测标准偏差为0.91,该模型可用于测定准度要求相对不高而比较珍贵和量少的样品。而用粉碎油茶种仁建立的模型测定效果最好,与常规方法测量结果之间的相关系数为0.98,预测标准偏差为0.33,因此该模型可代替常规测试方法使用。检验结果表明定标模型预测精度高、稳定性较好。近红外光谱技术可用于快速测定油茶种仁含油量,具有很好的应用前景。

In this paper we aim to provide a rapid, simple and accurate method for determining camellia seed oil content, by the DA7200 near infrared apparatus （made in Sweden）. One hundred and fifty seeds that were collected from different origins in Guangdong province were tested. Based on the analysis data of the absorption spectra and the chemical characteristics, two models for kernel oil content determination were established, and then the accuracy of the models was evaluated. The result shows that the model with the whole seed kernel can reached to 0.88 of the correlation coefficient between the prediction and the chemically measured values, and the standard deviation of prediction is in the range of 0.91. Thus this model with the whole seed kernel can be used to measure low quantity and precious samples with the relative low accuracy. The model with the crushed seed kernel can reached to 0.98 of the correlation coefficient between the prediction and the chemically measured values, and the standard deviation of prediction is in the range of 0.33. This model is very well and can directly replace conventional method. The test results show that prediction models have high precision and good stability. The forecast results indicated that near-infrared spectroscopy for rapidly testing the oil content of dry kernel was useful and would have a very good application prospects.