基于近红外光谱荞麦淀粉、蛋白质和总黄酮含量测定方法研究

Research on determination method of starch,protein and total flavonoids content in buckwheat by Near-infrared spectroscopy

淀粉、蛋白质和总黄酮是荞麦中重要的营养成分和药理保健功能因子,实现对荞麦内部成分的快速检测对荞麦资源开发、育种和加工等环节具有重要的理论研究意义和应用价值。选用来自不同产地的40个荞麦样品,测定其淀粉、蛋白质和总黄酮含量,采用FieldSpec 3光谱仪采集了其光谱信息。将40个样本随机分成了预测集和验证集,其中预测集30个,验证集10个。通过比较不同预处理方法,利用主成分分析法和人工神经网络相结合的方法分别建立了荞麦淀粉、蛋白质和总黄酮含量的预测模型,并进行了验证。结果表明,首先经平滑与多元散射校正预处理,再利用主成分分析法和人工神经网络相结合的方法分别建立的荞麦淀粉含量的预测模型预测值和测量值相关性高,相关度为0.8719,相对误差小,为1.82%,说明该方法预测荞麦淀粉含量是可行的。但对荞麦蛋白质和总黄酮含量的预测并不理想,还有待进一步研究。

Starch, proteins and total flavonoids were the vital nutrients and pharmacological function factor in buckwheat, to achieve the fast detection of the buckwheat internal composition had an important theoretical significance and application value for buckwheat breeding, processing and other steps, Forty buckwheat samples from different origins had been selected. The starch, proteins and total flavonoids content of buckwheat were determined,and the near-infrared spectrum of buckwheat had been obtained using FieldSpec 3 near-infrared spectroscopy. Forty samples were randomly divided into the prediction set and validation set with 30 and iO samples respectively. Compared with different pre-processing methods, the prediction models of buckwheat starch proteins and total flavonoids content had been established using the combination method of principal component analysis and artificial neural network,finally the models had been verified.The results showed that the correlation coefficient between the prediction value and measurement value of buckwheat starch content was 0.87ig,and the relative error was smaller and its mean value was 1.82%,the method of the buckwheat starch content prediction was feasible.But the prediction for buckwheat protein and total flavonoids content was not ideal, need to be further studied.