摘要
以不同产地的石榴汁样品为对象,对其近红外光谱数据进行预处理并通过小波变化处理提取光谱特征,采用遗传算法对支持向量机的三个参数进行优化,建立基于近红外光谱技术与支持向量机的石榴汁中花色苷含量检测模型。结果表明,模型对验证集的均方根误差为0.019 766,决定系数为0.999 2,模型预测性能良好。近红外光谱技术可用于石榴汁中花色苷含量的定量检测。
Pomegranate juices from different origins were served as the sample.Preprocessing method of near infrared spectroscopy data were determined and the feature were extracted by wavelet analysis.The three parameters of support vector machine （SVM）were optimized by genetic algorithms（GA）.The model for determination of anthocyanin content in pomegranate juice by near infrared spectroscopy and support vector machine was established.The result showed that the model had the predication performance,the root mean square error of the validation and the determination coefficients were 0.019 766 and 0.999 2. Near-infrared spectroscopy technology could be used for the quantitative detection of anthocyanin content in pomegranate juice.
出处
《陕西师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第2期99-102,共4页
Journal of Shaanxi Normal University：Natural Science Edition
基金
陕西省自然科学基础研究计划项目(2011JM3011)
关键词
近红外光谱
石榴汁
花色苷
支持向量机
near infrared spectroscopy
pomegranate juice
anthocyanin
support vector machine
