摘要
为实现鲜枣内部综合品质的在线无损快速检测,利用可见/近红外光谱漫反射技术,针对完熟期壶瓶枣的内部品质,包括含水率、可溶性固形物含量、硬度、可溶性蛋白质含量、维生素C含量5项指标,分别采用竞争性自适应重加权算法(CARS)提取特征波长并建立最小二乘-支持向量机(LS-SVM)预测模型,硬度预测模型的相关系数和均方根误差分别为0.945 2和41.684 9,其余品质预测模型的相关系数均在0.923 0及以上、均方根误差均在3.779 2及以下。在此基础上,对5项品质指标进行了相关性分析,表明在0.01或0.05水平上两两指标间存在极显著或显著的相关性,故采用因子分析法构建了内部综合品质评价指标,建立了CARS-LS-SVM预测模型,结果表明该模型的相关系数和均方根误差分别为0.924 1和6.063 5,预测精度较高。研究表明,所建立的CARS-LS-SVM模型可有效实现鲜枣内部综合品质的评价。
A non-destructive method for on-line determining the internal comprehensive quality of Huping jujube fruit was investigated based on visible/near-infrared reflection spectrum. Moisture content, soluble solid content, firmness, soluble protein content and vitamin C content were respectively used as internal indexes to assess the quality of Huping jujube at full ripe stage. Competitive adaptive reweighted sampling （CARS） was applied to select sensitive wavelengths. Models of the least squares-support vector machines （LS- SVM） were built based on the sensitive wavelengths respectively. The model of firmness showed that the correlation coefficient of prediction was 0. 945 2 and root mean square error of prediction was 41. 684 9. The other four models obtained the better results with the correlation coefficient of each prediction over 0. 923 0 and root mean square error of each prediction from 0. 267 4 to 3. 779 2. Then, the correlation was analyzed between the quality indexes. The results indicated that an extremely significant or a significant correlation was revealed between any two indexes in the P 〈0.01 or P 〈0.05 levels. Therefore, factor analysis was carried out on five internal quality index of fresh jujube to develop the internal comprehensive quality index, and the CARS - LS - SVM model of this index was established. The results indicated that the correlation coefficient of prediction was 0. 924 1 and root mean square error of prediction was 6. 063 5. This research showed that the established CARS - LS - SVM model was effective to realize evaluation of the internal comprehensive quality on fresh jujube. This research provided theoretical basis for on-line, rapid and non-destructive detection on internal comprehensive quality of fresh jujube.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第9期324-329,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31271973)
关键词
鲜枣
内部综合品质
特征提取
可见/近红外光谱
fresh jujube
internal comprehensive quality
feature extraction
visible/near-infraredspectrum
