摘要
为了快速测定马铃薯干物质含量,利用可见-短波近红外光谱无损检测马铃薯的干物质含量,以207个具有代表性的马铃薯样本作为研究对象,其中115个作为马铃薯切片样本的研究,92个作为完整马铃薯的研究,通过对比两种样本的模型预测效果,探讨可见-短波近红外光谱用于马铃薯干物质含量的完全无损检测的可行性。切片样本光谱数据用Savitzky-Golay(S-G)一阶卷积求导方法预处理,根据局部最大值最小值原则和含氢基团(C-H、O-H)伸缩振动的敏感波段选定了5段特征波长参与建模,模型外部检验决定系数R^2=0.941 6,标准误差RMSE=3.91。完整马铃薯样本光谱数据在Multiplicative Scatter Correction(MSC)基础上使用S-G一阶卷积求导方法预处理,通过选取了线性关系较好的5段波长参与建模。模型外部检验决定系数R2=0.847 5,标准误差RMSE=4.07。结果表明,完整马铃薯样本模型的检测效果虽然没有切片样本效果理想,但仍可以作为实际生产中进行马铃薯干物质含量检验的有效手段。
The utility of VIS-SWIR spectroscopy was assessed as a means of estimating the dry matter(DM)concentration of potato tubers.An attempt has been made to evaluate potato DMC by non-destructive potato samples.A comparative research method has been introduced,in which both sliced and intact tubers of potatoes are employed to assess their DM by using VIS-SWIR.A total of 207 potato tubers were subdivided into two groups:the first group(n=115)was longitudinally cut into around 20-mm-thick slices in the middle part of each tubers,the second group(n=92)was complete potato samples.All those samples were then tested by VISSWIR.The spectral results of sliced potato samples were pretreated by the Savitzky-Golay(S-G)method to increase the signal-tonoise ratio without too much distorting of the original signals.Based on the local principle of maxima and minima,and also the vibration of C-H and O-H bonds,five characteristic wavelengths were selected to build the model,with its determinate coefficient(R2)of 0.9416 and its standard deviation(RMSE)of 3.91.Similarly,the spectral results of complete potato samples were processed by derivative S-G method after pretreated by Multiplicative Scatter Correction(MSC).Also five characteristic wavelengths of better linear relations were chosen to build the model,with its R2 of 0.847 5 and its RMSE of 4.07.The comparative research results suggest,although not 100%correlative,the non-destructive detection of potato quality by VIS-SWIR might a lso be a practical method for bulk potato testing of DM.
作者
陈争光
冯惠妍
尹淑欣
王雪
王淑云
Chen Zhengguang;Feng Huiyan;Yin Shuxin;Wang Xue;Wang Shuyun(College of Information Technology,Heilongjiang Bayi Agricultural University,Daqing 163319;Science and Technology Archives,Institute of Scientific and Technical Information of Heilongjiang Province)
出处
《黑龙江八一农垦大学学报》
2018年第2期47-51,共5页
journal of heilongjiang bayi agricultural university
基金
黑龙江省教育厅科研资助项目(12521370)