Near-Infrared Model Establishment For Potatoes Some Quality Traits And Application In Potato Breeding

As rapid development of potato industry in China,Potato breeding requires increasingly high.Requires not only high-yielding and disease-resistant,but also for different purposes.With an efficient, rapid detection capability of Near Infrared Spectroscopy,reducing the work of breeders,accelerate the potato breeding and shorten the breeding time,this is particularly important for potato breeding. The research was around three quality indexes which were potato starch content.crude fiber content and potato amylose content,using three different regression techniques and three different scattering processing for223samples of potato flour and525samples of potato starch,create three different near-infrared models of potato,and predicting amylose content.. The results showed that:(1)Near-infrared spectroscopy model testing starch content:,the cross-validation correlation coefficient (1-VR) was0.847,the egression squared (RSQ) was0.871,the standard error of calibration (SEC) was0.563, standard error of validation (SEP) was0.741,and the exactness (SEC/SEP) was0.760.The calibration equation could be used in determination of potato starch content and screening good materials for potato breeding.There was a large space for the development of the model. It can be optimized, and improved the predictive ability gradually.(2)Near-infrared spectroscopy model testing crude fiber content:,the Cross-validation correlation coefficient (1-VR) was0.855,the egression squared (RSQ) was0.865,the standard error of calibration (SEC) was0.449, standard error of validation (SEP) was0.621,and the exactness (SEC/SEP) was0.732.The calibration equation could be used in determination of potato crude fiber content and screening good materials for potato breeding.There was a large space for the development of the model. It can be optimized, and improved the predictive ability gradually.(3)Near-infrared spectroscopy model testing amylose content:,the Cross-validation correlation coefficient (1-VR) was0.822,the egression squared (RSQ) was0.886,the standard error of calibration (SEC) was1.090, standard error of validation (SEP) was0.964,and the exactness (SEC/SEP) was1.131.The calibration equation could be used in determination of potato amylose content and screening good materials for potato breeding.There was a large space for the development of the model. It can be optimized, and improved the predictive ability gradually. (4)In creating three different potato near-infrared models of three times.The Modified Partial Least Square (MPLS) was better than Partial Least Square regression (PLS) and Primary constituents regression(PCR) in Regression part.The Standard Normal Variant and Detrend Only (SNV+Detrend) was better than Standard Normal Variant (SNV) and NONE in Scatter part.(5)In determination of potato amylose content:there were36samples that were more than20%in potato amylose content.The NO.21882was the highest,23.53%,And the highest amylopectin content is NO.21973,84.62%.It help screening good materials of higher amylose content and amylopectin content for potato breeding.accelerate the potato breeding and shorten the breeding time.