|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|6458143||1361723||2017||7 صفحه PDF||ندارد||دانلود کنید|
â¢Freeze tolerance of seedlings increase after cold acclimation.â¢The freeze tolerance is positively related to the presence of soluble sugar.â¢NIR spectra can monitor variations of soluble sugars in Loblolly pine seedlings.â¢The NIR model for leaf galactose is the most stable and accurate model.
The freeze tolerance of seedlings can be affected by cold acclimation. This acclimation can in turn alter the seedlings absorbance spectra due to variation of soluble sugar content (SSC). If models developed from the absorbance at wavelengths in the near infrared reflectance (NIR) region could provide information concerning the variation in SSC, nursery managers could assess the freeze tolerance of each seedling before planting. The objective of this research was to investigate the potential to use NIR based analysis to study the variation of SSC in Loblolly pine (Pinus taeda L.) seedlings after cold acclimation. Seventy seedlings were frozen using different cold treatments and 10 seedlings were designated as controls. The standard wet chemistry method was employed to collect the original SSC data, including glucose, fructose and galactose. NIR spectroscopy was completed by using a NIR machine, with a range of 10,000â4000Â cmâ1. The results were analyzed using partial least squares regression in an effort to determine the SSC in the leaves, stems and roots. Results showed that the freeze tolerance of seedlings increased after cold acclimation and was positively related to the presence of SSC. After cold acclimation, leaves had the most SSC, and the galactose content was more than that of glucose or fructose in seedlings. Based on the determination coefficient (R2) and the residual predictive deviation (RPD), the predicted results of NIR models were evaluated. The model for leaf galactose (R2Â =Â 0.88 and RPDÂ =Â 2.17) was the most stable and accurate model. This paper demonstrates that NIR coupled with chemometric modeling can be a useful tool in the monitoring of SSC variation and this information could be useful in the prediction of freeze tolerance.
Journal: Agricultural and Forest Meteorology - Volume 232, 15 January 2017, Pages 536-542