Prediction of starch reserves in intact and ground grapevine cane wood tissues using near-infrared reflectance spectroscopy.

BACKGROUND: Near-infrared reflectance spectroscopy (NIRS) technology can be a powerful analytical technique for the assessment of plant starch, but generally samples need to be freeze-dried and ground. This study investigated the feasibility of using NIRS technology to quantify starch concentration in ground and intact grapevine cane wood samples (with or without the bark layer). A partial least squares regression was used on the sample spectral data and was compared against starch analysis using a conventional wet chemistry method. RESULTS: Accurate calibration models were obtained for the ground cane wood samples (n = 220), one based on 17 factors (R2 = 0.88, root mean square error of validation (RMSEV) of 0.73 mg g-1 ) and the other based on 10 factors (R2 = 0.85, RMSEV of 0.80 mg g-1 ). In contrast, the prediction of starch within intact cane wood samples was very low (R2 = 0.19). Removal of the cane bark tissues did not substantially improve the accuracy of the model (R2 = 0.34). Despite these poor correlations and low ratio of prediction to deviation values of 1.08-1.24, the root mean square error of cross-validation (RMSECV) values were 0.75-0.86 mg g-1 , indicating good predictability of the model. CONCLUSIONS: As indicated by low RMSECV values, NIRS technology has the potential to monitor grapevine starch reserves in intact cane wood samples. © 2020 Society of Chemical Industry.

Non-structural carbohydrates in woody plants compared among laboratories.

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g(-1) for soluble sugars, 6-533 (mean = 94) mg g(-1) for starch and 53-649 (mean = 153) mg g(-1) for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R(2) = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g(-1) for total NSC, compared with the range of laboratory estimates of 596 mg g(-1). Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.