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

Abstract

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. 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. 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.