Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy

Abstract

The coefficients of determination in the external validation (R VAL) ranged from 0.66 to 0.85. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for different quality compounds and showed values that were characteristic of equations suitable for screening purposes. From the study of the MPLS loadings of the first three terms of the different equations for sugars and starch, it can be concluded that some major cell components such as pigments, cellulose, organic acids highly participated in modelling the equations for carbohydrates. © 2017 Society of Chemical Industry.

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

“…As such, future work should aim to include a more even distribution of a large range of starch concentrations to improve the correlation. Further, increased sampling that includes season and variety variability would also allow the development of a more robust model, as has already been developed for ground plant samples . Therefore, it is still worth pursuing the use of intact samples for predicting starch concentration by NIRS, if not for non‐destructive real‐time measurements in the field, then for reducing the sample preparation time of collected samples.…”

“…Further, increased sampling that includes season and variety variability would also allow the development of a more robust model, as has already been developed for ground plant samples. 9,14,21 Therefore, it is still worth pursuing the use of intact samples for predicting starch concentration by NIRS, if not for non-destructive real-time measurements in the field, then for reducing the sample preparation time of collected samples. Collecting NIR spectra from transverse and/or longitudinal sections of the cane wood rather than from the outer surface of the cane wood, as done in this study, may also help improve the correlation.…”

“…Alternative approaches have been explored including reflectance spectroscopy combined with on‐solid iodine complexation and in vivo X‐ray micro‐computed tomography technology . Chemometrics combined with near‐infrared reflectance spectroscopy (NIRS) has the potential to provide a faster and more reliable approach for quantification of starch concentrations in various tissues …”

“…12 Chemometrics combined with near-infrared reflectance spectroscopy (NIRS) has the potential to provide a faster and more reliable approach for quantification of starch concentrations in various tissues. 13,14 NIRS is a powerful technique that has enabled rapid analysis of various compositional parameters in wine grapes, must and grapevine tissues, 13,15,16 grapevine water potential [17][18][19] and starch. Accurate predictive models of starch have been reported for a range of woody tree systems including grapevine, Vitis spp., 8,20,21 73 tree species 22 and Eucalyptus globulus 23 using dried and ground samples.…”

Study on the Volatile Oxidation Compounds and Quantitative Prediction of Oxidation Parameters in Walnut (Carya cathayensis Sarg.) Oil

“…R 2 is the coefficient of determination and is the measure of fitness of the proposed model to the observed data. According to Pomares–Viciana, based on the R 2 , models can be defined as follows: models with a low correlation (0.26< R 2 < 0.49); models that can be used to discriminate between low and high values of the samples (0.50< R 2 <0.64); models that can be used for rough predictions of samples (0.65< R 2 <0.81); models with good correlations (0.82< R 2 <0.90); and models with excellent precision ( R 2 >0.90). Considering this, the R 2 value of 0.99 was quite favorable for quantification model in this research.…”