BACKGROUND: Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible-NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates. RESULTS: Visible-NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed. CONCLUSION: The coefficients of determination in the external validation (R2 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.
BACKGROUND: Zucchini fruit plays an important part in healthy nutrition due to its high content of carbohydrates. Recent studies have demonstrated the feasibility of visible-NIRS to predict quality profile. However, this procedure has not been applied to determine carbohydrates. RESULTS: Visible-NIR and wet chemical methods were used to determine individual sugars and starch in zucchini fruits. By applying a principal component analysis (PCA) with NIR spectral data a differentiation between the less sweet versus the sweetest zucchini accessions could be found. For the determination of carbohydrate content effective prediction models for individual sugars such as glucose, fructose, sucrose and starch by using partial least squares (PLS) regression have been developed. CONCLUSION: The coefficients of determination in the external validation (R2 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.
Authors: María Del Carmen García-García; Emilio Martín-Expósito; Isabel Font; Bárbara Del Carmen Martínez-García; Juan A Fernández; Juan Luis Valenzuela; Pedro Gómez; Mercedes Del Río-Celestino Journal: Sensors (Basel) Date: 2022-05-28 Impact factor: 3.847