Pomares-Viciana Teresa, Martínez-Valdivieso Damián, Font Rafael, Gómez Pedro, Del Río-Celestino Mercedes
Department of Genomics and Biotechnology, IFAPA Center La Mojonera, La Mojonera, Almería, Spain.
Department of Food and Health, IFAPA Center La Mojonera, La Mojonera, Almería, Spain.
J Sci Food Agric. 2018 Mar;98(5):1703-1711. doi: 10.1002/jsfa.8642. Epub 2017 Oct 9.
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.
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.
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.
西葫芦果实因其高碳水化合物含量在健康营养方面发挥着重要作用。最近的研究已证明可见 - 近红外光谱法（visible - NIRS）预测品质特征的可行性。然而，该方法尚未应用于测定碳水化合物。
采用可见 - 近红外光谱法和湿化学方法测定西葫芦果实中的单糖和淀粉。通过对近红外光谱数据进行主成分分析（PCA），可以区分甜度较低和甜度最高的西葫芦品种。利用偏最小二乘法（PLS）回归，已建立了用于测定葡萄糖、果糖、蔗糖和淀粉等单糖碳水化合物含量的有效预测模型。
外部验证的决定系数（R VAL）范围为0.66至0.85。不同品质化合物的标准偏差（SD）与预测比率的标准误差（RPD）以及SD与范围（RER）各不相同，且显示出适用于筛选目的方程的特征值。从对糖和淀粉不同方程前三项的MPLS载荷研究中可以得出结论，一些主要的细胞成分，如色素、纤维素、有机酸在碳水化合物方程建模中高度参与。©2017化学工业协会。
