Facultad de Ciencias Exactas, Naturales y Agrimensura, Univ Nacional del Nordeste, Av Libertad 5450, 3400 Corrientes, Argentina.
J Food Sci. 2012 Sep;77(9):C1018-22. doi: 10.1111/j.1750-3841.2012.02881.x. Epub 2012 Aug 16.
The spectroscopic characterization of different varieties of sunflower seeds based on their oleic acid content is proposed. One hundred fifty samples of sunflower seeds from different places of Argentina were analyzed by near-infrared diffuse reflectance spectroscopy (NIRDRS). Seed samples were grounded and sieved without chemical treatment previous to the analysis. For the characterization, the used multivariate methods were: principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA), and partial least square discriminant analysis (PLS-DA). By using PCA, CA, and LDA, and from the point of view of varieties of sunflower seeds, 2 groups were differentiated, based on the concentration of oleic acid: a low oleic group, which ranged from 15% to 25% w/w oleic acid; and the other one (mid-high oleic varieties) which ranged from 26% to 90% w/w oleic acid. However, by using the PLS-DA, 3 groups were correctly differentiated based on the concentration of oleic acid: low oleic (from 15% to 25% w/w oleic acid); mid oleic (26% to 76% w/w oleic acid); and high oleic (≥ than 77% w/w oleic acid), demonstrating the high classification ability of this method. This multivariate characterization of sunflower seed varieties did not require chromatographic analysis to generate the matrix of concentrations, and only direct measures of NIRDRS spectra were required. This characterization can be useful to quickly know the variety of sunflower seed in the grain market. Practical Applications: This manuscript describes a method to determine 3 varieties of sunflower seeds (high, mid, and low oleic) The advantage of this method is to avoid the use of techniques that require long-time analysis.
基于油酸含量对不同品种葵花籽的光谱特征进行了研究。采用近红外漫反射光谱（NIRDRS）分析了来自阿根廷不同地区的 150 个葵花籽样品。在分析之前，种子样品未经化学处理就被研磨和筛分。在特征化方面，使用了主成分分析（PCA）、聚类分析（CA）、线性判别分析（LDA）和偏最小二乘判别分析（PLS-DA）等多元方法。通过使用 PCA、CA 和 LDA，从葵花籽品种的角度来看，根据油酸浓度将 2 组葵花籽区分开来：一组油酸浓度较低（15%至 25%w/w 油酸），另一组油酸浓度较高（26%至 90%w/w 油酸）。然而，通过使用 PLS-DA，可以根据油酸浓度正确区分 3 组葵花籽品种：低油酸（15%至 25%w/w 油酸）、中油酸（26%至 76%w/w 油酸）和高油酸（≥77%w/w 油酸），证明了该方法具有较高的分类能力。这种葵花籽品种的多元特征化不需要色谱分析来生成浓度矩阵，只需要直接测量 NIRDRS 光谱。这种特征化可以快速了解谷物市场上葵花籽的品种。实际应用：本文描述了一种确定 3 种葵花籽品种（高油酸、中油酸和低油酸）的方法。该方法的优点是避免使用需要长时间分析的技术。
