Khouja Mariem, Páscoa Ricardo N M J, Melo Diana, Costa Anabela S G, Nunes M Antónia, Khaldi Abdelhamid, Messaoud Chokri, Oliveira M Beatriz P P, Alves Rita C
Laboratory of Nanobiotechnology and Valorization of Medicinal Phytoresources, Department of Biology, National Institute of Applied Science and Technology, University of Carthage, B.P. 676, Tunis Cedex 1080, Tunisia.
National Research Institute of Rural Engineering, Water and Forests, University of Carthage, B.P. 10, Ariana, Tunis 2080, Tunisia.
Foods. 2022 Dec 6;11(23):3939. doi: 10.3390/foods11233939.
Pine seeds are known for their richness in lipid compounds and other healthy substances. However, the reference procedures that are commonly applied for their analysis are quite laborious, time-consuming, and expensive. Therefore, it is important to develop rapid, accurate, multi-parametric, cost-effective and, essentially, environmentally friendly analytical techniques that are easily implemented at an industrial scale. The viability of using near-infrared (NIR) spectroscopy to analyse the seed lipid content and profile of three different pine species (, and ) was investigated. Moreover, species discrimination using NIR was also attempted. Different chemometric models, namely partial least squares (PLS) regression, for lipid analysis, and partial least square-discriminant analysis (PLS-DA), for pine species discrimination, were applied. In relation to the discrimination of pine seed species, a total of 90.5% of correct classification rates were obtained. Regarding the quantification models, most of the compounds assessed yielded determination coefficients (R) higher than 0.80. The best PLS models were obtained for total fat, vitamin E, saturated and monounsaturated fatty acids, C20:2, C20:1n9, C20, C18:2n6c, C18:1n9c, C18 and C16:1. Globally, the obtained results demonstrated that NIR spectroscopy is a suitable analytical technique for lipid analysis and species discrimination of pine seeds.
松子以其富含脂质化合物和其他健康物质而闻名。然而，常用于其分析的参考方法相当费力、耗时且昂贵。因此，开发快速、准确、多参数、经济高效且本质上环保的分析技术非常重要，这些技术要能在工业规模上轻松实施。研究了使用近红外（NIR）光谱分析三种不同松树品种（、和）种子脂质含量和分布的可行性。此外，还尝试了使用近红外进行品种鉴别。应用了不同的化学计量学模型，即用于脂质分析的偏最小二乘法（PLS）回归和用于松树品种鉴别的偏最小二乘判别分析（PLS-DA）。关于松树种子品种的鉴别，获得了90.5%的正确分类率。对于定量模型，大多数评估的化合物的测定系数（R）高于0.80。总脂肪、维生素E、饱和脂肪酸和单不饱和脂肪酸、C20:2、C20:1n9、C20、C18:2n6c、C18:1n9c、C18和C16:1的最佳PLS模型。总体而言，所得结果表明近红外光谱是一种适用于松子脂质分析和品种鉴别的分析技术。
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