Universitat Politècnica de Catalunya, Department of Agri-Food Engineering and Biotechnology, Esteve Terrades 8, 08860, Castelldefels, Spain; Agromillora Iberia S.L.U, Center of Initial Materials, Ctra. BV-2247 km. 3, 08770, Sant Sadurní d'Anoia, Spain.
Universitat Rovira i Virgili, Department of Analytical Chemistry and Organic Chemistry, Campus Sescelades, 43007, Tarragona, Spain.
Talanta. 2019 Nov 1;204:320-328. doi: 10.1016/j.talanta.2019.05.105. Epub 2019 Jun 5.
Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.
近红外光谱（NIRS）可以作为一种更快、更经济的替代方法，用于在繁殖过程中筛选苗圃植物的品种混合物，以确保品种纯度并避免在发货批次中出现错误。这项工作的总体目标是开发和优化 NIR 光谱采集方法，以构建稳健的多元判别模型。本研究使用了三种具有农业价值的甜樱桃（Avijor、Guara 和 Pentacebas）品种。研究了变异的来源，包括叶片在树上的位置、同一品种树木之间的差异以及品种水平的差异。研究了三种类型的处理样本。每个分析都包括新鲜叶片、干燥叶片和干燥粉末形式的叶片。还对光谱预处理方法进行了研究，并应用多元方法分析不同因素对分类的影响。这些方法包括主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）和方差分析同时成分分析（ASCA）。结果表明，品种是分类的最重要因素。提供最佳结果的光谱预处理方法是标准正态变量（SNV）、Savitzky-Golay 一阶导数和均值中心化方法的组合。就处理样本的类型而言，在训练集和测试集验证水平下，新鲜和干燥粉末叶片的正确分类百分比最高。本研究代表了将 NIRS 作为识别甜樱桃品种的方法的第一步。
