Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain.
Research Institute in Food Nutrition and Food Safety, University of Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), E-08901 Santa Coloma de Gramanet, Barcelona, Spain.
Sensors (Basel). 2018 Dec 18;18(12):4479. doi: 10.3390/s18124479.
The development of a simple HPLC-UV method towards the evaluation of Spanish paprika's phenolic profile and their discrimination based on the former is reported herein. The approach is based on C reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).
本文报道了一种简单的 HPLC-UV 方法的发展，用于评估西班牙辣椒粉的酚类成分，并根据这些成分对其进行鉴别。该方法基于 C 反相色谱法生成特征指纹图谱，并结合线性判别分析（LDA）进行分类。为此，优化了色谱条件，以实现已在辣椒粉中鉴定出的主要酚类化合物的分离。将辣椒粉样品通过超声和离心进行样品提取阶段；优化提取程序和条件以最大限度地产生足够的鉴别指纹。最后，对色谱图进行基线校正，使用快速傅里叶变换（FFT）压缩，然后通过主成分分析（PCA）和 LDA 进行分析，以对辣椒粉样品进行分类。在开发的程序下，共分析了 96 个辣椒粉样品，其中测试子集（n=25）的分类率达到了 100%。
