Zeghoud Soumeia, Rebiai Abdelkrim, Hemmami Hadia, Ben Seghir Bachir, Elboughdiri Noureddine, Ghareba Saad, Ghernaout Djamel, Abbas Nadir
Laboratory Valorization and Technology of Saharan Resources (VTRS), University of El-Oued, P.O. Box 789, El-Oued 39000, Algeria.
Laboratory of Industrial Analysis and Materials Engineering (LAGIM), University 8 May 1945, P.O. Box 401, Guelma 24000, Algeria.
ACS Omega. 2021 Feb 5;6(7):4878-4887. doi: 10.1021/acsomega.0c05816. eCollection 2021 Feb 23.
Bee pollen collected by honeybees () is one of the bee products, and it is as valuable as honey, propolis, royal jelly, or beebread. Its quality varies according to its geographic location or plant sources. This study aimed to apply rapid, simple, and accurate analytical methods such as attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and high-performance liquid chromatography (HPLC) along with chemometrics analysis to construct a model aimed at discriminating between different pollen samples. In total, 33 samples were collected and analyzed using principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least squares regression (PLS) to assess the differences and similarities between them. The PCA score plot based on both HPLC and ATR-FTIR revealed the same discriminatory pattern, and the samples were divided into four major classes depending on their total content of polyphenols. The results revealed that spectral data obtained from ATR-FTIR acquired in the region (4000-500 cm) were further subjected to a standard normal variable (SNV) method that removes scattering effects from spectra. However, PCA, HCA, and PLS showed that the best PLS model was obtained with a regression coefficient ( ) of 0.9001, root-mean-square estimation error (RMSEE) of 0.0304, and root-mean-squared error cross-validation (RMSEcv) of 0.036. Discrimination between the three species has also been possible by combining the pre-processed ATR-FTIR spectra with PCA and PLS. Additionally, the HPLC chromatograms after pre-treatment (SNV) were subjected to unsupervised analysis (PCA-HCA) and supervised analysis (PLS). The PLS model confers good results by factors ( = 0.98, RMSEE = 8.22, and RMSEcv = 27.86). Prospects for devising bee pollen quality assessment methods include utilizing ATR-FTIR and HPLC in combination with multivariate methods for rapid authentication of the geographic location or plant sources of bee pollen.
蜜蜂采集的蜂花粉（）是蜂产品之一，其价值与蜂蜜、蜂胶、蜂王浆或蜂粮相当。其质量因地理位置或植物来源而异。本研究旨在应用衰减全反射傅里叶变换红外光谱法（ATR-FTIR）和高效液相色谱法（HPLC）等快速、简单且准确的分析方法，并结合化学计量学分析，构建一个旨在区分不同花粉样品的模型。总共收集了33个样品，并使用主成分分析（PCA）、层次聚类分析（HCA）和偏最小二乘回归（PLS）进行分析，以评估它们之间的差异和相似性。基于HPLC和ATR-FTIR的PCA得分图显示出相同的判别模式，并且根据多酚的总含量将样品分为四大类。结果表明，从ATR-FTIR在（4000-500 cm）区域获得的光谱数据进一步采用标准正态变量（SNV）方法，该方法可消除光谱中的散射效应。然而，PCA、HCA和PLS表明，最佳的PLS模型的回归系数（）为0.9001，均方根估计误差（RMSEE）为0.0304，交叉验证均方根误差（RMSEcv）为0.036。通过将预处理后的ATR-FTIR光谱与PCA和PLS相结合，也能够区分这三个物种。此外，预处理（SNV）后的HPLC色谱图进行了无监督分析（PCA-HCA）和有监督分析（PLS）。PLS模型通过各项因素（= 0.98，RMSEE = 8.22，RMSEcv = 27.86）给出了良好的结果。设计蜂花粉质量评估方法的前景包括将ATR-FTIR和HPLC与多变量方法结合使用，以快速鉴定蜂花粉的地理位置或植物来源。
