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Bee pollen collected by honeybees (Apis mellifera) 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–1) 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 (R 2) 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 (R 2 = 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.

Pistacia Atlantica in folk medicine is used by Algerian traditional healers for treating a wide variety of diseases and conditions including dyspepsia, digestive problems, peptic ulcers, and, in particular, inflammatory diseases. The present study aimed to assess the phytochemical composition, in vitro antioxidant activity (using 2,2-diphenyl-1-picrylhydrazyl (DPPH), ABTS+, and reducing power methods), enzyme inhibitory activity (towards α-amylase and urease), antibacterial activity, and in vivo anti-inflammatory activity of the unripe fruit extracts of Pistacia atlantica collected from different parts of the Djelfa region of Algeria. According to the findings, various aqueous extracts exhibited significant antioxidant and enzymatic activities in all tests, but showed that they have a weak inhibitory effect against all tested bacterial strains. Twenty-one minerals comprising both macro- and microelements (Ba, Br, Ca, Cl, Co, Cr, Cs, Eu, Fe, K, Mg, Mn, Mo, Na, Rb, Sb, Sc, Sr, Th, U, and Zn) were determined using the technique of neutron activation analysis (INAA). The result indicates that the concentration of the mineral element is close to the minimal FAO recommendation. In addition, the result revealed significant anti-inflammatory activities. The data generated can be a valuable source of information for the pharmaceutical industry and medical research. These results suggest that the unripe fruit extracts of Pistacia atlantica have an appropriate potential to be utilized across a wide range of contexts as an agent with multifunctional uses, as well as a natural remedy for other physiological diseases.

Since ancient times, herbal medicines (HM) have played a vital role in worldwide healthcare systems. It is therefore critical that a thorough evaluation of the quality and control of its complicated chemical makeup be conducted, in order to ensure its efficacy and safety. The notion of HM chemical prints, which aim to acquire a full characterization of compound chemical matrices, has become one of the most persuasive techniques for HM quality evaluation during the last few decades. The link between NMR and chemometrics is discussed in this article. The chemometric latent variable technique has been shown to be extremely valuable in inductive studies of biological systems as well as in solving industrial challenges. The results of unsupervised data exploration utilizing main component analysis as well as the multivariate curve resolution, were various. On the other hand, many contemporary NMR applications in metabolomics and quality control are based on supervised regression or classification analyses.