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.
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In this review, we examine ‘greener’ routes to nanoparticles of iron oxides, in the recent years; nanotechnology
has emerged as a state-of-the-art and cutting edge technology with multifarious applications in a wide array of fields. Natural products or extracted from natural products. Such as different plant extracts, have been used as reductants, and as capping agents during synthesis. A very easy, efficient and environment-friendly protocol was developed to synthesize green
nanoparticles (NPs) with an aqueous extract of various plant, phenolic compounds extracted from plants play a major role as
a non-toxic reducing and capping agents for nanoparticle. Nanoparticles and their compounds are known to exert a strong
inhibitory and microbial activity on bacteria, viruses, and fungi. In today's world, because of the epidemic of infectious diseases caused by different pathogenic bacteria and the development of antibiotic resistance. Green synthesis, characterization,
and application of nanoparticles (NPs) are becoming an important challenge in nanotechnology. Green synthesis of nanoparticles is made in large quantities worldwide for a wide range of applications. This technique is very safe and environmentally friendly.
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.
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