Determination of nutritional parameters of bee pollen by Raman and infrared spectroscopy

Bee Pollen as a Source of Biopharmaceuticals for Neurodegeneration and Cancer Research: A Scoping Review and Translational Prospects

Bee Pollen (BP) has many advantageous properties relying on its multitargeting potential, a new tendency in managing many challenging illnesses. In cancer and neurodegeneration, the multiple effects of BP could be of unequaled importance and need further investigation. Although still limited, available data interestingly spotlights some floral sources with promising activities in line with this investigation. Adopting scoping review methodology, we have identified many crucial bioactivities that are widely recognized to individual BP compounds but remain completely untapped in this valuable bee cocktail. A wide range of these compounds have been recently found to be endowed with great potential in modulating pivotal processes in neurodegeneration and cancer pathophysiology. In addition, some ubiquitous BP compounds have only been recently isolated, while the number of studied BPs remains extremely limited compared to the endless pool of plant species worldwide. We have also elucidated that clinical profits from these promising perspectives are still impeded by challenging hurdles such as limited bioavailability of the studied phytocompounds, diversity and lack of phytochemical standardization of BP, and the difficulty of selective targeting in some pathophysiological mechanisms. We finally present interesting insights to guide future research and pave the way for urgently needed and simplified clinical investigations.

Combatting the antigenicity of common ragweed pollen and its primary allergen Amb a 1 with cold atmospheric pressure air plasma

Airborne allergens, especially those originating from various types of pollen, significantly compromise the health and well-being of individuals on a global scale. Here, cold atmospheric pressure plasma (CAP) created in ambient air was used to treat highly allergenic and invasive Ambrosia artemisiifolia pollen. Immunoassays were used to evaluate the impact of CAP on the principal A. artemisiifolia allergen Amb a 1, demonstrating that > 90 % reduction in antigenicity could be achieved. Chemical analyses using Fourier Transform infrared revealed that CAP induced significant alterations to proteins on the surface of pollen grains, resulting in a 43 % increase in the amide I peak area and a 57 % increase in the amide II peak area. These findings were corroborated by Raman and X-ray photoelectron spectroscopy, which indicated that the protein modifications induced by CAP were due to carbonylation and nitration/nitrosylation processes. Beyond protein transformations, CAP also induced notable oxidation and modification of lipid-like compounds, polysaccharides and sporopollenin. Evident transformations at the chemical level translated into morphological changes at the grain surface, manifesting as increased roughness via significant outer-layer etching. These findings underscore the potential of CAP technology as a viable approach for mitigating against the allergenicity of pollen, providing a deeper understanding into the underlying chemical mechanisms.

Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning

Apple ring rot caused by Botryosphaeria dothidea can cause fruit decay during the growth and storage stages of apple fruit. Understanding the infection process and cellular defense response at the cellular micro-level holds immense importance in the field of prevention and control. Consequently, there is a pressing need to develop suitable chemical imaging analysis methods. Here we proposed a label-free, high-throughput imaging method for cellular investigation of apple fruit ring rot infected by Botryosphaeria dothidea, based on confocal Raman microspectroscopic imaging technology combined with multivariate curve resolution-alternating least squares algorithm (MCR-ALS). We conducted Raman measurements on every apple fruit and obtain an image cube. This cube was then unfolded into an augmented matrix in a column-wise manner. We proceeded with simultaneous MCR-ALS analysis, resolving the single-substance spectrum and concentration profile from the mixed signals. Lastly, the accurate and pure molecular imaging of low methoxyl pectin, high methoxyl pectin, cellulose, lignin, and phenols were realized by refolding the resolved concentration data to construct the composition image. Thereafter, we realized the study of the spatial-temporal changes distribution of the above substances in the cuticle and cell wall of green and red apples at different stages of infection. The imaging method proposed in this paper is expected to provide a chemical imaging strategy for studying pathogen infection process and fruit defense response at the cellular level. In addition, by utilizing a fiber-optic probe near-infrared reflection spectrometer in conjunction with machine learning, we developed a rapid and non-destructive classification method. This method allows for the timely identification of apples exhibiting early infection by Botryosphaeria dothidea. Notably, both principal component analysis-quadratic discriminant analysis and support vector machine achieved a classification accuracy of 100%.

Translational Research on Bee Pollen as a Source of Nutrients: A Scoping Review from Bench to Real World

The emphasis on healthy nutrition is gaining a forefront place in current biomedical sciences. Nutritional deficiencies and imbalances have been widely demonstrated to be involved in the genesis and development of many world-scale public health burdens, such as metabolic and cardiovascular diseases. In recent years, bee pollen is emerging as a scientifically validated candidate, which can help diminish conditions through nutritional interventions. This matrix is being extensively studied, and has proven to be a very rich and well-balanced nutrient pool. In this work, we reviewed the available evidence on the interest in bee pollen as a nutrient source. We mainly focused on bee pollen richness in nutrients and its possible roles in the main pathophysiological processes that are directly linked to nutritional imbalances. This scoping review analyzed scientific works published in the last four years, focusing on the clearest inferences and perspectives to translate cumulated experimental and preclinical evidence into clinically relevant insights. The promising uses of bee pollen for malnutrition, digestive health, metabolic disorders, and other bioactivities which could be helpful to readjust homeostasis (as it is also true in the case of anti-inflammatory or anti-oxidant needs), as well as the benefits on cardiovascular diseases, were identified. The current knowledge gaps were identified, along with the practical challenges that hinder the establishment and fructification of these uses. A complete data collection made with a major range of botanical species allows more robust clinical information.

Honeybee pollen assisted biosynthesis of nanogold and its application as catalyst in reduction of 4-nitrophenol

Nowadays, the exploration of natural materials for the production of nanoparticles is of special interest due to its ecofriendly nature. In this paper, we presented the biosynthesis of gold nanoparticles (AuNPs) in a green route by using water extract of pollen from Andean honeybees. Furthermore, AuNPs have been characterized by various techniques and tested for the catalytic reduction of 4-nitrophenol (4-NP). The biosynthesized AuNPs were analyzed using UV-vis spectroscopy, Transmission electron microscopy (TEM), Dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) spectroscopy to confirm their optical properties, stability, surface morphology, and purity. The synthesized AuNPs proved to be well dispersed, spherical, and triangular in shape, with particle sizes ranging from 7 to 42 nm having λmax at 530 nm. Moreover, FTIR suggests the capping of AuNPs with pollen constituents and XRD confirms the crystalline structure of AuNPs. Additionally, prepared AuNPs were demonstrated to be effective in reducing organic pollutant 4-NP to 4-aminophenol (k = 59.17898 × 10-3 min-1, R2 = 0.994). All of these studies have emphasized that AuNPs production can be scale up by using naturally available pollen grains and open up a new perspective for beekeepers.

Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra

Poplar bark and leaves can be an attractive source of salicylates and other biologically active compounds used in medicine. However, the biochemical variability of poplar material requires a standardization prior to processing. The official analytical protocols used in the pharmaceutical industry rely on the extraction of active compounds, which makes their determination long and costly. An analysis of plant materials in their native state can be performed using vibrational spectroscopy. This paper presents for the first time a comparison of diffuse reflectance in the near- and mid-infrared regions, attenuated total reflection, and Raman spectroscopy used for the simultaneous determination of salicylates and flavonoids in poplar bark and leaves. Based on 185 spectra of various poplar species and hybrid powdered samples, partial least squares regression models, characterized by the relative standard errors of prediction in the 4.5-9.9% range for both calibration and validation sets, were developed. These models allow for fast and precise quantification of the studied active compounds in poplar bark and leaves without any chemical sample treatment.

Revealing the Chemical Composition of Birch Pollen Grains by Raman Spectroscopic Imaging

The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.

Bee Bread as a Promising Source of Bioactive Molecules and Functional Properties: An Up-To-Date Review

Bee bread is a natural product obtained from the fermentation of bee pollen mixed with bee saliva and flower nectar inside the honeycomb cells of a hive. Bee bread is considered a functional product, having several nutritional virtues and various bioactive molecules with curative or preventive effects. This paper aims to review current knowledge regarding the chemical composition and medicinal properties of bee bread, evaluated in vitro and in vivo, and to highlight the benefits of the diet supplementation of bee bread for human health. Bee bread extracts (distilled water, ethanol, methanol, diethyl ether, and ethyl acetate) have been proven to have antioxidant, antifungal, antibacterial, and antitumoral activities, and they can also inhibit α-amylase and angiotensin I-converting enzyme in vitro. More than 300 compounds have been identified in bee bread from different countries around the world, such as free amino acids, sugars, fatty acids, minerals, organic acids, polyphenols, and vitamins. In vivo studies have revealed the efficiency of bee bread in relieving several pathological cases, such as hyperglycemia, hyperlipidemia, inflammation, and oxidative stress.

Rapid discrimination and screening of volatile markers for varietal recognition of Curcumae Radix using ATR-FTIR and HS-GC-MS combined with chemometrics

ETHNOPHARMACOLOGICAL RELEVANCE

Curcumae Radix (Yujin) has a long medicinal use history in China, which is used to cure diseases like jaundice, cholelithiasis caused by dampness-heat of gallbladder and liver, and so on. It comes from the dried tuberous roots of C. kwangsiensis (Guiyujin), C. longa (Huangyujin), C. phaeocaulis (Lvyujin) and C. wenyujin (Wenyujin). Though there are differences in chemical compositions and pharmacological activities among the four species of Yujin, they have not been differentiated well in clinical application due to their similar morphological characterizations.

AIM OF THE STUDY

In this study, the four species of Yujin were rapidly and accurately discriminated. The potential volatile markers for varietal recognition were identified.

MATERIALS AND METHODS

Attenuated total reflection fourier transformed infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to rapidly discriminate the four species of Yujin. Headspace-gas chromatography-mass spectrometry (HS-GC-MS) technology coupled with chemometrics was employed to characterize volatile profiling, differentiate species and select potential markers for varietal recognition of Yujin.

RESULTS

By applying PCA (principal components analysis) and HCA (hierarchical cluster analysis), HS-GC-MS realized complete differentiation of the four species of Yujin, while ATR-FTIR only recognized Guiyuijin. Back propagation neural network (BP-NN), KNN (K-nearest neighbor) and LDA (linear discriminant analysis) models based on spectral data achieved 100% discriminant accuracies. Support vector machines (SVM), KNN and PLS-DA (partial least square discriminant analysis) models based on volatile compounds also realized 100% discriminant accuracies. Additionally, the potential volatile markers for varietal recognition of Yujin were screened using PLS-DA, including 2 for Guiyujin, 6 for Lvyujin, 9 for Wenyujin and 13 for Huangyujin.

CONCLUSIONS

The present study developed reliable methods for the varietal discrimination and volatile compounds characterization of Yujin, which will provide references for its quality control and clinical efficacy.

Modeling of Antioxidant Activity, Polyphenols and Macronutrients Content of Bee Pollen Applying Solid-State 13C NMR Spectra

An application of solid ¹³C nuclear magnetic resonance (NMR) spectroscopy for the determination of macronutrients, total polyphenols content, antioxidant activity, N C S elements, and pH in commercially available bee pollens is reported herein. Solid-state ¹³C NMR spectra were recorded for homogenized pollen granules without chemical treatment or dissolution of samples. By combining spectral data with the results of reference analyses, partial least squares models were constructed and validated separately for each of the studied parameters. To characterize and compare the models’ quality, the relative standard errors of prediction (RSEP) were calculated for calibration and validation sets. In the case of the analysis of protein, fat and reducing sugars, these errors were in the 1.8–2.5% range. Modeling the elemental composition of bee pollen on the basis of ¹³C NMR spectra resulted in RSEP_cal/RSEP_val values of 0.3/0.6% for the sum of NHCS elements, 0.3/0.4% for C, 1.8/1.9% for N, and 4.2/6.1% for S quantification. Analyses of total phenolics and ABTS antioxidant activity resulted in RSEP values in the 2.7–3.5% and 2.8–3.8% ranges, respectively, whereas they were 1.4–2.1% for pH. The obtained results demonstrate the usefulness of ¹³C solid-state NMR spectroscopy for direct determination of various important physiochemical parameters of bee pollen.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R²) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R² values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

Direct identification and quantitation of fluorescent whitening agent in wheat flour based on multi-molecular infrared (MM-IR) spectroscopy and stereomicroscopy

Fluorescent brighteners, illegally used to whitening wheat flour, are detrimental to people health. The aim was to establish a rapid and direct method to identify and quantify fluorescent whitening agent OB-1 (FWA OB-1) in wheat flour by using multi-molecular infrared (MM-IR) spectroscopy combined with stereomicroscopy. Characteristic peak profile of FWA OB-1 used as a judgment basis was spatially revealed by stereomicroscopy with group-peak matching of MM-IR at 1614 cm^-1, 1501 cm^-1 and 893 cm^-1 and were further unveiled by the second derivative infrared spectroscopy (SD-IR) and its two-dimensional correlation infrared (SD-2DCOS IR) spectroscopy for higher resolution, and were validated by high-performance liquid chromatography (HPLC). Moreover, a quantitative prediction model based on IR spectra was established by partial least squares 1 (PLS1) (R², 98.361; SEE, 5.032; SEP, 5.581). The developed method was applicable for rapid and direct analysis of FWA OB-1 (low to 10 ppm) in flour with relative standard deviation (RSD) of 5%. The capabilities of MM-IR with spectral qualitative and quantitative analysis would be applicable to direct identification and quantitation of fluorescent whitening agents or other IR-active compounds in powder objects.

Antioxidant Activity in Bee Products: A Review

Bee products have been used since ancient times both for their nutritional value and for a broad spectrum of therapeutic purposes. They are deemed to be a potential source of natural antioxidants that can counteract the effects of oxidative stress underlying the pathogenesis of many diseases. In view of the growing interest in using bioactive substances from natural sources to promote health and reduce the risk of developing certain illnesses, this review aims to update the current state of knowledge on the antioxidant capacity of bee products such as honey, pollen, propolis, beeswax, royal jelly and bee venom, and on the analytical methods used. The complex, variable composition of these products and the multitude of analytical methods used to study their antioxidant activities are responsible for the wide range of results reported by a plethora of available studies. This suggests the need to establish standardized methods to more efficiently evaluate the intrinsic antioxidant characteristics of these products and make the data obtained more comparable.

Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes-A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R² during the validation except for one attribute, which was much higher compared to the independent models built for instruments.