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

Exploring the Botanical Origins of Bee-Collected Pollen: A Comprehensive Historical and Contemporary Analysis

Bee-collected pollen is one of the most important bee products. In order to predict its chemical composition and nutritional value botanical origin of pollen plays a crucial role. This review intended to collect all available data published about botanical origin of pollen collected all around the world. Due to enourmous amount of data and variables nonlinear principal component analysis (NLPCA), by applying Categorical Principal Component Analysis (CATPCA), was conducted in order to try to determine any specifity and/or differences among samples. Also, importance of some plant families/genera/species for bees was monitored. Based on CATPCA results families can serve in order to distinct samples from North/South America. Also, some samples from Europe (Turkey and Serbia) were characterized with presence of some specific families. Genera were excellent tool to distinguish samples from different parts of Brazil as well as Australia, Asia and Africa. Due to high and specific biodiversity pollen samples obtained from Sonoran desert (USA) completely were separated during analysis. This review presents the first attempt to summarize and classify a large number of data about botanical sources of bee-collected pollen.

Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder

Bear bile powder (BBP) is a valuable animal-derived product with a huge adulteration problem on market. It is a crucially important task to identify BBP and its counterfeit. Electronic sensory technologies are the inheritance and development of traditional empirical identification. Considering that each drug has its own specific odor and taste characteristics, electronic tongue (E-tongue), electronic nose (E-nose) and GC-MS were used to evaluate the aroma and taste of BBP and its common counterfeit. Two active components of BBP, namely tauroursodeoxycholic acid (TUDCA) and taurochenodeoxycholic acid (TCDCA) were measured and linked with the electronic sensory data. The results showed that bitterness was the main flavor of TUDCA in BBP, saltiness and umami were the main flavor of TCDCA. The volatiles detected by E-nose and GC-MS were mainly aldehydes, ketones, alcohols, hydrocarbons, carboxylic acids, heterocyclic, lipids, and amines, mainly earthy, musty, coffee, bitter almond, burnt, pungent odor descriptions. Four different machine learning algorithms (backpropagation neural network, support vector machine, K-nearest neighbor, and random forest) were used to identify BBP and its counterfeit, and the regression performance of these four algorithms was also evaluated. For qualitative identification, the algorithm of random forest has shown the best performance, with 100% accuracy, precision, recall and F1-score. Also, the random forest algorithm has the best R² and the lowest RMSE in terms of quantitative prediction.

Biscuits Enriched with Monofloral Bee Pollens: Nutritional Properties, Techno-Functional Parameters, Sensory Profile, and Consumer Preference

Bee pollens are potential functional food ingredients as they contain essential nutrients and a wide range of bioactive compounds. The aim of this study was to investigate the effects of enrichment with monofloral bee pollens on the nutritional properties, techno-functional parameters, sensory profile, and consumer preference of biscuits. Biscuits were prepared according to the AACC-approved method by substituting wheat flour with pollens of rapeseed (Brassica napus L.), phacelia (Phacelia tanacetifolia Benth.) and sunflower (Helianthus annuus L.) at 2%, 5% and 10% levels. The macronutrient composition of the biscuits was determined: crude protein content (Kjeldahl method), crude fat content (Soxhlet extraction), ash content (carbonization), moisture content (drying), carbohydrate content (formula). Their total phenolic content (TPC) and in vitro antioxidant capacity (FRAP, TEAC, DPPH) were determined spectrophotometrically. The colour of the biscuits was measured using a tristimulus-based instrument, and their texture was characterized by using a texture analyser. Sensory profile of biscuits was determined by qualitative descriptive analysis (QDA). The consumer acceptance and purchase intention of the biscuits were also evaluated, based on the responses of 100 consumers. Additionally, an external preference map was created to illustrate the relationship between consumer preference and the sensory profile of the biscuits, and penalty analysis was conducted to identify directions for product development. Phacelia pollen appeared to be the most effective for improving the nutritional quality of biscuits. The addition of phacelia pollen at the 10% substitution level increased the protein content and TPC of the control biscuit by 21% and 145%, respectively. Significant changes (p < 0.05) were also observed regarding the colour and texture of biscuits. The results of the QDA revealed that biscuits containing pollens of different botanical sources have heterogeneous sensory attributes. The biscuit containing sunflower pollen at the 2% substitution level was preferred the most (overall liking = 6.9 ± 1.6), and purchase intentions were also the highest for this product. Based on the results of the present study, it is recommended to use sunflower pollen for developing pollen-enriched foods in the future.

A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS

The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

Protective Effects of PollenAid Plus Soft Gel Capsules’ Hydroalcoholic Extract in Isolated Prostates and Ovaries Exposed to Lipopolysaccharide

Pollen extract represents an innovative approach for the management of the clinical symptoms related to prostatitis and pelvic inflammatory disease (PID). In this context, the aims of the present work were to analyze the phenolic composition of a hydroalcoholic extract of PollenAid Plus soft gel capsules, and to evaluate the extract’s cytotoxic effects, in human prostate cancer PC3 cells and human ovary cancer OVCAR-3 cells. Additionally, protective effects were investigated in isolated prostate and ovary specimens exposed to lipopolysaccharide (LPS). The phytochemical investigation identified catechin, chlorogenic acid, gentisic acid, and 3-hydroxytyrosol as the prominent phenolics. The extract did not exert a relevant cytotoxic effect on PC3 and OVCAR-3 cells. However, the extract showed a dose-dependent inhibition of pro-inflammatory IL-6 and TNF-α gene expression in prostate and ovary specimens, and the extract was effective in preventing the LPS-induced upregulation of CAT and SOD gene expression, which are deeply involved in tissue antioxidant defense systems. Finally, a docking approach suggested the capability of catechin and chlorogenic acid to interact with the TRPV1 receptor, playing a master role in prostate inflammation. Overall, the present findings demonstrated anti-inflammatory and antioxidant effects of this formulation; thus, suggesting its capability in the management of the clinical symptoms related to prostatitis and PID.

Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil

Several socio-economic problems have been hidden by the COVID-19 pandemic crisis. Particularly, the agricultural and food industrial sectors have been harshly affected by this devastating disease. Moreover, with the worldwide population increase and the agricultural production technologies being inefficient or obsolete, there is a great need to find new and successful ways to fulfill the increasing food demand. A new era of agriculture and food industry is forthcoming, with revolutionary concepts, processes and technologies, referred to as Agri-food 4.0, which enables the next level of agri-food production and trade. In addition, consumers are becoming more and more aware about the origin, traceability, healthy and high-quality of agri-food products. The integration of new process of production and data management is a mandatory step to meet consumer and market requirements. DNA traceability may provide strong approach to certify and authenticate healthy food products, particularly for olive oil. With this approach, the origin and authenticity of products are confirmed by the means of unique nucleic acid sequences. Selected tools, methods and technologies involved in and contributing to the advance of the agri-food sector are presented and discussed in this paper. Moreover, the application of DNA traceability as an innovative approach to authenticate olive products is reported in this paper as an application and promising case of smart agriculture.

Sensory Panel Performance Evaluation—Comprehensive Review of Practical Approaches

Sensory assessors determine the result of sensory analysis; therefore, investigation of panel performance is inevitable to obtain well-established results. In the last few decades, numerous publications examine the performance of both panelists and panels. The initial point of any panelist measures are the applied selection methods, which are chosen according to the purpose (general suitability or product-specific skills). A practical overview is given on the available solutions, methods, protocols and software relating to all major panelist and panel measure indices (agreement, discrimination, repeatability, reproducibility and scale usage), with special focus on the utilized statistical methods. The novel approach of the presented methods is multi-faceted, concerning time factor (measuring performance at a given moment or over a period), the level of integration in the sensory testing procedure and the target of the measurements (panelist versus panel). The present paper supports the choice of the performance parameter and its related statistical procedure. Available software platforms, their accessibility (open-source status) and their functions are thoroughly analyzed concerning panelist or whole panel evaluation. The applied sensory test method strongly defines the applicable performance evaluation tools; therefore, these aspects are also discussed. A special field is related to proficiency testing. With the focus on special activities (product competitions, expert panels, food and horticultural goods), practical examples are given. In our research, special attention was given to sensory activity in companies and product experts or product-specific panels. Emerging future trends in this field will involve meta-analyses, application of AI and integration of psychophysics.

Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.

Characterizing the Volatile and Sensory Profiles, and Sugar Content of Beeswax, Beebread, Bee Pollen, and Honey

Bee products are a well-known remedy against numerous diseases. However, from the consumers’ perspective, it is essential to define factors that can affect their sensory acceptance. This investigation aimed to evaluate the volatile and sensory profiles, and sugar composition of beeswax, beebread, pollen, and honey. According to the HS-SPME/GC-MS results, 20 volatiles were identified in beeswax and honey, then 32 in beebread, and 33 in pollen. Alkanes were found to dominate in beeswax, beebread, and pollen, while aldehydes and monoterpenes in honey. In the case of sugars, a higher content of fructose was determined in beebread, bee pollen, and honey, whereas the highest content of glucose was assayed in beeswax. In the QDA, the highest aroma intensity characterized as honey-like and sweet was found in honey, while the acid aroma was typical of beebread. Other odor descriptors, including waxy, pungent, and plant-based aromas were noted only in beeswax, honey, and pollen, respectively.

Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.