Evaluation of minerals, toxic elements and bioactive compounds in rose petals (Rosa spp.) using chemometric tools and artificial neural networks

Color, proximate composition, bioactive compounds and antinutrient profiling of rose

Rose (Rosa sp.) is one of the most important ornamentals which is commercialize for its aesthetic values, essential oils, cosmetic, perfume, pharmaceuticals and food industries in the world. It has wide range of variations that is mostly distinguished by petal color differences which is interlinked with the phytochemicals, secondary metabolites and antinutrient properties. Here, we explored the color, bioactive compounds and antinutritional profiling and their association to sort out the most promising rose genotypes. For this purpose, we employed both quantitative and qualitative evaluation by colorimetric, spectrophotometric and visual analyses following standard protocols. The experiment was laid out in randomized complete block design (RCBD) with three replications where ten rose genotypes labelled R1, R2, R3, R4, R5, R6, R7, R8, R9 and R10 were used as plant materials. Results revealed in quantitative assessment, the maximum value of lightness, and the luminosity indicating a brightening of rose petals close to a yellow color from rose accessions R4, and R10, respectively which is further confirmed with the visually observed color of the respective rose petals. Proximate composition analyses showed that the highest amount of carotenoid and β-carotene was found in R10 rose genotype, anthocyanin and betacyanin in R7. Among the bioactive compounds, maximum tocopherol, phenolic and flavonoid content was recorded in R8, R6 and R3 while R1 showed the highest free radical scavenging potentiality with the lowest IC50 (82.60 µg/mL FW) compared to the others. Meanwhile, the enormous variation was observed among the studied rose genotypes regarding the antinutrient contents of tannin, alkaloid, saponin and phytate whereas some other antinutrient like steroids, coumarines, quinones, anthraquinone and phlobatanin were also figured out with their presence or absence following qualitative visualization strategies. Furthermore, according to the Principal Component Analysis (PCA), correlation matrix and cluster analysis, the ten rose genotypes were grouped into three clusters where, cluster-I composed of R3, R4, R5, R8, cluster-II: R9, R10 and cluster-III: R1, R2, R6, R7 where the rose genotypes under cluster III and cluster II were mostly contributed in the total variations by the studied variables. Therefore, the rose genotypes R9, R10 and R1, R2, R6, R7 might be potential valuable resources of bioactive compounds for utilization in cosmetics, food coloration, and drugs synthesis which have considerable health impact.

Argentinian Rose Petals as a Source of Antioxidant and Antimicrobial Compounds

The total phenolic, flavonoid, and anthocyanin contents were evaluated in 11 cultivars of Argentinian roses of different colors. HPLC-ESI-QTOF/MS was used to identify the components where ellagic and quinic acids, quercetin, and kaempferol glycosylated derivatives were found. The phenolic contents ranged from 78.8 ± 3.2 to 203.4 ± 3.1 mg GAE/g dw, the flavonoid content ranged from 19.1 ± 3.8 to 125.9 ± 6.5 mg QE/g dw, and the anthocyanin content ranged from less than 0.01 to 5.8 ± 0.1 mg CE/g dw. The dark red cultivars exhibited the greatest levels of the analyzed compounds and of the antioxidant activities, even higher than those of certain plants known for their high phenolic contents and antioxidant activity. Moreover, the addition of these extracts decreased the population of L. innocua and P. aeruginosa to undetectable levels 24 h after inoculation. Rose petal extracts, mainly those with a dark red color, can be used as natural additives in food, feed, and cosmetics, as they contain a high proportion of bioactive compounds with antioxidant and antimicrobial effects.

Exploring Plants with Flowers: From Therapeutic Nutritional Benefits to Innovative Sustainable Uses

Flowers have played a significant role in society, focusing on their aesthetic value rather than their food potential. This study's goal was to look into flowering plants for everything from health benefits to other possible applications. This review presents detailed information on 119 species of flowers with agri-food and health relevance. Data were collected on their family, species, common name, commonly used plant part, bioremediation applications, main chemical compounds, medicinal and gastronomic uses, and concentration of bioactive compounds such as carotenoids and phenolic compounds. In this respect, 87% of the floral species studied contain some toxic compounds, sometimes making them inedible, but specific molecules from these species have been used in medicine. Seventy-six percent can be consumed in low doses by infusion. In addition, 97% of the species studied are reported to have medicinal uses (32% immune system), and 63% could be used in the bioremediation of contaminated environments. Significantly, more than 50% of the species were only analysed for total concentrations of carotenoids and phenolic compounds, indicating a significant gap in identifying specific molecules of these bioactive compounds. These potential sources of bioactive compounds could transform the health and nutraceutical industries, offering innovative approaches to combat oxidative stress and promote optimal well-being.

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12-30 °C and aw = 0.75-0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted aw, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R2 = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.

Characterization of the chemical composition (mineral, lead and centesimal) in pine nut (Araucaria angustifolia (Bertol.) Kuntze) using exploratory data analysis

In this study, mineral composition, centesimal composition and lead were evaluated in pine nut seeds (raw and cooked) from five Brazilian states. Mineral composition was determined by ICP OES and lead by GF AAS. The results for minerals were evaluated by Boxplot, PCA and HCA, using the R software. Average minerals in raw and cooked samples (mg 100 g^-1) were: 15.2 and 10.8 (Ca), 0.168 and 0.113 (Cu), 0.506 and 0.330 (Fe), 536 and 420 (K), 51.3 and 40.6 (Mg), 0.373 and 0.208 (Mn), 132 and 102 (P) and 0.746 and 0.520 (Zn). The average centesimal composition (raw and cooked) was: 53.5 and 47.2% (moisture), 1.76 and 1.26% (ash), 3.90 and 3.53% (protein), 40.8 and 48.0% (carbohydrate) and 179 and 206 kcal/100 g (total caloric value) and Pb was not detected. The chemometric analysis showed a distinction of raw and cooked samples due to significant nutrient losses after thermal processing.

Chemical, Nutritional and Sensory Characteristics of Six Ornamental Edible Flowers Species

Ornamental edible flowers can be used as novel nutraceutical sources with valuable biological properties. The purpose of this study was to establish nutritional, chemical, and sensory characteristics, antioxidant capacity (AC), and the relationship between their bioactive components and AC. The selected flowers Begonia × tuberhybrida, Tropaeolum majus, Calendula officinalis, Rosa, Hemerocallis, and Tagetes patula, can be easily collected due to their larger size. Their methanolic extracts were spectrophotometrically determined for polyphenols, flavonoids, and AC. Mineral elements were analyzed by atomic-absorption spectroscopy; crude protein was quantified by the Kjeldahl method. Eventually, 30 panelists evaluated sensory properties in 11 attributes. In addition, this study may serve to popularize selected blossoms. In flowers the contents of minerals were in this order: K > Ca > P > Mg > Na > Zn > Mn > Fe > Cu > Mo. AC ranged between 4.11 and 7.94 g of ascorbic acid equivalents/kg of fresh mass. The correlation coefficients between AC-total phenolics and AC-total flavonoids were r = 0.73* and r = 0.58*, respectively. It is also possible to observe a strong correlation between mineral elements and bioactive compounds. Hemerocallis was rated as the best and most tasteful; additionally, it exhibited the highest AC, total phenolic and flavonoid contents.

The use of chemometric modelling to determine chemical composition-antimicrobial activity relationships of essential oils used in respiratory tract infections

The antimicrobial effects of essential oils are commonly cited within aromatherapeutic texts for use in respiratory tract infections. These essential oils are inhaled or applied to the skin to treat infections and manage symptoms associated with these conditions. A limited number of these essential oils have been scientifically studied to support these claims, specifically, against respiratory pathogens. This study reports on the minimum inhibitory concentration (MIC) of 49 commercial essential oils recommended for respiratory tract infections, and identifies putative biomarkers responsible for the determined antimicrobial effect following a biochemometric workflow. Essential oils were investigated against nine pathogens. Three essential oils, Amyris balsamifera (amyris), Coriandrum sativum (coriander) and Santalum austrocaledonicum (sandalwood) were identified as having greater activity (MIC value = 0.03-0.13 mg/ml) compared to the other essential oils investigated. The essential oil composition of all 49 oils were determined using Gas Chromatography coupled to Mass Spectroscopy (GC-MS) analysis and the GC-MS data analysed together with the antimicrobial data using chemometric tools. Eugenol was identified as the main biomarker responsible for antimicrobial activity in the majority of the essential oils. The ability of a chemometric model to accurately predict the active and inactive biomarkers of the investigated essential oils against pathogens of the respiratory tract was 80.33%.

Self-organizing map applied to the choice of internal standards for the determination of Cd, Pb, Sn, and platinum group elements by inductively coupled plasma mass spectrometry

The behaviors of internal standards, according to different flow rates of the cell collision gas (He), were studied for the determination of Cd, Pb, Pd, Pt, Rh, and Sn in samples of fish and mollusks by inductively coupled plasma mass spectrometry (ICP-MS). The elements Bi, Ge, In, Sc, and Y were selected as internal standards, considering their masses and first ionization energies. Addition and recovery experiments were carried out at three concentration levels to evaluate the accuracy of the method applied for the analysis of two samples with different matrices. The results were evaluated using a self-organizing map (SOM). The best analyte/IS pairs were as follows: ¹¹⁴Cd⁺/⁷⁴Ge⁺, ¹⁹⁵Pt⁺/⁷⁴Ge⁺, and ²⁰⁸Pb⁺/⁷⁴Ge⁺. For ¹⁰³Rh⁺, ¹⁰⁶Pd⁺, and ¹²⁰Sn⁺, greater accuracy was achieved without use of an internal standard. Helium gas (2.8 mL min^-1) was used in the collision cell for the analytes, except for Sn, and recoveries ranged from 98 to 101% under optimal conditions. The use of SOM as an exploratory analysis tool was an effective approach for selection of the most appropriate internal standards.

Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil

Fish are important sources of protein, making them very significant in the human diet. Although the consumption of this food is beneficial for health, it is essential that the product does not contain inorganic components above the limits recommended by the current legislation. Therefore, a method for determination of elements in fish (Mugil cephalus) samples was optimized. A simplex centroid mixture design with restriction was applied for optimization of the acid digestion of samples in an open system under reflux in order to evaluate the best ratio between the reagents HNO₃, H₂O₂ and H₂O. The results indicated that more intense analyte signals were obtained when a mixture containing 3.6 mL of HNO₃ (65% v/v), 0.4 mL of H₂O₂ (30% v/v) and 6.0 mL of H₂O was used. The accuracy of the method was assessed with a CRM of oyster tissue (NIST 1566b). The method presented relative standard deviations (RSDs) of 3.54%; 3.82%; 4.81% and 3.50% for Zn, Fe, Cu and S, respectively. The detection limits were 0.002 mg kg^-1 for Cu and Zn and 0.02 mg kg^-1 for Fe and S. The proposed method was applied for the determination of Zn, Fe, Cu and S in fish samples. A Kohonen Self-Organizing Map (KSOM) with K-means implementation was applied to better delimit the boundary between groups and the spatial and temporal influence on how concentrations of the chemical elements were perceived. To verify the separation, the Davies-Bouldin and Silhouette indices were used, obtaining 0.5374 and 0.8541, respectively, indicating satisfactory separation.

Trace Elements in Edible Flowers from Italy: Further Insights into Health Benefits and Risks to Consumers

The use of edible flowers in cooking dates back to ancient times, but recently it is gaining success among the consumers, increasingly attentive to healthy and sustainable foods of high quality, without neglecting taste, flavour, and visual appeal. The present study aims to deepen the knowledge regarding the mineral composition of edible flowers, an aspect not widely investigated in scientific literature. The concentrations of Cd, Co, Cu, Fe, Mn, Ni, Pb, Sr, V, and Zn have been determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP OES) in flowers belonging to a wide variety of species. The study highlights that some floral species are characterized by significantly higher concentrations of certain trace elements, e.g., the flowers of Acmella oleracea for Mn, those of basil (Ocimum basilicum) and of pumpkins (Cucurbita moschata and C. pepo) for Cu and Sr, and those of orange daylily (Hemerocallis fulva) for Ni. Potentially toxic elements are present at low concentrations, often below the limit of the detection for Cd, Co, Ni, V. In all samples, Cd and Pb are well below the maximum permitted levels in foodstuffs. It can be concluded that the edible flowers analyzed can be considered a good source of essential elements and do not present risks for the consumer health as for the mineral composition.