Search Results (2,266)

Coffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approach coffee-related quality tasks. Twelve different metal oxide sensors were employed in the e-nose construction. The tasks were (i) the separation of Coffea arabica and Coffea canephora species, (ii) the distinction between roasting profiles (light, medium, and dark), and (iii) the separation of expired and non-expired coffees. Exploratory analysis with principal component analysis (PCA) pointed to a fair grouping of the tested samples according to their specification, indicating the potential of the volatiles in grouping the samples. Moreover, a supervised classification employing soft independent modeling of class analogies (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares support vector machine (LS-SVM) led to great results with accuracy above 90% for every task. The performance of each model varies with the specific task, except for the LS-SVM models, which presented a perfect classification for all tasks. Therefore, combining the e-nose with distinct classification models could be used for multiple-purpose classification tasks for producers as a low-cost, rapid, and effective alternative for quality assurance. Full article

Over the years, several studies have investigated the essential oils (EOs) of Salvia taxa, revealing significant chemical variability in their composition. The present study focused on the characterization of the EOs of seven Salvia taxa growing wild in Greece, namely S. aethiopis L., S. argentea L., and S. sclarea L. (Aethiopis section); S. officinalis L. subsp. officinalis and S. tomentosa Mill. (Eusphace section); S. verticillata L. subsp. verticillata (Hemisphace section); and S. amplexicaulis Lam. (Plethiosphace section). Chemometric analysis, including PCA, HCA, and a clustered heat map, were applied to identify possible relationships among the samples based on their constituents, chemical groups, and thujone contents. The analysis classified the samples into two distinct groups based on their chemical classes; Group I (Svert, Sarg, Sampl, and Sath) was characterized by the highest amounts of sesquiterpene hydrocarbons (42.7–88.0%), followed by oxygenated sesquiterpenes (6.7–41.6%) and monoterpenes (0–17.2%), while Group II (Soff, Stom, and SScl) showed the highest amounts of oxygenated monoterpenes (47–66.4%), followed by monoterpene hydrocarbons (4.9–22.7%), sesquiterpenes (3.2–15.3%), and oxygenated diterpenes (3.5–9.0%). Regarding thujone content, two major groups were detected. The first group comprised Sscl, Svert, Sarg, Sampl, and Sath while the second group comprised Soff and Stom (Subgenus Salvia/Section Eusphace), which exhibited the highest percentages of thujones. These findings provide a basis for further investigation into taxonomic studies of the Salvia genus. Full article

Brazilian stingless bee species produce honey with distinct physicochemical and bioactive properties shaped by environmental factors. This study investigated the effects of the rainy and dry seasons on the physicochemical characteristics, chemical fingerprinting, mineral content, and antioxidant capacity of honey from Melipona mondury and Melipona bicolor. The honey samples were analyzed for their phytochemical properties (official methods), total phenolics (Folin–Ciocalteu method), flavonoid content (aluminum complex formation method), antioxidant capacity (FRAP and ABTS assays), and antioxidant activity (erythrocyte model). The mineral content was assessed via TXRF spectroscopy, and chemical fingerprinting was conducted using mass spectrometry. Chemometric tools were used for the samples’ discriminating analyses, including Principal Component Analysis (PCA) and Partial Least Squares–Discriminant Analysis (PLS-DA). Seasonal variations significantly affected the moisture, total soluble solids, and acidity. In turn, the antioxidant capacity was influenced mainly by the bee species. The mineral composition, particularly potassium, phosphorus, and calcium, remained stable. Multivariate analysis identified m/z ions (VIP scores > 2.5), rather than physicochemical or antioxidant capacity parameters, as critical for seasonal discrimination. The antioxidant activity, assessed by oxidative hemolysis prevention, was robust across the seasons, with M. mondury honey (2 mg·mL⁻¹) from the rainy season outperforming ascorbic acid. These findings underscore the impact of the rainy and dry seasons and the potential of secondary metabolite fingerprinting to identify collection periods. Full article

Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination R² = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications. Full article

This study investigates the aroma characterization of unique white tea varieties from the Lüchun county of Yunnan province, Mainland China. These include shaken, unshaken, steam-cooked, and compressed varieties. The aroma profile of white tea varieties was analyzed using two-dimensional gas chromatography–olfactometry–mass spectrometry (GC×GC-O-MS), electronic nose (e-nose), and descriptive sensory evaluation. A chemometric approach was used to compare sensory scores to instrumental data. A total of 154 volatile compounds were detected in 16 white tea varieties through GC×GC-O-MS. Among these, 133 compounds were successfully identified through the National Institute of Standards and Technology (NIST) library, and 21 were listed as unknown. The identified volatile classes include aldehydes, such as hexanal and heptanal, which contribute to the green aroma of white tea, and alcohols like 2-heptanol and 3-hexen-1-ol, which exhibit fresh and floral odor notes. The content and relative odor active values (r-OAVs) of the volatile compounds were calculated. The chemometric data revealed significant variations in volatile contents between shaken and unshaken white tea varieties. The orthogonal partial least squares discriminant analysis (OPLS-DA) model showed strong validity and stability. This study describes the impact of processing conditions on the flavor profile of white tea and provides a solid foundation for monitoring the aroma quality of different processed white tea varieties. Full article

Food is essential for human survival; however, food can be an important route of exposure to contaminants. This study investigated the presence and distribution of anthropogenic contaminants in food consumed by families with small children in South Florida, United States, evaluating seasonal and socio-economic variabilities in chemical composition. QuEChERS protocols, followed by non-targeted analysis (NTA) using an LC-Orbitrap HRMS system, were used for the comprehensive screening of organic contaminants. The compounds were annotated and identified with the Compound Discoverer (CD) software, and contaminant distributions were analyzed using boxplots and Principal Component Analysis (PCA). The results showed significant seasonal and socio-economic differences in contaminant distributions (p < 0.05). In the wet season, a predominance of polymers and surfactants, such as dodecanedioic acid and N-dodecylacrylamide, were found in food, which might be due to increased transport of industrial pollutants during increased precipitation, while plasticizers (e.g., bis(2-ethylhexyl) phthalate) and drugs (e.g., warfarin) were more prevalent during the dry season, which could be related to less dilution effects in this period. A higher abundance of 1-nitrosopiperidine, present in cured meats, was noted in food from upper socio-economic classes, while the lower class showed higher abundance of benzocaine, a common topical anesthetic. Full article

The COVID-19 pandemic has caused over 7 million deaths globally in the past four years. Siparuna spp. (Siparunaceae), which is used in Brazilian folk medicine, is considered a genus with potential antiviral alternatives. This study explored the correlation between phytochemicals in Siparuna leaf extracts (S. ficoides, S. decipiens, S. glycycarpa, S. reginae, and S. cymosa) and their potential against various SARS-CoV-2 targets. In vitro assays examined interactions between the spike protein and the ACE2 receptor, protease activity, and viral replication inhibition in Calu-3 cell models. UHPLC-MS/MS analysis, processed with MZmine and evaluated chemometrically, revealed isoquinoline alkaloids with bulbocapnine, showing promising therapeutic potential. Predictions regarding absorption, distribution, metabolism, excretion, and toxicity were conducted, along with molecular docking and dynamics simulations, to evaluate protein−ligand interaction stability. The results confirmed the antiviral activity of the Siparuna genus against SARS-CoV-2 targets, with 92% of the extracts maintaining over 70% cellular viability at 200 μg·mL⁻¹ and 80% achieving more than 50% viral activity suppression at 50 μg·mL⁻¹. These findings highlight the potential of isoquinoline alkaloids as novel anti-coronavirus agents and support the need for further exploration, isolation, and testing of Siparuna compounds in the fight against COVID-19. Full article

The aim of this study was to compare the antioxidant potential in the fruits of different hybrids of Rubus idaeus and Rubus occidentalis (four hybrids) against the fruit of known cultivars of both species (R. idaeus—three cultivars; R. occidentalis—five cultivars) and, using chemometric analysis, to select factors affecting the level of polyphenols and antioxidant properties. Antioxidant activity was determined using the ABTS, DPPH and FRAP tests. Chemometric analysis enabled the separation of R. idaeus and R. occidentalis cultivars and classified the hybrid R. idaeus/R. occidentalis R1314701 as belonging to the R. occidentalis species. Moreover, two hybrids, Rubus occidentalis/Rubus idaeus R1613411 and R. idaeus/R. occidentalis R1613409, can be classified as a purple raspberry. Crossbreeding species/cultivars of the Rubus genus may result in an increased content of anthocyanins, but on the other hand, it may lead to a reduction in free radical scavenging activity in the ABTS and DPPH. Spearman’s correlations confirm the correlations between the total polyphenol content and antioxidant activity in the DPPH, ABTS and FRAP, as well as the anthocyanin content and antioxidant activity in the ABTS and FRAP tests. Chemometric analysis can be an effective tool in determining the species affiliation of obtained hybrids and cultivars. Full article

Patterns of disease and therapeutic responses vary widely among patients with autoimmune glomerulonephritis. This study introduces groundbreaking personalized infrared (IR)-based diagnostics for real-time monitoring of disease status and treatment responses in lupus nephritis (LN). We have established a relative absorption difference (RAD) equation to assess characteristic spectral indices based on the temporal peak heights (PHs) of two characteristic serum absorption bands: ν₁ as the target signal and ν₂ as the PH reference for the ν₁ absorption band, measured at each dehydration time (t) during dehydration. The RAD gap (Ψ), defined as the difference in the RAD values between the initial and final stages of serum dehydration, enables the measurement of serum levels of IgG glycosylation (ν₁ (1030 cm⁻¹), ν₂ (1171 cm⁻¹)), serum lactate (ν₁ (1021 cm⁻¹), ν₂ (1171 cm⁻¹)), serum hydrophobicity (ν₁ (2930 cm⁻¹), ν₂ (2960 cm⁻¹)), serum hydrophilicity (ν₁ (1550 cm⁻¹), ν₂ (1650 cm⁻¹)), and albumin (ν₁ (1400 cm⁻¹), ν₂ (1450 cm⁻¹)). Furthermore, this IR-based assay incorporates an innovative algorithm and our proprietary iPath software (ver. 1.0), which calculates the prognosis prediction function (PPF, Φ) from the RAD gaps of five spectral markers and correlates these with conventional clinical renal biomarkers. We propose that this algorithm-assisted, IR-based approach can augment the patient-centric care of LN patients, particularly by focusing on changes in serum IgG glycosylation. Full article

Lomatium is a genus of 98 species, widely distributed in western North America. This work presents a chemometric analysis of the essential oils of seven species of Lomatium (L. anomalum, L. dissectum var. dissectum, L. multifidum, L. nudicaule, L. packardiae, L. papilioniferum, and L. triternatum var. triternatum) from the intermountain western United States (Oregon and Idaho). The essential oils were obtained by hydrodistillation and analyzed by gas chromatographic methods. Lomatium packardiae essential oil can be characterized as limonene-rich, L. anomalum is a species rich in sabinene and α-pinene, and L. multifidum essential oils were rich in myrcene, while L. dissectum var. dissectum essential oils were dominated by octyl acetate and decyl acetate, L. papilioniferum essential oils from western Idaho had high p-cymene and 2-methyl-5-(1,2,2-trimethylcyclopentyl)phenol concentrations, while those from Oregon had relatively high β-phellandrene and sedanenolide levels. The essential oils of L. triternatum var. triternatum were too variable to confidently assign a chemical type. The major components in the L. nudicaule essential oils were β-phellandrene (16.0–45.7%), (Z)-ligustilide (5.6–47.1%), (E)-β-ocimene (3.3–9.9%), and δ-3-carene (0.2–12.6%). The enantiomeric distributions of α-pinene, camphene, sabinene, β-pinene, limonene, and linalool were also utilized to discriminate between the Lomatium taxa. There are not enough consistent data to properly characterize L. triternatum var. triternatum or the Oregon L. papilioniferum essential oils. Additional research is needed to confidently describe the chemotype(s) of these species. Full article

Background/Objectives: Developing antifungal drugs with lower potential for interactions with food may help to optimize treatment and reduce the risk of antimicrobial resistance. Chemometrics uses statistical and mathematical methods to analyze multivariate chemical data, enabling the identification of key correlations and simplifying data interpretation. We used the partial least squares (PLS) approach to explore the correlations between various characteristics of oral antifungal drugs (including antifungal antibiotics) and dietary interventions, aiming to identify patterns that could inform the optimization of antifungal therapy. Methods: We analyzed 15 oral antifungal drugs, including azoles (8), antifungal antibiotics (4), antifungal antimetabolites (1), squalene epoxidase inhibitors (1), and glucan synthase inhibitors (1). The input dataset comprised information from published clinical trials, chemical records, and calculations. We constructed PLS models with changes in the pharmacokinetic parameters (∆AUC, area under the curve; ∆C_max, maximum drug concentration; and ∆T_max, time to reach maximum drug concentration) after dietary intervention as the response parameters and eight groups of molecular descriptors (M1–M8) as the predictor parameters. We performed separate analyses for the different nutritional interventions. Results: In the final PLS model with food as an intervention, we effectively reduced the dimensionality of the dataset while retaining a substantial percentage of the original information (variance), as significant components explained 69.8% and 17.5% of the predictor and response parameter variances, respectively. The PLS model was significant because its components met the cross-validation criteria. We obtained six significant positive and negative correlations between the descriptors related to atoms and the postprandial ∆T_max. Conclusions: The PLS method is valuable for investigating interactions between antifungal drugs (including antifungal antibiotics) and food. The correlations obtained can be used in drug modeling to predict interactions with dietary interventions based on the antifungal drug’s chemical structure. Incorporating chemometric techniques into the early drug development stages could facilitate the design of antifungal antibiotics and other antifungal agents with optimized absorption in the presence of dietary components. Full article

The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction. A combination of these methods with appropriate quantitative techniques is beneficial to reveal the differences between samples or improve predictive performance. Quantitative evaluation can be divided into similarity measurement methods and machine learning algorithms. When evaluating small batch samples, similarity measurements can provide quantitative discrimination results. When the sample data are sufficient and matched with Raman spectroscopy parameters, machine learning algorithms suitable for intelligent discrimination can be trained and optimized. Finally, with the rise of deep learning algorithms and fusion strategies, some challenges in this field are proposed. Full article

Copper (Cu) is a critical micronutrient for wheat (Triticum aestivum L.), essential for growth and grain baking quality, yet its availability is limited because Cu is specifically adsorbed on colloids of highly weathered tropical soils like Oxisols. This study hypothesizes that Cu-doped biochar composites can outperform traditional Cu fertilizers in improving wheat growth and Cu use efficiency. Composites were synthesized from chicken manure (FCM), shrimp shells (FSC), and sewage sludge (FSS), doped with copper sulfate (CuSO₄5H₂O) or copper oxide (CuO), and pyrolyzed at 300 °C or 550 °C. The experimental design involved greenhouse trials in two Oxisols (RYL and DRL), assessing Cu release kinetics, plant Cu uptake, and dry matter production. Fourier Transform Infrared Spectroscopy (FTIR) confirmed successful Cu integration. Results revealed that CSS/CS-5 (FSS + CuSO₄5H₂O at 550 °C) improved Cu uptake and shoot biomass in DRL soil, while CSC/CS-3 (FSC + CuSO₄5H₂O at 300 °C) enhanced wheat CuSO₄5H₂O growth in RYL soil. Peak Cu availability varied by CuSO₄5H₂O soil and composite type, with residual Cu highest CuSO₄5H₂O in CuSO₄5H₂O-treated soils. These findings demonstrate that Cu–biochar composites, tailored to soil conditions, offer a sustainable alternative to mineral Cu fertilizers by enhancing the nutrient availability and wheat grain yield. Full article

The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans’ soaking times of 13–15, 15–17, and 17–19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water’s color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R² values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples. Full article

Sargassum fusiforme, an edible seaweed, plays a crucial role in our daily lives by providing essential nutrients, including minerals, to the human body. The detection of mineral content during different growth stages of S. fusiforme benefits the goals of ensuring product quality, meeting diverse consumer needs, and achieving quality classification. Currently, the determination of minerals in S. fusiforme primarily relies on inductively coupled plasma mass spectrometry and other methods, which are time-consuming and labor-intensive. Thus, a rapid and convenient method was developed for the determination of six minerals (i.e., Na, Mg, Ca, Cu, Fe, and K) in S. fusiforme via near-infrared (NIR) spectroscopy based on chemometrics. This study investigated the variations in minerals in S. fusiforme from different growth stages. The effects of four spectral pretreatment methods and three wavelength selection methods, including the synergy interval partial least squares (SI-PLS) algorithm, genetic algorithm (GA), and competitive adaptive reweighted sampling method (CARS) on the model optimization, were evaluated. Superior CARS-PLS models were established for Na, Mg, Ca, Cu, Fe, and K with root mean square error of prediction (RMSEP) values of 0.8196 × 10³ mg kg⁻¹, 0.4370 × 10³ mg kg⁻¹, 1.544 × 10³ mg kg⁻¹, 0.9745 mg kg⁻¹, 49.88 mg kg⁻¹, and 7.762 × 10³ mg kg⁻¹, respectively, and coefficient of determination of prediction (R_P²) values of 0.9787, 0.9371, 0.9913, 0.9909, 0.9874, and 0.9265, respectively. S. fusiforme demonstrated higher levels of Mg and Ca at the seedling stage and lower levels of Cu and Fe at the maturation stage. Additionally, S. fusiforme exhibited higher Na and lower K at the growth stage. NIR combined with CARS-PLS is a potential alternative for monitoring the concentrations of minerals in S. fusiforme at different growth stages, aiding in the convenient evaluation and further grading of the quality of S. fusiforme. Full article