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Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C-C-C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.
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Honey differentiation based on the botanical origin is crucial to guarantee product authenticity, especially considering the increasing number of fraud cases. This study assessed the metabolomic differences arising from various botanical origins in honey products sold in Spanish markets, focusing on two goals: (1) discrimination within monofloral samples (eucalyptus, rosemary, and orange blossom honey) and (2) differentiation between multifloral vs. monofloral honey samples. An omics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap-high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) was applied for the reliable identification of specific honey markers selected by orthogonal partial least squares discriminant analysis (OPLS-DA) (R2Y = 0.929-0.981 and Q2 = 0.868-0.952), followed by the variable importance in projection (VIP) approach. Key amino acid, alkaloid, and trisaccharide markers were identified to distinguish between honey samples. Some Amadori compounds were highlighted as eucalyptus honey markers, suggesting their potential use for honey aging and botanical origin differentiation. L-phenylalanine and raffinose were markers of rosemary honey. Four markers (e.g., trigonelline, L-isoleucine, and N-(1-deoxy-1-fructosyl)isoleucine) were found in higher levels in multifloral samples, indicating a greater availability of amino acids, potentially increasing the Maillard reaction. This research is the first to address the botanical origin's impact on honey by identifying novel markers not previously described.
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Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.
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Colorimetría , Odorantes , Máquina de Vectores de Soporte , Gusto , Té , Compuestos Orgánicos Volátiles , Té/química , Compuestos Orgánicos Volátiles/análisis , Colorimetría/métodos , Odorantes/análisis , Olfato , Camellia sinensis/química , HumanosRESUMEN
This study aimed to investigate the effect of vacuum freeze drying combined with catalytic infrared drying (FD-CIRD) process on aromas, free amino acids, reducing sugars and free fatty acids in chive leaves and stems. Gas chromatography-mass spectrometry combined with multivariate data analysis revealed that dipropyl disulfide was the key aroma that distinguished the differences between chive leaves and stems. The key aromas benzeneacetaldehyde, decanal and 1-octen-3-ol enhanced FD-CIRD chive leaves and stems aromas. The free amino acid content was highest at FD-CIRD stage in all samples except for the control (FD), while the reducing sugar content was lowest. The content of unsaturated fatty acids gradually decreased at FD stage and increased at FD-CIRD stage. Additionally, correlation analysis revealed that phenylalanine was a potential precursor of benzacetenealdehyde, oleic and linolenic acids were potential precursors of decanal and 1-octen-3-ol. Therefore, FD-CIRD technique helps to improve the sensory profile of dried chives.
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Liofilización , Odorantes , Vacio , Odorantes/análisis , Compuestos Orgánicos Volátiles/química , Cromatografía de Gases y Espectrometría de Masas , Rayos Infrarrojos , Aminoácidos/química , Aminoácidos/análisis , Hojas de la Planta/química , Catálisis , Desecación/métodos , Desecación/instrumentaciónRESUMEN
An anti-interference colorimetric sensor array (CSA) technique was developed for the qualitative and quantitative detection of target heavy metals in corn oil. This method involves a binding mechanism that triggers changes in atomic energy levels and visible color changes. A custom-built olfactory visualization device was employed to gather spectral data, revealing distinct CSA color difference patterns. Subsequently, three pattern recognition algorithms were used to create an identification model for the target heavy metals. The results showed that the ACO-KNN (Ant Colony Optimization-K-Nearest Neighbor) model outperformed the other models, achieving accuracy rates of 90.28% and 89.58% for the calibration and prediction sets, respectively. The ACO-PLS (Partial Least Square) model was more stable with the lowest root mean square error of prediction (RMSEP), which were 0.1730 and 0.1180, respectively. The limit of detection (LOD) and quantification (LOQ) of Pb and Hg were (0.3, 0.6, 1.1 and 2.2) x 10-3 mg/L, respectively.
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Colorimetría , Contaminación de Alimentos , Metales Pesados , Espectroscopía Infrarroja Corta , Colorimetría/métodos , Colorimetría/instrumentación , Metales Pesados/análisis , Contaminación de Alimentos/análisis , Espectroscopía Infrarroja Corta/métodos , Límite de Detección , Aceite de Maíz/químicaRESUMEN
Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today's complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.
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COVID-19 , Aprendizaje Profundo , SARS-CoV-2 , Humanos , COVID-19/epidemiología , Epidemias/estadística & datos numéricos , Modelos EpidemiológicosRESUMEN
Scale-down models (SDM) are pivotal tools for process understanding and improvement to accelerate the development of vaccines from laboratory research to global commercialization. In this study, a 3 L SDM representing a 50 L scale Vero cell culture process of a live-attenuated virus vaccine using microcarriers was developed and qualified based on the constant impeller power per volume principle. Both multivariate data analysis (MVDA) and the traditional univariate data analysis showed comparable and equivalent cell growth, metabolic activity, and product quality results across scales. Computational fluid dynamics simulation further confirmed similar hydrodynamic stress between the two scales.
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Fermentation stage is a crucial factor for flavor profiles formation of hawthon wine. Thus, comprehensive knowledge of dynamic relationship between nonvolatile (NVOCs) and volatile aroma compounds (VOCs) from hawthorn wine at different fermentation stages was investigated by GC-MS and HPLC coupled with multivariate analysis. The increase of alcohols/esters/acids but decrease of terpenes/aldehydes/ketones was observed as fermentation extension. Specifically, OAV of ethyl acetate, ethyl caprylate, and ethyl caprate was > 50 from the 3rd day to 10th day, giving more fruity properties. Multivariate analysis showed that 1-hexanol, ethyl myristate, isobutyric acid, et al., were linked to the sensory evaluation of "sweet", "floral" and "fruity", and fructose, glucose and bitter amino acids were responsible for reduction of "bitterness" and "astringency". Additionally, VOCs were positively correlated with organic acids while negative to amino acids/soluble sugars, probably due to metabolization as precursors, providing references for aroma enhancement by regulating NVOCs precursors.
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Crataegus , Fermentación , Aromatizantes , Cromatografía de Gases y Espectrometría de Masas , Odorantes , Gusto , Compuestos Orgánicos Volátiles , Vino , Vino/análisis , Compuestos Orgánicos Volátiles/química , Compuestos Orgánicos Volátiles/metabolismo , Compuestos Orgánicos Volátiles/análisis , Odorantes/análisis , Humanos , Aromatizantes/química , Aromatizantes/metabolismo , Análisis Multivariante , Crataegus/química , Femenino , Masculino , AdultoRESUMEN
Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.
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Imágenes Hiperespectrales , Listeria monocytogenes , Análisis de Componente Principal , Serogrupo , Espectroscopía Infrarroja Corta , Listeria monocytogenes/aislamiento & purificación , Listeria monocytogenes/clasificación , Espectroscopía Infrarroja Corta/métodos , Imágenes Hiperespectrales/métodos , Análisis Discriminante , Análisis de los Mínimos CuadradosRESUMEN
Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.
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Gas chromatography ion mobility spectrometry (GC-IMS) is a robust and sensitive benchtop technique commonly used for non-target screening of volatile organic compounds. It has been applied to authenticity analysis by generating characteristic "fingerprints" of food samples, well suited for chemometric data analysis. This dataset contains headspace GC-IMS spectra from 50 monofloral honey samples from three different botanical origins, 18 acacia honeys (Robinia pseudoacacia), 19 canola honeys (Brassica napus) and 18 honeydew honeys (forest flowers). Honeys were sourced from the beekeepers directly or obtained from governmental food inspectors from Baden-Wuerttemberg, Germany. Authenticity was confirmed by pollen analysis in the framework of the official control of foodstuffs. The data was acquired using a setup based on an Agilent 6890N gas chromatograph (Agilent Technologies, Palo Alto, CA) and an OEM Standalone IMS cell from G.A.S Sensorsysteme m. b. H. (Dortmund, Germany). All samples were recorded in duplicates and spectra are presented as raw data in the .mea file format. The dataset is available on Mendeley Data: https://data.mendeley.com/datasets/jxj2r45t2x.
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In the last decade, "expressions" of grape marc spirits aged in wooden barrels of characteristic amber color and complex sensory attributes have been introduced. Yet studies on constituents migrating from the barrel to the beverage are scarce, and their metabolic profile remains unexplored. Furthermore, the literature on the assessment of their antioxidant activity is limited. NMR metabolomics and spectrophotometry have been implemented in 38 samples to elucidate the impact of the aging procedure on the metabolites' composition and establish whether these beverages exhibit antioxidant activity. Provenance was related to fusel alcohols, esters, acetaldehyde, methanol, saccharides, and 2-phenylethanol, while ethyl acetate and ethyl lactate contributed to discriminating samples of the same winery. Identified metabolites such as vanillin, syringaldehyde, and sinapaldehyde were related to the aging procedure. The maturation in the barrel was also associated with an increase in xylose, glucose, fructose, and arabinose. The antioxidant potential of the aged Greek grape marc spirits resulting from their maturation in oak barrels was highlighted. The metabolic profiling and antioxidant potential of aged Greek grape marc spirits were assessed for the first time. Finally, the enrichment of the aromatic region was noted with the presence of metabolites with a furanic and phenolic ring derived, respectively, from the polysaccharides' degradation or the thermal decomposition of lignin.
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Wheat species with various ploidy levels may be different regarding their immunoreactive potential in celiac disease (CD), but a comprehensive comparison of peptide sequences with known epitopes is missing. Thus, we used an untargeted liquid chromatography tandem mass spectrometry method to analyze the content of peptides with CD-active epitope in the five wheat species common wheat, spelt, durum wheat, emmer, and einkorn. In total, 494 peptides with CD-active epitope were identified. Considering the average of the eight cultivars of each species, spelt contained the highest number of different peptides with CD-active epitope (193 ± 12, mean ± SD). Einkorn showed the smallest variability of peptides (63 ± 4) but higher amounts of certain peptides compared to the other species. The wheat species differ in the presence and distribution of CD-active epitopes; hence, the entirety of peptides with CD-active epitope is crucial for the assessment of their immunoreactive potential.
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Enfermedad Celíaca , Epítopos , Proteínas de Plantas , Proteómica , Triticum , Enfermedad Celíaca/inmunología , Triticum/química , Triticum/inmunología , Epítopos/inmunología , Epítopos/química , Proteínas de Plantas/inmunología , Proteínas de Plantas/química , Proteínas de Plantas/genética , Humanos , Espectrometría de Masas en Tándem , Péptidos/inmunología , Péptidos/químicaRESUMEN
Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.
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Polvos , Análisis de Componente Principal , Comprimidos , Resistencia a la Tracción , Comprimidos/química , Análisis de los Mínimos Cuadrados , Polvos/química , Excipientes/química , Tamaño de la Partícula , Composición de Medicamentos/métodosRESUMEN
Cereal grains play an important role in human health as a source of macro- and micronutrients, besides phytochemicals. The metabolite diversity was investigated in cereal crops and their milling fractions by untargeted metabolomics ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) of 69 samples: 7 species (barley, oat, pearl millet, rye, sorghum, triticale, and wheat), 23 genotypes, and 4 milling fractions (husk, bran, flour, and wholegrain). Samples were also analyzed by in vitro antioxidant activity. UHPLC-MS/MS signals were processed using XCMS, and metabolite annotation was based on SIRIUS and GNPS libraries. Bran and husk showed the highest antioxidant capacity and phenolic content/diversity. The major metabolite classes were phenolic acids, flavonoids, fatty acyls, and organic acids. Sorghum, millet, barley, and oats showed distinct metabolite profiles, especially related to the bran fraction. Molecular networking and chemometrics provided a comprehensive insight into the metabolic profiling of cereal crops, unveiling the potential of coproducts and super cereals such as sorghum and millet as sources of polyphenols.
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Antioxidantes , Grano Comestible , Espectrometría de Masas en Tándem , Antioxidantes/metabolismo , Antioxidantes/química , Antioxidantes/análisis , Grano Comestible/química , Grano Comestible/metabolismo , Cromatografía Líquida de Alta Presión , Sorghum/química , Sorghum/metabolismo , Avena/química , Avena/metabolismo , Avena/genética , Triticum/química , Triticum/metabolismo , Triticum/genética , Flavonoides/metabolismo , Flavonoides/análisis , Flavonoides/química , Extractos Vegetales/química , Extractos Vegetales/metabolismo , Mijos/química , Mijos/metabolismo , Mijos/genética , Hordeum/química , Hordeum/metabolismo , Hordeum/genética , Semillas/química , Semillas/metabolismo , Metabolómica , Productos Agrícolas/química , Productos Agrícolas/metabolismo , Productos Agrícolas/genéticaRESUMEN
Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.
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Coffea , Café , Manipulación de Alimentos , Semillas , Brasil , Coffea/química , Semillas/química , Manipulación de Alimentos/métodos , Café/química , Alcaloides/análisis , Cromatografía Líquida de Alta Presión , Humanos , Gusto , Análisis de Componente PrincipalRESUMEN
The composition of honey is mostly determined by the species-specific characteristics of flowering plants, which is reflected in the significant deviations in composition of honey varieties. The high-quality acacia honey is assessed based on both physical-chemical parameters and melissopalynology. The appearance of rape pollen in acacia honey makes the acacia honey be sorted into the multifloral honey category. Over carrying out melissopalynology, the149 samples of various honeys (acacia, rape and multifloral) have also been analysed by using physical-chemical and elemental analysis. Multivariate data analysis revealed that multifloral honey is much closer to acacia honey than to rape honey, as it can be observed from the examined unique parameters. By the PCA (Principal Component Analysis) analysis based on united set of physico-chemical and melissopalynology results the acacia and rape honey samples are entirely separated for each other, while multifloral honey samples are very close to acacia honey group and partially overlap with it. On ignoring the pollen analysis and based on the rest of the results, the multifloral honey category is almost indistinguishable from the declared and verified acacia honey category.
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The Italian garlic ecotype "Vessalico" possesses distinct characteristics compared to its French parent cultivars Messidor and Messidrôme, used for sowing, as well as other ecotypes in neighboring regions. However, due to the lack of a standardized seed supply method and cultivation protocol among farmers in the Vessalico area, a need to identify garlic products that align with the Vessalico ecotype arises. In this study, an NMR-based approach followed by multivariate analysis to analyze the chemical composition of Vessalico garlic sourced from 17 different farms, along with its two French parent cultivars, was employed. Self-organizing maps allowed to identify a homogeneous subset of representative samples of the Vessalico ecotype. Through the OPLS-DA model, the most discriminant metabolites based on values of VIP (Variable Influence on Projections) were selected. Among them, S-allylcysteine emerged as a potential marker for distinguishing the Vessalico garlic from the French parent cultivars by NMR screening. Additionally, to promote sustainable agricultural practices, the potential of Vessalico garlic extracts and its main components as agrochemicals against Xanthomonas campestris pv. campestris, responsible for black rot disease, was explored. The crude extract exhibited a MIC of 125 µg/mL, and allicin demonstrated the highest activity among the tested compounds (MIC value of 31.25 µg/mL).
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INTRODUCTION: The Olive (Olea europaea L.) is one of the most popular edible oil-producing fruits, consumed worldwide for its myriad nutritional and health benefits. Olive oil production generates huge quantities of by-products from the fruit, which are considered environmental hazards. Recently, more and more efforts have been made to valorize olive by-products as a source of low-cost, value-added food applications. OBJECTIVE: The main objective of this study was to globally assess the metabolome of olive fruit by-products, including olive mill wastewater, olive pomace, and olive seeds from fruits from two areas, Siwa and Anshas, Egypt. METHODS: Gas chromatography-mass spectrometry (GC-MS) and ultra-high-performance liquid chromatography with mass spectrometry (UPLC-MS) were used for profiling primary and secondary metabolites in olive by-products. Also, multivariate data analyses were used to assess variations between olive by-product samples. RESULTS: A total of 103 primary metabolites and 105 secondary metabolites were identified by GC-MS and UPLC-MS, respectively. Fatty acids amounted to a major class in the olive by-products at 53-91%, with oleic acid dominating, especially in the pomace of Siwa. Mill wastewater was discriminated from other by-products by the presence of phenolics mainly tyrosol, hydroxyl tyrosol, and α-tocopherol as analyzed by UPLC-MS indicating their potential antioxidant activity. Pomace and seeds were rich in fatty acids/esters and hydroxy fatty acids and not readily distinguishable from each other. CONCLUSION: The current work discusses the metabolome profile of olive waste products for valorization purposes. Pomace and seeds were enriched in fatty acids/esters, though not readily distinguishable from each other.
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The characterization of local improved varieties as well as the reduction of synthetic chemical fertilizers are sustainable approaches in the vision of a new precision Farming. Aim of our study was to improve the geographical characterization of local ecotypes and to identify peculiar features of new crops in terms of bioactive compounds. NMR and LC-MS metabolite profiling approaches followed by multivariate data analysis were applied to characterize local rosemary and garlic ecotypes. With the aim of applying for a protected designation of origin, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify representative sensory quality indicators for Vessalico garlic and rosemary "Eretto Liguria" local ecotypes, Variable Influence on Projections (VIP) values of OPLS-DA indicated six metabolites as quality indicators for Vessalico garlic and sixteen metabolites as quality indicators for rosemary "Eretto Liguria". Finally, to discover and utilize new ecotypes in a sustainable way, Vessalico garlic extracts antiviral activity, previously evaluated against Tomato brown rugose fruit virus (ToBRFV), a Tobamovirus affecting tomato crops, was extended to Pepino mosaic virus (PepMV) with positive results.