Sono-assembly of ellagic acid into nanostructures significantly enhances aqueous solubility and bioavailability.

INTRODUCTION: Ellagic acid (EA), commonly found in foods, offers significant health benefits in combating chronic diseases. However, its therapeutic potential is hindered by its extremely poor solubility and bioavailability. METHOD: In this study, EA nanoparticles (EA NPs) were produced using a sono-assembly method, without additional agents. RESULTS: EA NPs exhibited stick-like nanoparticle structures with an average size of 147.3 ± 0.73 nm. EA NPs likely adopt a tunnel-type solvate structure, with 4 water participating in disruption of intramolecular hydrogen bonds in EA and establishment of intermolecular hydrogen bonds between EAs. Importantly, EA NPs exhibited remarkable enhancements in water solubility, with 120.7-fold increase in water, and 97.8-fold increase in pH 6.8 buffer. Moreover, ex vivo intestinal permeability studies demonstrated significant improvements (P < 0.5). These findings were further supported by in vivo pharmacokinetic studies, where EA NPs significantly enhanced the relative bioavailability of EA by 4.69 times.

Can low-dose intravenous bevacizumab be as effective as high-dose bevacizumab for cerebral radiation necrosis?

Although intravenous bevacizumab (IVBEV) is the most promising treatment for cerebral radiation necrosis (CRN), there is no conclusion on the optimal dosage. Our retrospective study aimed to compare the efficacy and safety of high-dose with low-dose IVBEV in treating CRN associated with radiotherapy for brain metastases (BMs). This paper describes 75 patients who were diagnosed with CRN secondary to radiotherapy for BMs, treated with low-dose or high-dose IVBEV and followed up for a minimum of 6 months. The clinical data collected for this study include changes in brain MRI, clinical symptoms, and corticosteroid usage before, during, and after IVBEV treatment. At the 3-month mark following administration of IVBEV, a comparison of two groups revealed that the median percentage decreases in CRN volume on T2-weighted fluid-attenuated inversion recovery and T1-weighted gadolinium contrast-enhanced image (T1CE), as well as the signal ratio reduction on T1CE, were 65.8% versus 64.8% (p = 0.860), 41.2% versus 51.9% (p = 0.396), and 37.4% versus 35.1% (p = 0.271), respectively. Similarly, at 6 months post-IVBEV, the median percentage reductions of the aforementioned parameters were 59.5% versus 62.0% (p = 0.757), 39.1% versus 31.3% (p = 0.851), and 35.4% versus 28.2% (p = 0.083), respectively. Notably, the incidence of grade ≥3 adverse events was higher in the high-dose group (n = 4, 9.8%) than in the low-dose group (n = 0). Among patients with CRN secondary to radiotherapy for BMs, the administration of high-dose IVBEV did not demonstrate superiority over low-dose IVBEV. Moreover, the use of high-dose IVBEV was associated with a higher incidence of grade ≥3 adverse events compared with low-dose IVBEV.

Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics.

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

Pangenome analysis provides insight into the evolution of the orange subfamily and a key gene for citric acid accumulation in citrus fruits.

The orange subfamily (Aurantioideae) contains several Citrus species cultivated worldwide, such as sweet orange and lemon. The origin of Citrus species has long been debated and less is known about the Aurantioideae. Here, we compiled the genome sequences of 314 accessions, de novo assembled the genomes of 12 species and constructed a graph-based pangenome for Aurantioideae. Our analysis indicates that the ancient Indian Plate is the ancestral area for Citrus-related genera and that South Central China is the primary center of origin of the Citrus genus. We found substantial variations in the sequence and expression of the PH4 gene in Citrus relative to Citrus-related genera. Gene editing and biochemical experiments demonstrate a central role for PH4 in the accumulation of citric acid in citrus fruits. This study provides insights into the origin and evolution of the orange subfamily and a regulatory mechanism underpinning the evolution of fruit taste.

Expression and Function of VEGFRs in Normal Epidermis and Psoriatic Lesional Keratinocytes.

The study aimed to explore the expression and function of VEGFRs in normal epidermis and keratinocytes of psoriatic lesions. In this study, the expression and role of VEGFRs in keratinocytes were examined using examples from psoriatic and healthy individuals. The experiment was completed by immunofluorescence analysis, reverse transcription polymerase chain reaction, Western blot, and real-time quantitative RT-PCR after the skin of nonlesional, adjacent, and lesional skin was excised. Observations indicated that in non-lesional psoriatic areas and adjacent lesional areas of the skin of psoriasis patients, the fluorescent signals of VEGFR-1 and VEGFR-2 were strongly labelled with keratinocytes, and in psoriatic lesions, keratinocytes were present throughout the entire thickness of the epidermis, with the exception of the stratum corneum. The distribution of VEGFR-3 in psoriatic nonlesional and adjacent lesional skin was consistent with that in normal epidermis, whereas all layers of the epidermis of psoriatic lesions expressed VEGFR-3. The mRNA expression levels of VEGFR-1,2,3 steadily increased from the normal epidermis to the psoriatic nonlesional, adjacent lesional, and perilesional areas, with the lesional epidermis' keratinocytes exhibiting the greatest levels of mRNA expression. Ca ions upregulate VEGFR-1,2,3 mRNA and protein expression in keratinocytes of nonlesional areas of psoriasis. VEGFRs protein expression and cortical IOD values of psoriatic and normal population cells showed a positive correlation. Hence, in comparison to normal epidermal keratinocytes, psoriatic lesional regions' keratinocytes considerably enhanced their expression of VEGFR-1,2,3 mRNA and protein. The overexpression of VEGFR-1,2,3 in psoriatic lesions may be encouraged by VEGF and Ca þ ions.

Biomimetic Flexible Sensors and Their Applications in Human Health Detection.

Bionic flexible sensors are a new type of biosensor with high sensitivity, selectivity, stability, and reliability to achieve detection in complex natural and physiological environments. They provide efficient, energy-saving and convenient applications in medical monitoring and diagnosis, environmental monitoring, and detection and identification. Combining sensor devices with flexible substrates to imitate flexible structures in living organisms, thus enabling the detection of various physiological signals, has become a hot topic of interest. In the field of human health detection, the application of bionic flexible sensors is flourishing and will evolve into patient-centric diagnosis and treatment in the future of healthcare. In this review, we provide an up-to-date overview of bionic flexible devices for human health detection applications and a comprehensive summary of the research progress and potential of flexible sensors. First, we evaluate the working mechanisms of different classes of bionic flexible sensors, describing the selection and fabrication of bionic flexible materials and their excellent electrochemical properties; then, we introduce some interesting applications for monitoring physical, electrophysiological, chemical, and biological signals according to more segmented health fields (e.g., medical diagnosis, rehabilitation assistance, and sports monitoring). We conclude with a summary of the advantages of current results and the challenges and possible future developments.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Porous Structural Microfluidic Device for Biomedical Diagnosis: A Review.

Microfluidics has recently received more and more attention in applications such as biomedical, chemical and medicine. With the development of microelectronics technology as well as material science in recent years, microfluidic devices have made great progress. Porous structures as a discontinuous medium in which the special flow phenomena of fluids lead to their potential and special applications in microfluidics offer a unique way to develop completely new microfluidic chips. In this article, we firstly introduce the fabrication methods for porous structures of different materials. Then, the physical effects of microfluid flow in porous media and their related physical models are discussed. Finally, the state-of-the-art porous microfluidic chips and their applications in biomedicine are summarized, and we present the current problems and future directions in this field.

Whole-Exome Sequencing Identified Rare Genetic Variants Associated with Undervirilized Genitalia in Taiwanese Pediatric Patients.

Disorders/differences of sex development (DSDs) are a group of rare and phenotypically variable diseases. The underlying genetic causes of most cases of 46XY DSDs remains unknown. Despite the advent of genetic testing, current investigations of the causes of DSDs allow genetic-mechanism identification in about 20-35% of cases. This study aimed primarily to establish a rapid and high-throughput genetic test for undervirilized males with and without additional dysmorphic features. Routine chromosomal and endocrinological investigations were performed as part of DSD evaluation. We applied whole-exome sequencing (WES) complemented with multiplex ligation-dependent probe amplification to seek explainable genetic causes. Integrated computing programs were used to call and predict the functions of genetic variants. We recruited 20 patients and identified the genetic etiologies for 14 (70%) patients. A total of seven of the patients who presented isolated DSD phenotypes were found to have causative variants in the AR, MAP3K1, and FLNA genes. Moreover, the other seven patients presented additional phenotypes beyond undervirilized genitalia. Among them, two patients were compatible with CHARGE syndrome, one with Robinow syndrome, and another three with hypogonadotropic hypogonadism. One patient, who carried a heterozygous FLNA mutation, also harbored a heterozygous PTPN11 mutation and thus presented some phenotypes of Noonan syndrome. We identified several genetic variants (12 nonsense mutations and one microdeletion) that account for syndromic and nonsyndromic DSDs in the Taiwanese population. The identification of these causative genes extended our current understanding of sex development and related congenital disorders.

PARASIAS: A new method for analyzing higher-order tensors with shifting profiles.

Higher-order tensor data analysis has been extensively employed to understand complicated data, such as multi-way GC-MS data in untargeted/targeted analysis. However, the analysis can be complicated when one of the modes shifts e.g., the elution profiles of specific compounds often with respect to retention time; something which violates the assumptions of more traditional models. In this paper, we introduce a new analysis method named PARASIAS for analyzing shifted higher-order tensor data by combining spectral transformation and the simple PARAFAC modeling. The proposed method is validated by applications on both simulated and real multi-way datasets. Compared to the state-of-art PARAFAC2 model, the results indicate that fitting of PARASIAS is 13 times faster on simulated datasets and more than eight times faster on average on the real datasets studied. PARASIAS has significant advantages in terms of model simplicity, convergence speed, the robustness to shift changes in the data, the ability to impose non-negativity constraint on the shift mode and the possibility of easily extending to data with multiple shift modes. However, the resolved profiles of PARASIAS model are always a little worse when the number of components in the data are larger than three and without using additional factors in PARASIAS model. In such cases, more components are necessary for PARASIAS to model the data than that would be needed e.g., by PARAFAC2. The reason for this is also discussed in this work.

Cortical thickness and curvature abnormalities in patients with major depressive disorder with childhood maltreatment: Neural markers of vulnerability?

BACKGROUND: Childhood maltreatment has been related to various disadvantageous lifetime outcomes. However, the brain structural alterations that occur in major depressive disorder (MDD) patients with childhood maltreatment are incompletely investigated. METHODS: We extensively explored the cortical abnormalities including cortical volume, surface area, thickness, sulcal depth, and curvature in maltreated MDD patients. Twoway ANOVA was performed to distinguish the effects of childhood maltreatment and depression on structural abnormalities. Partial correlation analysis was performed to explore the relationship between childhood maltreatment and cortical abnormalities. Moreover, we plotted the receiver operating characteristic curve to examine whether the observed cortical abnormalities could be used as neuro biomarkers to identify maltreated MDD patients. RESULTS: We reach the following findings: (i) relative to MDD without childhood maltreatment, MDD patients with childhood maltreatment existed increased cortical curvature in inferior frontal gyrus; (ii) compared to HC without childhood maltreatment, decreased cortical thickness was observed in anterior cingulate cortex and medial prefrontal cortex in MDD patients with childhood maltreatment; (iii) we confirmed the inseparable relationship between cortical curvature alterations in inferior frontal gyrus as well as childhood maltreatment; (iv) cortical curvature abnormality in inferior frontal gyrus could be applied as neural biomarker for clinical identification of MDD patients with childhood maltreatment. CONCLUSIONS: Childhood maltreatment have a significant effects on cortical thickness and curvature abnormalities involved in inferior frontal gyrus, anterior cingulate cortex and medial prefrontal cortex, constituting the vulnerability to depression.

Aberrant static and dynamic functional connectivity of amygdala subregions in patients with major depressive disorder and childhood maltreatment.

Major depressive disorder (MDD) with childhood maltreatment is a heterogeneous clinical phenotype of depression with prominent features of brain disconnectivity in areas linked to maltreatment-related emotion processing (e.g., the amygdala). However, static and dynamic alterations of functional connectivity in amygdala subregions have not been investigated in MDD with childhood maltreatment. Here, we explored whether amygdala subregions (i.e., medial amygdala [MeA] and lateral amygdala [LA]) exhibited static functional connectivity (sFC) and dynamic functional connectivity (dFC) disruption, and whether these disruptions were related to childhood maltreatment. We compared sFC and dFC patterns in MDD with childhood maltreatment (n = 48), MDD without childhood maltreatment (n = 30), healthy controls with childhood maltreatment (n = 57), and healthy controls without childhood maltreatment (n = 46). The bilateral MeA and LA were selected as the seeds in the FC analysis. The results revealed a functional connectivity disruption pattern in maltreated MDD patients, characterized by sFC and dFC abnormalities involving the MeA, LA, and theory of mind-related brain areas including the middle occipital area, middle frontal gyrus, superior medial frontal gyrus, angular gyrus, supplementary motor areas, middle temporal gyrus, middle cingulate gyrus, and calcarine gyrus. Significant correlations were detected between impaired dFC patterns and childhood maltreatment. Furthermore, the dFC disruption pattern served as a moderator in the relationship between sexual abuse and depression severity. Our findings revealed neurobiological features of childhood maltreatment, providing new evidence regarding vulnerability to psychiatric disorders.

Intrinsic brain abnormalities in female major depressive disorder patients with childhood trauma: A resting-state functional magnetic resonance imaging study.

Objective: Childhood trauma is a strong predictor of major depressive disorder (MDD). Women are more likely to develop MDD than men. However, the neural basis of female MDD patients with childhood trauma remains unclear. We aimed to identify the specific brain regions that are associated with female MDD patients with childhood trauma. Methods: We recruited 16 female MDD patients with childhood trauma, 16 female MDD patients without childhood trauma, and 20 age- and education level-matched healthy controls. All participants underwent resting-state functional magnetic resonance imaging (MRI). Regional brain activity was evaluated as the amplitude of low-frequency fluctuation (ALFF). Furthermore, functional connectivity (FC) analyses were performed on areas with altered ALFF to explore alterations in FC patterns. Results: There was increased ALFF in the left middle frontal gyrus (MFG) and the right postcentral gyrus (PoCG) in MDD with childhood trauma compared with MDD without childhood trauma. The areas with significant ALFF discrepancies were selected as seeds for the FC analyses. There was increased FC between the left MFG and the bilateral putamen gyrus. Moreover, ALFF values were correlated with childhood trauma severity. Conclusion: Our findings revealed abnormal intrinsic brain activity and FC patterns in female MDD patients with childhood trauma, which provides new possibilities for exploring the pathophysiology of this disorder in women.

Aberrant brain connectivity is associated with childhood maltreatment in individuals with major depressive disorder.

Although childhood maltreatment confers a high risk for the development of major depressive disorder, the neurobiological mechanisms underlying this connection remain unknown. The present study sought to identify the specific resting-state networks associated with childhood maltreatment. We recruited major depressive disorder patients with and without a history of childhood maltreatment (n = 31 and n = 30, respectively) and healthy subjects (n = 80). We used independent component analysis to compute inter- and intra- network connectivity. We found that individuals with major depressive disorder and childhood maltreatment could be characterized by the following network disconnectivity model relative to healthy subjects: (i) decreased intra-network connectivity in the left frontoparietal network and increased intra-network connectivity in the right frontoparietal network, (ii) decreased inter-network connectivity in the posterior default mode network-auditory network, posterior default mode network-limbic system, posterior default mode network-anterior default mode network, auditory network-medial visual network, lateral visual network - medial visual network, medial visual network-sensorimotor network, medial visual network - anterior default mode network, occipital pole visual network-dorsal attention network, and posterior default mode network-anterior default mode network, and (iii) increased inter-network connectivity in the sensorimotor network-ventral attention network, and dorsal attention network-ventral attention network. Moreover, we found significant correlations between the severity of childhood maltreatment and the intra-network connectivity of the frontoparietal network. Our study demonstrated that childhood maltreatment is integrally associated with aberrant network architecture in patients with major depressive disorder.

Genome-wide identification of ovate family in Citrus and functional characterization of CitOFP19.

Fruit shape is an important trait for fruit appearance and commercial value. Diversity of fruit-shape has been utilized in the breeding of pummelo (Citrus maxima), a basic species in Citrus. However, little is known about genetic basis of fruit shape in citrus. In this study, we identified 16 OVATE family protein (OFP) genes in the pummelo genome. Phylogenetically, they were classified into three subfamilies, which was consistent with the classification of their Arabidopsis orthologs. Synteny analysis suggested that segment and tandem duplications were responsible for their expansion in pummelo. Expression pattern analysis of Citrus OFPs (CitOFPs) showed that CitOFP19 had significantly higher expression level in the ovaries of round pummelo than in those of pear-shaped pummelo. Heterologous overexpression of CitOFP19 in tomato resulted in pear-shaped ovary and fruit shape. Taken together, this study characterized OVATE gene family in Citrus genome and assessed the function of CitOFP19.

Wearable Near-Field Communication Sensors for Healthcare: Materials, Fabrication and Application.

The wearable device industry is on the rise, with technology applications ranging from wireless communication technologies to the Internet of Things. However, most of the wearable sensors currently on the market are expensive, rigid and bulky, leading to poor data accuracy and uncomfortable wearing experiences. Near-field communication sensors are low-cost, easy-to-manufacture wireless communication technologies that are widely used in many fields, especially in the field of wearable electronic devices. The integration of wireless communication devices and sensors exhibits tremendous potential for these wearable applications by endowing sensors with new features of wireless signal transferring and conferring radio frequency identification or near-field communication devices with a sensing function. Likewise, the development of new materials and intensive research promotes the next generation of ultra-light and soft wearable devices for healthcare. This review begins with an introduction to the different components of near-field communication, with particular emphasis on the antenna design part of near-field communication. We summarize recent advances in different wearable areas of near-field communication sensors, including structural design, material selection, and the state of the art of scenario-based development. The challenges and opportunities relating to wearable near-field communication sensors for healthcare are also discussed.

Altered Variability and Concordance of Dynamic Resting-State Functional Magnetic Resonance Imaging Indices in Patients With Major Depressive Disorder and Childhood Trauma.

Childhood trauma is a non-specific risk factor for major depressive disorder (MDD). resting-state functional magnetic resonance imaging (R-fMRI) studies have demonstrated changes in regional brain activity in patients with MDD who experienced childhood trauma. However, previous studies have mainly focused on static characteristics of regional brain activity. This study aimed to determine the specific brain regions associated with MDD with childhood trauma by performing temporal dynamic analysis of R-fMRI data in three groups of patients: patients with childhood trauma-associated MDD (n = 48), patients without childhood trauma-associated MDD (n = 30), and healthy controls (n = 103). Dynamics and concordance of R-fMRI indices were calculated and analyzed. In patients with childhood trauma-associated MDD, a lower dynamic amplitude of low-frequency fluctuations was found in the left lingual gyrus, whereas a lower dynamic degree of centrality was observed in the right lingual gyrus and right calcarine cortex. Patients with childhood trauma-associated MDD showed a lower voxel-wise concordance in the left middle temporal and bilateral calcarine cortices. Moreover, group differences (depressed or not) significantly moderated the relationship between voxel-wise concordance in the right calcarine cortex and childhood trauma history. Overall, patients with childhood trauma-associated MDD demonstrated aberrant variability and concordance in intrinsic brain activity. These aberrances may be an underlying neurobiological mechanism that explains MDD from the perspective of temporal dynamics.

Secondary-metabolites fingerprinting of Argania spinosa kernels using liquid chromatography-mass spectrometry and chemometrics, for metabolite identification and quantification as well as for geographic classification.

Argan (Argania spinosa L.) fruit kernels' composition has been poorly studied and received less research intensity than the resulting Argan oil. The Moroccan Argan kernels contain a wealth of metabolites and can be investigated for nutritional and health aspects as well as for economic benefits. Ultra-Performance Liquid Chromatography Mass Spectrometry (UPLC-MS) was employed to trace the geographical origin of Argan kernels based on secondary-metabolite profiles. One-hundred and twenty Argan fruit kernels from five regions ('Agadir', 'Ait-Baha' 'Essaouira', 'Tiznit' and 'Taroudant') were studied. Characterization and quantification of 36 secondary metabolites (33 polyphenolic and 3 non-phenolic) were achieved. Those metabolites are highly influenced by the geographic origin. Then, the untargeted UPLC-MS fingerprint was decomposed by metabolomic data handling tools, such as multivariate curve resolution alternating least squares (MCR-ALS) and XCMS. The two resulting data matrices were pretreated and prepared separately by chemometric tools and then two data fusion strategies (low- and mid-levels) were applied on them. The four data sets were comparatively investigated. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Soft Independent Modeling of Class Analogies (SIMCA) were used to classify samples. The exploration or classification models demonstrated a good ability to discriminate and classify the samples in the geographical-origin based classes. Summarized, the developed fingerprints and their metabolomics-based data handling successfully allowed geographical traceability evaluation of Moroccan Argan kernels.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics.

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H3O+, NO+, O2+ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.