Partial Least Squares Discriminant Analysis Research Articles

Identification of high-risk oral leukoplakia (OLK) using combined Raman spectroscopic analysis of brush biopsy and saliva samples: A proof of concept study.

The gold standard method of diagnosis of oral leukoplakia (OLK) is a tissue biopsy followed by histological examination. Raman spectroscopic studies of cytological brush biopsy and saliva samples have previously been shown to differentiate low (no and mild dysplasia) and high risk (moderate and severe dysplasia) OLKs, discriminant models of cellular samples achieving higher specificity, whereas those based on saliva samples achieved higher sensitivity. The current study combines the spectral data sets of cell and saliva samples in an attempt to improve the overall efficiency of the discriminating models. Raman spectral data from cellular (nucleus and cytoplasm) and saliva samples, collected from patients with OLK (n=12), was analysed as a concatenated or fused dataset and as data blocks in a multiblock analysis. The concatenated data was subjected to partial least squares-discriminant analysis (PLS-DA) to discriminate high and low grade dysplasia. Finally, multi-block analysis was performed using sequential orthogonalised PLS-DA, by which each set of data blocks was combined sequentially to provide maximum discrimination. For the concatenated dataset of cells and saliva, 87% sensitivity and 76% specificity were achieved, while in the case of the multi-block analysis, 97% sensitivity and 100% specificity were achieved. It is concluded that multiblock analysis provides maximum sensitivity and specificity using both cell and saliva datasets, compared to fused datasets. This study demonstrates that Raman spectroscopy of minimally invasive brush biopsy and saliva samples could have a role in differentiating high and low-risk OLKs.

Infrared Spectroscopic Electronic Noses: An Innovative Approach for Exhaled Breath Sensing.

Gastric cancer remains a leading cause of cancer-related mortality, requiring the urgent development of innovative diagnostic tools for early detection. This study presents an integrated infrared spectroscopic electronic nose system, a novel device that combines infrared (IR) spectroscopy and electronic nose (eNose) concepts for analyzing volatile organic compounds (VOCs) in exhaled breath. This system was calibrated using relevant gas mixtures and then tested during a feasibility study involving 26 gastric cancer patients and 32 healthy controls using chemometric analyses to distinguish between exhaled breath profiles. The obtained results demonstrated that the integration of IR spectroscopy and eNose technologies significantly enhanced the accuracy of VOCs fingerprinting via principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). Distinct differences between the study groups were revealed with an accuracy of prediction of 0.96 in exhaled breath samples. This combined system offers a high sensitivity and specificity and could potetially facilitate rapid on-site testing rendering the technology an accessible option for early screening particularly in underserved populations.

Surface-Enhanced Raman Spectroscopy (SERS) for the Characterization of Bacterial Isolates in Pus

This study utilized SERS to characterize and identify Klebsiella pneumonia, Staphylococcus aureus, and Pseudomonas aeruginosa which were collected from pus and confirmed using 16S rRNA sequencing. These bacteria are typically cultured and isolated from human wounds. SERS peaks at 575, 629, 930, 1008, 1038, 1099, 1134, 1221, 1283, 1294, 1374, 1589, and 1707 cm−1 were shown to be distinguishing features of these strains. Principal component analysis (PCA) and partial least squares – discriminant analysis (PLS-DA) was applied to SERS spectral datasets. This study demonstrates that combining SERS with PCA and PLS-DA is effective for recognizing and distinguishing these bacterial isolates.

Artificial and Algorithmic Screening of Infrared Spectral Feature Bands of Gastrodia elata to Achieve Rapid Identification of Its Species

ABSTRACTGastrodia elata is a traditional Chinese medicine with medicinal and edible values. In this paper, two kinds of datasets were acquired: partial spectra (artificially obtained peak segment spectra) and full spectra (4000–400 cm−1). Competitive adaptive reweighted sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to extract the characteristic variables of the two datasets, and Partial Least Squares Discriminant Analysis (PLS‐DA) models, Support Vector Machines (SVM) models, Random Forests (RF) models, and Residual convolutional neural networks (ResNet) were established. It was found that among the PLS‐DA models whole‐MSC‐CARS‐PLS‐DA was optimal, with a Root Mean Square Error of Prediction (RMSEP) of 0.0658; among the SVM models Partial‐Standard Normal Variable (SNV‐SPA‐SVM was the best, with a kernel parameter of 0.1768 and the lowest number of support vectors; among the RF models Partial‐SNV‐RF is optimal, but not as effective as the first two models. The loss value of the ResNet model built based on effective information is 0.001, and the model building time is short and directly uses the original data. Therefore, the ResNet model based on feature bands is the most suitable for practical application compared with other models.

PGI Chianina meat traceability by means of multivariate HRMAS-NMR data analysis.

Food quality is a crucial issue for producers and consumers, either dealing with commodities according to basic standards or with top quality products. Among the parameters contributing to quality, the place of origin is considered to be one of the most relevant, especially for protected denomination of origin and protected geographical indication foods, PDO and PGI, respectively. These labels have been designed by the EU to protect and valorise high quality foodstuff produced in limited areas and to ensure higher incomes to farmers. Such economic interest has prompted the use of several analytical techniques for the traceability issue. Here we proposed the multivariate HRMAS-NMR (i.e. High-Resolution Magic Angle Spinning-Nuclear Magnetic Resonance) data analysis for the traceability of PGI Chianina meat, specifically for the semitendinosus muscle. The metabolic profile of Chianina meat assessed by HRMAS-NMR spectroscopy was analysed by means of PCA (Principal Component Analysis), PLS-DA (Partial Least Square-Discriminant Analysis) and OPLS-DA (Orthogonal Partial Least Square-Discriminant Analysis) in order to classify samples according to the geographical origin. The built models provided an excellent separation between PGI and non-PGI, and the use of the VIP (Valuable Influence on Projection) values allowed us to identify metabolites contributing significantly to classification. Specifically, we found molecules such as amino acids, carnosine, some nucleosides, and fatty acids to be responsible for the discrimination: the fatty acid profile of meat is affected by the different feeding systems, while the other metabolites are involved in the ageing process of meat (ATP degradation during post mortem and proteolysis).

SERS profiling of blood serum filtrate components from patients with type II diabetes using 100 kDa filtration devices.

Blood carries some of the most valuable biomarkers for disease screening as it interacts with various tissues and organs in the body. Human blood serum is a reservoir of high molecular weight fraction (HMWF) and low molecular weight fraction (LMWF) proteins. The LMWF proteins are considered disease marker proteins and are often suppressed by HMWF proteins during analysis. This issue is addressed by using a filtration device to isolate the filtrate portion from blood serum samples having biomarker proteins up to the size of the cutoff value of the filtration device. In this research, 100 kDa filter devices are employed to obtain the filtrate portions from blood serum samples of type II diabetes mellitus patients and healthy volunteers, followed by characterization using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles (Ag NPs) as the SERS substrate. By using this approach, the collected filtrate is expected to contain marker proteins at a size of <100 kDa, which are associated with type II diabetes. These marker proteins are in a narrow size range (cutoff value of 100 kDa). Hence, they may be more easily identified by their characteristic SERS spectral features as compared to their analysis in the respective whole blood serum samples due to the exclusion of larger size proteins. These proteins that are present in the filtrate portions of type II diabetes may include adiponectin, C-reactive protein, insulin, leptin, RBP4, IL-6, TNF-α, Fibroblast Growth Factor 21 (FGF21), albumin, transthyretin, alpha-antitrypsin, transferrin, apolipoprotein A-1 (ApoA-1) and fetuin-A. Some prominent SERS bands are observed at 356 cm-1, 435 cm-1, 490 cm-1, 548 cm-1, 596 cm-1, 729 cm-1, 746 cm-1, 950 cm-1, 1330 cm-1, 1362 cm-1, 1573 cm-1 and 1689 cm-1, which differentiate type II diabetes patients from healthy individuals. Moreover, the SERS spectral data sets of various samples are classified using two chemometric approaches: principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The validation of the PLS-DA analysis classification model is indicated with 81% accuracy, 79% specificity, and 85% sensitivity, having a value of AUC = 0.75.

Comparative metabolomic study of twelve Acacia species by UHPLC-q-tof-ESI-MS coupled with chemometrics in correlation with antibacterial activity.

Genus Acacia comprises around 1500 species. They are widely used to treat inflammation as well as bacterial and fungal infections as they are enriched in phytochemicals, especially phenolics. The aim of this study was to evaluate the antibacterial activity of leaves' methanolic extracts of twelve Acacia species growing in Egypt against Vibrio parahaemolyticus, Salmonella enterica, Listeria monocytogens, Klebsiella pnemoniae, Bacillus aquimaris, Bacillus subtilis, and Escherichia coli. These species are Acacia nilotica (wild and cultivated), Acacia seyal, Acacia auriculiformis, Acacia saligna, Acacia xanthophloea, Acacia tortilis subsp. raddiana (Gabal Elba and Aswan), Acacia tortilis, Acacia laeta (wild and cultivated), and Acacia albida. Furthermore, to study the metabolomic composition and variation among these species using ultra-high-performance liquid chromatography-electrospray ionization quadrupole time of flight mass spectrometry (UHPLC-q-tof-ESI-MS) coupled with multivariate statistical analysis and correlate it to the antibacterial potential. Results showed that Acacia nilotica (AN) has superior antibacterial activity over the other species. In addition, it exhibited a distinct segregation in Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). Full profiling of AN using UHPLC-ESI-q-tof-MS revealed 42 phenolics mainly catechins. It was further subjected to bio-guided fractionation and revealed the presence of methyl gallic acid, gallic acid, catechin gallate, and digallate isomers in its most bioactive fraction. These compounds were identical to the compounds annotated as VIPs and were responsible for the segregation of AN in both PCA and PLS-DA analyses. Hence, this study sheds light on the use of chemometrics as an early tool for the detection of bioactive compounds.

Evaluation of volatile safety in children's play mats based on non-targeted screening and risk prioritization.

Given the widespread use of play mats and their close contact with children, it is crucial to identify potential chemical hazards associated with these products. In this study, 34 play mats, comprising four materials (3 EPE, 15 XPE, 5 PVC, 11 EVA), were subjected to non-targeted screening based on headspace gas chromatography-Orbitrap high-resolution mass spectrometry (HS-GC-Orbitrap HRMS), which screened out 71 volatile substances. The 20 significantly different substances were recognized by partial least squares discriminant analysis (PLS-DA), while the characteristic substances in each type of play mat were determined through clustering heat map and Venn diagram. To facilitate the rapid identification of high-risk substances, a risk scoring scheme was developed, taking into account detection rates, average peak areas, and substance hazardousness. The substances were then ranked based on their total risk scores. The distribution of peak areas for the top 14 high-risk substances, including α-methylstyrene, formamide, and toluene, in products was detailed, highlighting the need for further attention to these chemicals. Overall, the study found the volatile chemical safety of the four play mat types to be in the order of EPE > XPE > PVC > EVA. The differentiation analysis and risk scoring scheme proposed in this paper provide significant insights for the efficient regulation and quality improvement of play mats and other products.

Non-targeted analysis using gas chromatography mass spectrometry to assess the free and bound aroma fingerprints of the emblematic Greek white winegrape varieties and guarantee varietal authenticity using multivariate chemometrics.

A non-targeted analytical approach using gas chromatography-mass spectrometry (GC-MS) is proposed for the analysis of the free and bound volatile fractions of three emblematic indigenous Greek white winegrape varieties belonging to Vidiano, Malagousia, and Savvatiano and establish volatile varietal markers using multivariate chemometrics. A total of 89 free and 103 bound volatile compounds were identified, categorized into alcohols, aldehydes, esters, acids, terpenes, norisoprenoids, C6 compounds, phenols, and ketones. A robust Partial Least Squares Discriminant Analysis (PLS-DA) prediction model was developed and validated, and successfully classified the grape samples according to the variety with 100% accuracy, demonstrating the potential of volatile profiling as a non-targeted fingerprinting approach for varietal discrimination. Malagousia variety exhibited the highest concentration of terpenes, particularly in the bound fraction, highlighting its intense floral character. The proposed analytical workflow was successfully applied for the investigation of the unique aroma profiles of Greek white winegrape varieties. The findings contribute to the understanding of unique volatile fingerprints of the winegrape varieties and support the adaptation of vineyard and winery practices to enhance their varietal expression.

Differentiation of the chronic lymphocytic leukemia response to ibrutinib and acalabrutinib treatment by single-cell MALDI-TOF MS imaging.

Ibrutinib and acalabrutinib, Bruton's tyrosine kinase inhibitors (BTKi) used for chronic lymphocytic leukemia (CLL) treatment, aim the same target but their off-target effects are different. The aim of this study was to use single-cell MALDI TOF mass spectrometry imaging to compare the CD19+lymphocytes' mass spectra in untreated and ibrutinib- or acalabrutinib-treated subjects in order to better understand the therapeutic effect of BTKi. 180 cells from 9 male subjects divided in 3 groups (untreated, ibrutinib-treated and acalabrutinib-treated) were analyzed using MALDI-TOF mass spectrometry analyzer. Mass spectra were acquired in the 300-600 Da mass range. Partial least squares discriminant analysis (PLS-DA) was used for evaluation of mass spectra, while Volcano plots and Venn diagram were used for a review of significantly altered m/z values. Cross-validated PLS-DA classification accuracy of cells was 85 %. Ibrutinib-treated cells overlap with the untreated cell population, while acalabrutinib-treated cells form a separate class. 13 m/z signals were specific for each BTKi group. In conclusion, single-cell MALDI-TOF mass spectrometry imaging detects differences in the mass spectra of CD19+ lymphocytes treated with two different BTKi. Drug-specific m/z signal clusters can be identified as a chemical fingerprint of the BTKi effect and represent candidates for the therapeutic response biomarkers.

Inter- and intra-varietal clonal differences influence the aroma compound profiles of wines analyzed by GC-MS and GC-IMS.

To investigate the impact of genetic factors on wine aroma, wines made from 22 clones of five grape varieties (Vitis vinifera L.) were used to analyze the volatile compounds by headspace solid phase microextraction gas chromatography mass spectrometer (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Results showed that 52 and 49 aroma compounds were identified from 22 clones of wines by two technologies, respectively. Esters were the most abundant compounds, followed by alcohols and aldehydes. The aroma profiles demonstrated significant varietal and clonal diversity, the clones with the highest aroma compound content were CH VCR6, PN VCR20, CS VCR11, ML VCR101, and CF 678. Partial least squares discriminant analysis (PLS-DA) identified decanoic acid, 1-heptanol, diethyl succinate, ethyl octanoate, and octanal as key biomarkers for distinguishing 22 clones of wines. Our results revealed that white wine CH VCR6 and red wine CS VCR11 possessed the most complex aromas. These findings address the research gap concerning the genetic determinants of wine aroma, highlighting the significance of grape variety and clone selection in developing wines with desirable sensory attributes.

Development of essential oil diffusion matrices using non-ionic surfactants-supported NADES and hydrophobic NADES.

Natural deep eutectic solvents (NADES) represent a significant advance in green chemistry, offering an eco-friendly alternative to conventional organic solvents for applications in extraction, reaction media, and formulations. This study explores the application of NADES in essential oil formulations, using lavender essential oil (LEO) to investigate the solubilization and release of volatile organic compounds (VOCs). Two distinct NADES formulations were evaluated: hydrophilic NADES combined with surfactants, and hydrophobic NADES. The results show that hydrophilic NADES, when combined with non-ionic surfactants, form stable emulsions that effectively solubilize and release LEO's VOCs, overcoming the high hydrophilicity limitation of NADES. Headspace analysis and gas chromatography-mass spectrometry (GC-MS) identified 25 VOCs, with differential components analyzed through orthogonal partial least-squares discriminant analysis (OPLS-DA). VOCs were well-clustered in OPLS-DA and principal component analysis (PCA), with five clearly differentiated groups (C, N1, N3, N5, and N9). However, challenges remain, particularly in the solubilization and gradual release of VOCs. In contrast, hydrophobic NADES demonstrated a controlled release mechanism, effectively encapsulating and steadily releasing VOCs, making them advantageous for sustained fragrance delivery. The study further emphasizes the importance of the Hydrophilic-Lipophilic Balance (HLB) in NADES formulations, showing that adjusting HLB through surfactant concentrations significantly influences VOC release dynamics. In conclusion, this research highlights the potential of NADES as efficient, eco-friendly solvent systems for essential oil applications. By providing insights into optimizing NADES compositions for specific solubility and release profiles, the study identifies both challenges and opportunities, paving the way for further advancements in green chemistry.

Serum Exosome SERS Assay Based on TiN‐Ag@Ag Sol Composite Substrate and Its Application in the Diagnosis of Gastric Cancer

ABSTRACTGastric cancer (GC) is a highly lethal malignancy, seriously threatening people's physical health. Accurate screening of gastric cancer could improve the survival rate of patients. Therefore, exploring noninvasive and efficient cancer screening methods for gastric cancer is of great significance. In the past few years, exosomes have received much attention for their potential in disease diagnosis and treatment. Here, the aim of this study was to explore the detection of serum exosomes via surface‐enhanced Raman spectroscopy (SERS) technique based on TiN‐Ag@Ag sol composite substrate, and its potential application in gastric cancer diagnosis is evaluated. Exosomes were extracted from the serum of 31 GC patients and 31 healthy controls (HC) using an exosome kit. This study used various machine learning algorithms such as principal component analysis linear discriminant analysis (PCA‐LDA), partial least squares discriminant analysis (PLS‐DA), support vector machine (SVM), and k‐nearest neighbor (KNN) algorithm to analyze SERS spectra, in order to distinguish between HC and GC. The results show that the k‐nearest neighbor algorithm performs the best in HC and GC classification. These results indicate that the combination of SERS and machine learning methods provides a new technological approach for gastric cancer screening. This study offers a new proposal for the universal applicability of analysis and identification with SERS of serum exosomes samples in clinical diagnosis.

Differentiation of Escherichia coli and Shigella flexneri by Metabolite Profiles Obtained Using Gold Nanoparticles-Based Surface-Assisted Laser Desorption/Ionization Mass Spectrometry.

Escherichia coli and Shigella flexneri are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying E. coli and S. flexneri by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from E. coli and S. flexneri cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system. The extracts were analyzed using Au-SALDI MS in positive ion mode, and reference spectra, compiled from 30 spectra for each bacterium, were added to the database. Identification of bacteria based on metabolite fingerprints in the Biotyper database produced correct results with scores exceeding 2.75. The results of Partial Least Squares-Discriminant Analysis (PLS-DA) demonstrated that the metabolomic approach could accurately differentiate the microorganisms under study. A panel of nine m/z values was also identified, each with an area under the ROC curve of above 0.8, enabling accurate identification of E. coli and S. flexneri. A search of metabolite databases allowed the following compounds to be assigned to the selected m/z values: N-acetylputrescine, arginine, 2-maleylacetate, benzoyl phosphate, N8-acetylspermidine, alanyl-glutamate, 4-hydroxy-2,3,4,5-tetrahydrodipicolinate, and sucrose. The analyses showed that identification of bacteria based on metabolite profiles obtained by the Au-SALDI MS method is feasible and can be useful for distinguishing closely related microorganisms that are difficult to differentiate by other techniques.

Effects of harvest time and plant part on essential oils, phenolics, and antioxidant activity in Lippia citriodora

This study investigates the effects of harvest time and plant parts on the concentrations of essential oils, phenolic content, flavonoids, and antioxidant activities (ABTS and DPPH) in the Lippia citriodora plant to determine the best outcome. The experiment consists of four different harvest times during flowering period and two plant parts (upper and lower). Harvest times were scheduled at weekly intervals.Significant variations were observed during experiment, Essential oil (EO) content reached peak value at the first harvest in upper parts of plants (L1U: 1.18%) and lowest value in lower parts by the fourth harvest (L4L: 0.25%). The highest phenolic content determined at the first harvest (L1: 44.04 mg GAE/g DW), while flavonoid levels reached peak value at the fourth harvest (L4: 314.07 mg rutin/g DW). Antioxidant activities, measured by ABTS and DPPH assays, were significantly greater in lower plant parts. Partial Least Squares Discriminant Analysis (PLS-DA) and Principal Component Analysis Discriminant Analysis (PCA-DA) confirmed clear distinctions between the upper and lower parts of the plant regarding bioactive compounds concentrations. The findings shows the importance of targeted harvest timing and plant parts in optimizing bioactive compound in Lippia citriodora, with implications for getting better benefits from plant.

Electrochemical Fingerprint Analyses Using Voltammetry and Liquid Chromatography Coupled with Multivariate Analyses for the Discrimination of Schisandra Fruits.

The appearances of Schisandrae Sphenantherae Fructus (SSF) and Schisandrae Chinensis Fructus (SCF) are very similar. Thus, being able to distinguish between SSF and SCF is useful for the quality control of these herbal medicines. In this study, two kinds of electrochemical fingerprint analyses using voltammetry or HPLC with electrochemical detection (HPLC-ECD) were developed in combination with multivariate analysis for discriminating between SSF and SCF. The data sets of the oxidation current values from voltammograms of SSF and SCF samples ranging from +0.5 to +1.0 V were applied to perform a partial least squares discriminant analysis (PLS-DA). Moreover, the data sets of the current heights of the characteristic target peaks on the chromatograms at an applied potential of +0.9 V were also applied to perform PLS-DA. In each PLS-DA obtained from the voltammograms and chromatograms, the scores for the SSF samples were plotted on a different region compared with the scores for the SCF samples. Considering the results of the cross-validation, the HPLC-ECD clearly discriminated between the SSF and SCF samples when compared with the voltammetry. In conclusion, our results show that the present electrochemical fingerprint analyses coupled with PLS-DA are useful as a means for discriminating between the SSF and SCF samples.

Rapid Non-Destructive Detection of Rice Seed Vigor via Terahertz Spectroscopy

Rice seed vigor significantly impacts yield, making the selection of high-vigor seeds crucial for agricultural production. Traditional methods for assessing seed vigor are time-consuming and destructive. This study aimed to develop a rapid, non-destructive method for evaluating rice seed vigor using terahertz spectroscopy. Rice seeds with varying vigor levels were prepared through high-temperature and high-humidity aging and classified into high-, low-, and non-vigorous groups based on germination performance. Terahertz transmission imaging (0.1–3 THz) was conducted on 420 seeds, and spectral data were preprocessed using several advanced data processing techniques, including competitive adaptive reweighting (CARS), uninformative variable elimination (UVE), and principal component analysis (PCA). Three chemometric models, namely random forest (RF), K-nearest neighbors (KNN), and partial least squares–discriminant analysis (PLS-DA), were established. The model based on CARS-KNN after band selection achieved the highest prediction accuracy of 97.14%. The results indicate that terahertz spectroscopy combined with band selection methods provides a reliable, non-destructive approach for rice seed vigor assessment, offering significant potential for agricultural quality control.

Bioreactor contamination monitoring using off-gassed volatile organic compounds (VOCs).

Metabolically active cells emit volatile organic compounds (VOCs) that can be used in real time to non-invasively monitor the health of cell cultures. We utilized these naturally occurring VOCs in an adapted culture method to detect differences in culturing Chinese hamster ovary (CHO) cells with and without Staphylococcus epidermidis and Aspergillus fumigatus contaminations. The VOC emissions from the cell cultures were extracted and measured from the culture flask headspace using polydimethylsiloxane (PDMS)-coated Twisters, which were subjected to thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. In our initial time points of 1 and 2 h, we detected VOC signatures that differentiated the cultures earlier than traditional plating techniques or visualization methods. Partial least squares-discriminant analysis (PLS-DA) models were built to differentiate the analytes from the CHO cells and S. epidermidis- and A. fumigatus-inoculated CHO cultures. A total of 41 compounds with a variable importance in projection (VIP) score greater than 1 was obtained across the models. Similarly, based on the PLS regression analyses to predict the cell concentration of S. epidermidis (R2 = 0.891) and A. fumigatus (R2 = 0.375), 15 and 20 relevant compounds were putatively identified, respectively; two known compounds overlapped between the two microbes. Some of the compounds were unidentified and future studies will determine the relationship between the VOCs and the metabolic changes in contaminated cultures.

Perfluorinated compounds linked to central precocious puberty in girls during COVID-19: an untargeted metabolomics study.

The incidence of central precocious puberty (CPP) in girls increased significantly during the COVID-19 pandemic. This study aimed to explore the impact of perfluorinated endocrine disruptors on CPP through metabolomics analysis in girls from Hainan Province, China. Serum samples from 100 girls with CPP and 100 healthy controls were collected. Untargeted metabolomics profiling was performed using ultra-high performance liquid chromatography coupled with quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive-Orbitrap-MS). Differentially expressed metabolites (DEMs) were screened, and pathway enrichment analysis was conducted. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) revealed distinct metabolic profiles between the CPP and control groups. A total of 511 metabolites were identified, including 296 up-regulated DEMs and 255 down-regulated DEMs. Three perfluorinated compounds-PFSM-perfluoroalkyl_sulfonamide_Me, PFSM-FSAA, and PFCA-unsaturated-were significantly upregulated in the CPP group. KEGG pathway enrichment analysis suggested the involvement of multiple pathways in the CPP process regulated by these compounds. Perfluorinated compounds may promote CPP in girls by interfering with various pathways and affecting the hypothalamic-pituitary-gonadal axis function. This study highlighted the need for further research and public health measures to address environmental endocrine disruptors.

Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law's texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire-ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm's effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83-100% and 95.89-99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity.