Squares-discriminant Analysis Research Articles

Complex Microbial Fertilizer Promotes the Growth of Summer-Sown Short-Season-Cultivated Cotton and Increases Cotton Yield in the Yangtze River Basin by Changing the Soil Microbial Community Structure

The summer-sowing short-season cotton cultivation model is an important method for simplified and mechanized cotton planting in the Yangtze River Basin. However, the effects of microbial fertilizers on cotton growth and soil under this model remain unclear. In 2023, we conducted a systematic analysis on the application of microbial fertilizers (compost) at varying levels (CK, MF1, MF2, and MF3) during different growth stages of cotton (bud, flowering, bolling, and boll opening). Results showed that appropriate microbial fertilizer application (MF2 and MF3) enhanced soil bacterial and fungal diversity, enriched beneficial microorganisms (e.g., Acidobacteriota and Candidatus Udaeobacter), improved soil nutrient availability, and increased antioxidant enzyme activity (POD, SOD), while reducing membrane lipid peroxidation (MDA). These effects led to significant improvements in yield traits, such as cotton plant height, number of fruiting branches and bolls, boll weight, and coat weight. The highest microbial fertilizer application level (MF3) resulted in a 54.35% increase in seed yield and a 75.37% increase in lint yield compared to CK. PLS-DA (Partial Least Squares Discriminant Analysis) and multivariate statistical analyses revealed that microbial fertilizer application fine-tuned soil microbial community composition, emphasizing the dynamic balance of the microbial ecosystem. This study provides scientific support for optimizing microbial fertilizer strategies to enhance the yield and quality of summer-sown short-season cotton and promote sustainable agriculture.

Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.

Ulcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify non-invasive biomarkers of disease extent may inform therapeutic decisions and risk stratification. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to identify the metabolites. Least absolute shrinkage and selection operator (LASSO) regression, random forest-recursive feature elimination (RF-RFE), and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen metabolites. Five machine learning algorithms (XGboost, KNN, NB, RF, and SVM) were used to construct prediction model. A total of 220 differential metabolites between the patients with UC and healthy controls (HCs) were confirmed by the OPLS-DA model. Machine learning screened eight essential metabolites for distinguishing patients with UC from HCs. A total of 23, 6, and 6 differential metabolites were obtained through machine learning between group E1 and E2, E1 and E3, and E2 and E3. The RF model had a prediction accuracy of up to 100% in all three training sets. The serum levels of tridecanoic acid were significantly lower and pelargonic acid were significantly higher in patients with extensive colitis than in the other groups. The serum level of asparaginyl valine in patients with rectal UC was significantly lower than that in E2 and E3 groups. Our findings revealed the metabolic landscape of UC and identified biomarkers for different disease extents, confirming the value of metabolites in predicting the occurrence and progression of UC.

Alkaloid profile of Italian alpine milk.

This study examines the alkaloid profiles in alpine milk. Alkaloids could pose a health concern but also prove interesting from a geographic traceability perspective. Over three consecutive days, 48 daily milk samples were collected from 16 lactating cows grazing on two alpine pastures in Northeast Italy, with 8 cows from each pasture. Simultaneously, alpine herbs selecting by the cows during grazing were collected using the hand-plucking technique. Additionally, 12 milk mass samples were obtained from an entire herd of 110 cows. Liquid chromatography coupled with high-resolution mass spectrometry was employed to analyze both herbage and milk samples, identifying 41 alkaloids with pure standards and putatively identifying another 116. The results revealed a transfer of 0.4% for pyrrolizidine alkaloids, 2.7% for indole alkaloids, and 12% for steroidal alkaloids from herbs to milk. A partial least squares-discriminant analysis model based on the alkaloid profiles achieved a correct reclassification of 67% of milk samples from cows grazing on the two distinct pastures. Despite the minimal transfer, which should be considered positive in terms of health, it opens the door to interesting studies on the use of alkaloids as traceability markers for mountain products. PRACTICAL APPLICATION: The study provides a novel perspective interaction between alpine grazing systems and milk composition. This research could be useful for enhancing mountain pasture products in terms of healthiness and can help prevent fraud on the declaration of origin.

Non-targeted metabolomics reveals fatty acid and associated pathways driving resistance to whitefly and tomato leafminer in wild tomato accessions.

Wild tomato species exhibit natural insect resistance, yet the specific secondary metabolites and underlying mechanisms governing the resistance remain unclear. Moreover, defense expression dynamically adapts to insect herbivory, causing significant metabolic changes and species-specific secondary metabolite accumulation. The present study aims to identify the resistance-related metabolites in wild tomato accessions that influence the defense mechanism against whitefly (Bemisia tabaci Asia II 7) and leafminer (Phthorimaea absoluta). In this study, LC-HRMS-based non-targeted metabolomics of resistant wild (Solanum cheesmaniae and Solanum galapagense) and susceptible cultivated (Solanum lycopersicum) accessions following 6- and 12-h post-infestation (hpi) by B. tabaci Asia II 7 and P. absoluta revealed distinct sets of resistance-related constitutive (RRC) and induced (RRI) metabolites. The key resistance-related metabolites were those involved in the fatty acid and associated biosynthesis pathways (e.g., triacontane, di-heptanoic acid, dodecanoic acid, undecanoic acid, N-hexadecanoic acid, pentacosane, monogalactosyldiacylglycerols, sphinganine, and 12-hydroxyjasmonic acid), which are recognized for their direct or indirect role in mediating plant defense against insects. Additionally, the differential accumulation of metabolites was evident through partial least squares-discriminant analysis (PLS-DA), highlighting differences in metabolite profiles between resistant and susceptible accessions at 6 and 12 hpi of B. tabaci and P. absoluta. Volcano plot analysis revealed a higher number of significantly upregulated metabolites in wild accessions following herbivory. Moreover, wild tomato accessions responded uniquely to B. tabaci and P. absoluta, highlighting species-specific metabolic responses of tomato accessions to the two feeding guilds. This study uncovered biochemical mechanisms governing resistance in wild tomato accessions, elucidated the influence of dual herbivory on the plant metabolome, and offered well-characterized parent materials and candidate metabolites for breeding insect-resistant varieties.

Near-Infrared Spectroscopy as a Tool for the Traceability Control of High-Quality Iberian Dry-Cured Meat Products

Near-infrared spectroscopy (NIRS) was evaluated to trace the high hydrostatic pressure (HHP) processing and preservation temperature (4 °C vs. 20 °C) over the course of a long term in vacuum-packaged Iberian dry-cured tenderloin (Iliopsoas et psoas minor). Spectra were obtained from a total of 298 samples, without opening the package, using a handheld MicroNIRTM 1700 OnSite-W microspectrophotometer (908.1 nm–1676.2 nm) (VIAVI Solutions Inc., United States). The discriminant models were developed by means of partial least squares-discriminant analysis (PLS-DA). The models obtained were capable of correctly classifying more than 60% of the samples according to their HHP processing, while almost 100% of the samples were correctly classified according to the temperature at which the samples were preserved. Thus, NIRS could help to support the traceability of treatments that represent a high added value to the product, such as HHP in premium Iberian dry-cured products.

Untargeted urine metabolomics reveals dynamic metabolic differences and key biomarkers across different stages of Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) often serving as its precursor stage. Early intervention at the MCI stage can significantly delay AD onset.MethodsThis study employed untargeted urine metabolomics, with data obtained from the MetaboLights database (MTBLS8662), combined with orthogonal partial least squares-discriminant analysis (OPLS-DA) to examine metabolic differences across different stages of AD progression. A decision tree approach was used to identify key metabolites within significantly enriched pathways. These key metabolites were then utilized to construct and validate an AD progression prediction model.ResultsThe OPLS-DA model effectively distinguished the metabolic characteristics at different stages. Pathway enrichment analysis revealed that Drug metabolism was significantly enriched across all stages, while Retinol metabolism was particularly prominent during the transition stages. Key metabolites such as Theophylline, Vanillylmandelic Acid (VMA), and Adenosine showed significant differencesdifferencesin the early stages of the disease, whereas 1,7-Dimethyluric Acid, Cystathionine, and Indole exhibited strong predictive value during the MCI to AD transition. These metabolites play a crucial role in monitoring AD progression. Predictive models based on these metabolites demonstrated excellent classification and prediction capabilities.ConclusionThis study systematically analyzed the dynamic metabolic differences during the progression of AD and identified key metabolites and pathways as potential biomarkers for early prediction and intervention. Utilizing urinary metabolomics, the findings provide a theoretical basis for monitoring AD progression and contribute to improving prevention and intervention strategies, thereby potentially delaying disease progression.

Changes in the intestinal microbiota induced by the postnatal environment and their association with hypertension.

It has been established that cross-fostering impacts the development of hypertension in spontaneously hypertensive rats (SHR). However, the ability of the cross-fostering protocol to shape gut microbiota profile in SHR and impact hypertension is not known. In this sense, the current study explored the influence of normotensive and hypertensive postnatal environments on the intestinal microbiota structure, composition, and functional capacity of SHR and Wistar rats. Our findings revealed significant differences in the microbiota's composition and its metabolic activity in young non-fostered SHR (SS) vs. Wistar (WW) rats, even before hypertension onset, characterized by a reduction of the "low-abundance" bacterial genera, a diminished availability of fecal butyrate and elevated hydrogen sulfide (H2S) production by the SS gut microbiota. Despite influencing the microbiota of both strains, cross-fostering did not fully replicate the microbiota composition of the naturally reared groups in the SHR nursed by Wistar mothers (SW), or in the Wistar rats breastfed by SHR mothers (WS). The SW group had fewer significant genera identified at the Partial Least Squares Discriminant Analysis (PLS-DA), despite resembling the genera profile identified in the normotensive group. While sharing bacterial genera with both SS and WW groups, the WS group is distinguished by its unique microbial composition, particularly by a greater diversity of the 'low-abundance' bacterial genera. Moreover, decreased systolic blood pressure was observed in the SW group compared to the SS group in adulthood. Thus, we could establish a link between microbiota composition and hypertension development, associating it with the loss of the "low-abundance" bacterial taxa. Our data suggest that the postnatal environment is pivotal to promoting gut microbiota compositional changes and contributes to hypertension development.

Authentication of Java Turmeric (Curcuma xanthorrhiza) from Turmeric (Curcuma longa) Using a Combination of UV-VIS-IR Spectrum and Chemometrics

Java turmeric (Curcuma xanthorriza) and turmeric (Curcuma longa) show similar colors, so they have the potential to be adulterated with each other, especially if they are presented in powder. This research aims to develop an analytical method for authenticating both types of samples with adulterant concentrations of 0.01% w/w and 0.005% w/w for the infrared range and 0.5 μg/g and 1 μg/g for the UV-Vis range. The pure sample was extracted for 40 minutes with 1:10 ethanol using ultrasonication. The extract was then concentrated using a rotary evaporator and freeze dryer. Adulterant samples were prepared by mixing both types of extracts. The absorption of the solution was measured at a wavelength of 200–800 nm and a wave number of 4000–400 cm-1. Multivariate analysis using partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) was performed on the spectra. PLS-DA has not been able to authenticate adulterated samples. However, SIMCA analysis can detect differences between pure curcuma and adulterated samples in the infrared range until a concentration of 0.005% w/w, while it can only authenticate correctly in the UV-Vis range until a 1 μg/g concentration.

Application of fourier transform infrared vibrational spectroscopy in identifying early biochemical changes in lipid profiles of individuals undergoing Roux-en-y gastric bypass

BackgroundFourier transform infrared spectroscopy (FTIR) is an analytical technique increasingly applied in biological analysis. This study investigates the application of FTIR to identify early biochemical changes, particularly in lipid profiles, in individuals undergoing Roux-en-Y gastric bypass (RYGB).MethodsAn observational study involving patients from a university hospital’s Bariatric and Metabolic Surgery Program, with evaluations performed before (T0) and two months after (T1) RYGB. Biochemical parameters, anthropometric data, and body composition were assessed. FTIR spectra were pre-processed and analyzed using Principal Component Analysis and Partial Least Squares Discriminant Analysis. The normality of the data was evaluated using the Kolmogorov-Smirnov test, followed by paired T-tests or Wilcoxon tests as appropriate. Spearman correlation analysis of spectral information with biochemical parameters was also performed. A significance level of p < 0.05 was set for all tests. The university hospital’s Research Ethics Committee approved the study (protocol CAAE 59075722.7.0000.5071).ResultsThe study evaluated 29 individuals (86.2% female) with a mean age of 41.2 ± 7.8 years. Significant differences were observed in anthropometric parameters and body composition (p < 0.001). Additionally, early improvements in the lipid profile were noted, with significant decreases in triglycerides, total cholesterol, and LDL cholesterol (p < 0.05) just two months post-surgery. FTIR identified correlations between biochemical parameters and specific spectral regions at T0 and T1. Notably, serum triglycerides showed a significant correlation with the lipid-specific spectral region (1796–1685 cm− 1) at both time points (p < 0.05).ConclusionFTIR can effectively monitor biochemical changes in RYGB surgery patients. The spectral range associated with lipid functional groups (1796 –1675 cm⁻¹) showed a significant relationship with serum triglyceride levels before and after RYGB. Additionally, various biochemical parameters exhibited strong correlations with other spectral regions, implying that the serum spectral profile can indicate biochemical variations at different post-surgery stages.Trial registrationBrazilian Registry of Clinical Trials (Rebec), September 5, 2022, protocol RBR-26chs2g.

Rainy and Dry Seasons Are Relevant Factors Affecting Chemical and Antioxidant Properties of Meliponini Honey.

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

Altitude-Driven Variations in Nutritional, Bioactive, and Mineral Profiles of Hawthorn (Crataegus spp.).

Hawthorn (Crataegus spp.), a plant widely distributed in temperate and subtropical regions, is valued for its bioactive compounds and diverse health benefits. Known for its remarkable adaptability to various environmental conditions, hawthorn thrives across different altitudes, but these environmental factors, particularly altitude, significantly influence the accumulation of its bioactive substances. This study investigates the effects of altitude on hawthorn's nutritional, bioactive, and mineral profiles to provide insights into its cultivation and utilization. Through comprehensive analysis of 20 nutritional indicators from high- and low-altitude samples, including essential nutrients, bioactive compounds, and trace elements, multivariate analyses such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) revealed clear altitude-driven clustering. While primary nutritional components like dietary fiber, protein, and soluble solids exhibited stability across different altitudes, low-altitude samples showed higher levels of hypericin, quercetin, and rutin, likely due to favorable light and temperature conditions. Conversely, high-altitude samples were enriched in calcium, reflecting adaptations to cold stress and structural needs, while phosphorus content was reduced under cooler conditions. Potassium, iron, zinc, selenium, and strontium levels remained stable, indicating robust metabolic regulation. These findings confirm the significant role of altitude in shaping hawthorn's bioactive and mineral profiles, providing essential guidance for altitude-specific cultivation practices and tailored processing strategies. By leveraging these insights, the functional and nutritional properties of hawthorn can be optimized, supporting its sustainable application in the food and health industries.

Profiling Exosomal Metabolomics as a Means for Diagnosis and Researching Early-Stage Hypertensive Nephropathy.

Aims/Background Hypertension (HT) is a prevalent medical condition showing an increasing incidence rate in various populations over recent years. Long-term hypertension increases the risk of the occurrence of hypertensive nephropathy (HTN), which is also a health-threatening disorder. Given that very little is known about the pathogenesis of HTN, this study was designed to identify disease biomarkers, which enable early diagnosis of the disease, through the utilization of high-throughput untargeted metabolomics strategies. Methods The participants of this study were patients admitted to The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, who were randomly divided into three groups: Normal group (n = 11), HT group (n = 10), and HTN group (n = 12). Urine exosomes were extracted, purified, and subjected to untargeted metabolomics analysis. Differential metabolites and their significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified. The least absolute shrinkage and selection operator (LASSO) regression analysis was then employed to establish a diagnostic model for early-stage HTN. Finally, logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify biomarkers related to early HTN. Results Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed significant differences in the metabolic profiles of the three patient groups. Compared to subjects of the Normal group, the HT and HTN groups exhibited significantly upregulated and downregulated profiles of differential metabolites, respectively. LASSO regression analysis results indicated that 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid are potential biomarkers for HTN or HT. Conclusion With untargeted metabolomics analysis, we successfully identified differential metabolites in HTN. A further LASSO regression analysis revealed that four key metabolites, namely 4-hydroxyphenylacetic acid, bilirubin, uracil, and iminodiacetic acid, hold promise for the diagnosis of early-stage HTN.

Plasma metabolome reveals altered oxidative stress, inflammation, and amino acid metabolism in dogs with idiopathic epilepsy.

Idiopathic epilepsy (IE) is the most common chronic neurological disease in dogs and an established natural animal model for human epilepsy types with genetic and unknown etiology. However, the metabolic pathways underlying IE remain largely unknown. Plasma samples of healthy dogs (n = 39) and dogs with IE (n = 49) were metabolically profiled (n = 121 known target metabolites) and fingerprinted (n = 1825 untargeted features) using liquid chromatography coupled to mass spectrometry. Dogs with IE were classified as mild phenotype (MP; n = 22) or drug-resistant (DR; n = 27). All dogs received the same standard adult maintenance diet for a minimum of 20 days (35 ± 11 days) before sampling. Data were analyzed using a combination of univariate (one-way analysis of variance or Kruskal-Wallis rank sum test), multivariate (limma, orthogonal partial least squares-discriminant analysis), and pathway enrichment statistical analysis. In dogs with both DR and MP IE, a distinct plasma metabolic profile and fingerprint compared to healthy dogs was observed. Metabolic pathways involved in these alterations included oxidative stress, inflammation, and amino acid metabolism. Moreover, significantly lower plasma concentrations of vitamin B6 were found in MP (p = .001) and DR (p = .005) compared to healthy dogs. Our data provide new insights into the metabolic pathways underlying IE in dogs, further substantiating its potential as a natural animal model for humans with epilepsy, reflected by related metabolic changes in oxidative stress metabolites and vitamin B6. Even more, several metabolites within the uncovered pathways offer promising therapeutic targets for the management of IE, primarily for dogs, and ultimately for humans.

Ultra-high performance liquid chromatography ion mobility-high-resolution mass spectrometry for the assessment of raw milk traceability.

The complexity of modern food supply chains limits the effectiveness of targeted approaches to address food traceability issues. Untargeted metabolomics provides a comprehensive profile of small molecules present within biological samples. In this study, the potential of ultra-high performance liquid chromatography-ion mobility-high resolution mass spectrometry (UHPLC-IMS-HRMS) to discriminate bovine milk samples collected at individual level was evaluated for traceability purposes. For the first time, IMS coupled with UHPLC-HRMS was applied to milk analysis, increasing confidence in metabolite annotation. Supervised Partial Least Squares-Discriminant Analysis coupled to backward elimination variable selection allowed the selection of 52 and 153 features able to discriminate samples belonging to different dairy supply chains and trace samples at herd level, respectively. Amino acids, glycerolipids, and glycerophospholipids were the most represented classes, influencing the biological/technological properties of the final product. The perfect classification of samples belonging to external test sets demonstrated the reliability of the proposed approach.

Identification and Classification of Multi‐Species Biofilms on Polymeric Surfaces Using Hyperspectral Imaging

ABSTRACTBiofilm‐associated contamination poses significant challenges to the food industry, particularly in ensuring effective sanitization and reliable detection. This study explores the use of hyperspectral imaging (HSI) in the shortwave infrared (SWIR) range for non‐destructive detection and classification of biofilms on thermoplastic polyurethane (TPU) surfaces. Multi‐species biofilms composed of Comamonas sp., Raoultella sp., and Escherichia coli were formed at 10°C and 25°C and biofilm protein and polysaccharide contents were determined. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS‐DA) were used to differentiate among four classes of TPU coupons, including blank (BLANK), control (CTRL), intermediate‐strength biofilms formed at 10°C (S10), and strong biofilms formed at 25°C (S25). PCA successfully clustered samples based on spectral profiles of the classes, identifying significant wavelength regions at 1451 and 1926 nm, which correlated with the water, protein, and polysaccharide content of multi‐species biofilms. PLS‐DA provided a classification accuracy ranging from 68% to 100%, with the highest classification accuracy (100%) observed for BLANK and biofilm‐contaminated (S25) TPU coupons and the lowest accuracy (68%) for CTR. Additionally, Partial Least Squares Regression (PLSR) was employed to predict the protein content of biofilms, achieving reliable predictions both in calibration ( of 0.81) and external validation ( of 0.72). These findings demonstrate the potential of HSI to detect and classify biofilm‐infected TPU coupons utilizing wavebands associated with proteins, polysaccharides and water. Hence, HSI can be used as a rapid and non‐destructive alternative to traditional methods for biofilm detection, including chemical‐based methods such as BioDetect (SANI MARC) and fluorescence‐based imaging methods like BACTISCAN.

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.

Discrimination of Healthy and Botrytis cinerea-Infected Grapes Using Untargeted Metabolomic Analysis with Direct Electrospray Ionization Mass Spectrometry.

Botrytis cinerea infections of grapes significantly reduce yield and quality and increase phenolic compound oxidation, resulting in color loss, off-flavors, and odors in wine. In this study, metabolites were extracted from grape homogenates comprising healthy or infected grapes from different vintages, cultivars, regions, and maturity stages. Samples were randomly analyzed by direct injection into an ion trap mass spectrometer, with data collected from 50 to 2000 m/z for 1 min. Molecular feature abundances from 0.1 to 0.4 min were normalized prior to Principal Components Analysis assessment of workflow. Samples were randomly assigned to a calibration and independent test sample set, with feature reduction, a two-class model Partial Least Squares-Discriminant Analysis, cross-validation, and permutation testing performed with the calibration data set. Prediction of sample class in the independent test samples demonstrated an overall predictive error of less than 5%. Feature importance was assessed using a combined variable importance in projection and selectivity ratio plot. Annotation of important molecular features using a high-resolution LC-QTOF mass spectrometry MS/MS of selected samples enabled key metabolites palmitic, oleic, linoleic and linolenic acids, succinate, and epicatechin to be identified and associated with infection. The proposed workflow establishes sensitive high-throughput rapid MS-based methods for phytosanitary testing of grape and fruit samples.

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).

Salivary Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy Combined with Chemometric Analysis: A Potential Point-of-Care Approach for Chronic Kidney Disease Screening.

The increasing prevalence of chronic kidney disease (CKD) and its terminal stage, end-stage renal disease (ESRD), raises the importance of an accurate, early, and point-of-care method to diagnose and monitor patients. Saliva is a potential point-of-care diagnostic biofluid for its simple collection and ability to reflect systemic health status. This study investigated salivary spectral signatures in ESRD patients and their diagnostic potential compared to healthy controls. Saliva samples were collected from 24 ESRD patients undergoing hemodialysis and 24 age/sex-matched healthy controls. The dried saliva samples were analyzed using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy in the 4000-400 cm⁻¹ range. Chemometric analyses, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), were applied to preprocessed spectra to identify discriminatory spectral features and establish classification models. Second derivative spectroscopic analysis of ATR-FTIR spectra revealed distinctive spectral patterns in dried ESRD saliva samples, including characteristic peak shifts observed in both the amide I secondary structures (from 1636 cm-1 in controls to 1629 cm-1 in ESRD) and carbohydrate (from 1037 cm-1 in controls to 1042 cm-1 in ESRD) regions. PCA demonstrated clear clustering patterns across key biological spectral regions, including the lipid CH stretching region (3000-2800 cm-1), the fingerprint region (1800-900 cm-1), and their combination (3000-2800 cm-1 + 1800-900 cm-1). PLS models based on the fingerprint region achieved optimal diagnostic performance (87.5-100% accuracy, 75-100% sensitivity, and 100% specificity). Biochemical markers associated with ESRD revealed variations in lipids, protein, sugar moieties, carbohydrates, and nucleic acids, reflecting the underlying pathological changes in CKD, with the most prominent band at ∼1405 cm-1. ATR-FTIR analysis of dried saliva demonstrated potential as a non-invasive diagnostic tool for ESRD. This approach could complement existing diagnostic methods, particularly in resource-limited settings or for frequent monitoring requirements.

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.