Partial Least Squares Discriminant Analysis Research Articles

Can chick meconium serve as a source of biomarkers linked to hatchling quality and the age of parent stock?

One possible approach to selecting chicks based on quality involves identifying biomarkers in biological samples. Concurrently, understanding the metabolic profile of chicks from different-aged breeders is essential for developing strategies to mitigate the age-related effects on hatchability. This study investigated whether chick quality and breeder age influence the metabolic profile of layer chick meconium. A total of 143 chicks from laying breeder hens, categorized as young, middle-aged or old, were visually assessed for quality, and meconium samples were collected for metabolomic analysis. Sixteen metabolites were found to be associated with good-quality chicks across all breeder ages, with an overall accuracy of 81.94 %. Using metabolite profiles, the accuracy for predicting young-hen-chick quality was 93.2 %, with high sensitivity (92.30 %) and specificity (93.75 %). Four metabolites were associated with poor-quality chicks with an overall accuracy of 77.53 %. Partial least squares discriminant analysis (PLS-DA) revealed enhanced metabolite separation in good-quality chicks, with five metabolites exhibiting high area under the curve (AUC) values (>90 %) in chicks from young hens compared to chicks from hens of other ages, including metabolites related to energy metabolism, hormonal activity, vitamin D synthesis and peptide constitution. Disregarding maternal age, five metabolites varied between good- and poor-quality chicks, but with a low accuracy of 61.26 % for quality discrimination. Chicks from young, middle-aged, and old hens exhibited 12, 11, and 2 metabolites that varied the expression between good and poor qualities, with accuracies for predicting good-quality chicks of 74.46 %, 70.83 %, and 51.06 %, respectively. Certain metabolites with 70 < AUC < 80 % have emerged as potential biomarkers for distinguishing between good- and poor-quality layer chicks. These include metabolites related to energy and growth metabolism, tryptophan and methionine metabolism, antioxidants and some with no known function in embryos. This work identified potential metabolites that can be investigated to mitigate the effects of hen age on hatchability. Additionally, several metabolites have emerged as potential biomarkers for distinguishing between good- and poor-quality chicks, depending on the breeder's age.

Analysis of volatile compounds and vintage discrimination of raw Pu-erh tea based on GC-IMS and GC-MS combined with data fusion.

Storage duration significantly influences the aroma profile of raw Pu-erh tea. To comprehensively investigate the differences in the volatile compounds across various vintages of raw Pu-erh teas and achieve the rapid classification of tea vintages, volatile compounds of raw Pu-erh tea with different years (2020-2023) were analyzed using a combination of gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-mass spectrometry (GC-MS). The datasets obtained from both techniques were integrated through low-level and mid-level data fusion strategies. Additionally, partial least squares discriminant analysis (PLS-DA) and random forest (RF) machine learning algorithms were applied to develop predictive models for the classification of tea storage durations. Consequently, GC-IMS and GC-MS identified 54 and 76 volatile compounds, respectively. Notably, the RF model, particularly when coupled with mid-level data fusion, exhibited exceptional predictive accuracy for tea storage time, reaching an accuracy of 100%. These findings provide a reference for elucidating the aroma characteristics of raw Pu-erh tea of different vintages and demonstrate that data fusion combined with machine learning has great potential for ensuring food quality.

Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (RecallM and AccuracyM equal to 1.00), followed by PCA-KNN (RecallM of 0.98–1.00 and AccuracyM equal to 1.00). Poorer performances were obtained by PLS-DA (RecallM of 0.68–0.72 and AccuracyM equal to 0.95) and PCA-DA (RecallM of 0.66–0.70 and AccuracyM equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies.

Feasibility study on fingerprinting organic and conventional mango fruits, chips, and juice using portable near-infrared spectroscopy.

This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms. The spectral data underwent pre-processing through methodologies such as multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives to enhance the precision of the models. Principal component analysis (PCA) and various multivariate classification algorithms, including linear discriminant analysis (LDA), random forest (RF), k-nearest neighbors (kNN), and partial least squares discriminant analysis (PLSDA), were utilised to categorise the samples effectively. The findings indicated that the random forest method and specific pre-processing techniques achieved the highest classification accuracy for distinguishing organic and conventional mango products. For mango fruit and chips, it achieved 88.76% and 77.98% accuracy, respectively, when pre-processed using the second derivative, while for juice, it achieved 87.53% accuracy without pre-processing. This investigation demonstrates the efficacy of portable NIR spectroscopy as a dependable and non-invasive method for verifying organic mango products, thereby enhancing the integrity of food labelling and fostering consumer confidence.

Analyzing the effect of different storage times and different processes on the volatile components in Shenling Baizhu powder by HS-GC-IMS combined with multivariate statistics.

this study aimed to examine the effect of different storage times (0, 7, 24, 57, and 119 days) on the volatile components of Shenling Baizhu powder across different preparation processes (Pharmacopoeia, ultra-micro pulverization-pulverization, and microparticle design methods). The findings offer insights to guide quality control measures for Shenling Baizhu powder. gas chromatography-ion mobility spectrometry (GC-IMS) was employed to ascertain the volatile components in Shenling Baizhu powder at various storage times across different preparation processes. In addition, the study compared the dynamic changes of these components under different conditions. Principal component analysis (PCA) and orthogonal partial least-squares discrimination analysis (OPLS-DA) were conducted using SIMCA-P19.0 software to analyze differences among these volatile components. the volatile components identified by GC-IMS comprised 52 monomers and dimers and polymers of some substances, primarily including aldehydes, esters, terpenes, nitrogenous compounds, and ketones. Fingerprint spectra revealed significant differences in the types and quantities of volatile components in Shenling Baizhu powder at different storage times under different processes. PCA indicated negligible differences in the types of volatile components among samples prepared using different processes. An OPLS-DA model was developed to distinguish storage times and identify characteristic markers. In addition, 22 volatile components with variable importance for the projection (VIP) > 1 were identified, significantly contributing to the discrimination of storage times for Shenling Baizhu powder. Overall, the content of volatile components in the samples gradually decreased with prolonged storage time. Samples prepared using the Pharmacopoeia method exhibited the most significant decline, whereas those prepared using the microparticle design method showed the least reduction in volatile components. HS-GC-IMS effectively captures the changing trends of volatile components in Shenling Baizhu powder across different preparation processes and storage times. This analysis provides a reference for quality control and process optimization in the production of Shenling Baizhu powder.

DOP119 Predicting response to anti-tumour necrosis factor-alpha therapy-α in fibrostenotic Crohn’s disease using infra-red microspectroscopy

Abstract Background Fibrostenotic strictures are common in Crohn’s disease, have limited treatment options and lack validated biomarkers to predict treatment response. Fourier-transform infra-red (FTIR) spectroscopy provides reproducible biochemical information from biospecimens using a rapid, label-free, non-destructive technique via molecular vibrations. We previously completed a proof-of-concept study that identified spectral biomarkers for inflammation in a model of colitis.1 The aim of this study was to investigate whether the tissue biochemical "fingerprint" using FTIR spectroscopy can predict patient clinical response to anti-tumour necrosis-factor-α (anti-TNFα) therapy, and identify spectral biomarkers for treatment response. Methods Hyperspectral images were acquired using the Agilent Cary 670 FPA-IR coupled with the Agilent Cary 620 microscope in transflection mode in the wavenumber region 1800 – 800 cm-1 from fixed ileo-colonoscopic biopsies from 62 patients with Crohn’s disease and 12 healthy controls. A subset of patients (n = 24) were included from STRIDENT2, a randomised study assessing adalimumab therapy for intestinal Crohn’s strictures. Thousands of spectra were obtained per sample, pre-processed via transformation to the second derivative, normalised and reduced to 25 spectra per sample prior to analysis using partial least squares discriminant analysis (PLSDA) with internal cross-validation. Spectral biomarkers were identified by the most contributive infra-red bands from the latent variables (LV) and significant variable importance in projection (VIP) scores of &amp;gt;1. Adjacent histological sections were stained with H&amp;E, Masson’s trichrome and α-smooth muscle actin with inflammation and fibrosis scored by a clinical pathologist. Results Spectra from baseline biopsies from the 24 patients in the STRIDENT study demonstrated excellent performance using PLSDA in discriminating between responders and non-responders in relation to the 12 month clinical outcomes, defined by area under the receiver operating characteristic curve (AUC) &amp;gt;0.9 for MRI and IUS responses, normalisation of faecal calprotectin and CRP, CDAI and pain responses (Table 1). Spectral biomarkers of interest for stricture response to anti-TNFα are shown in Figure 1 and include the amide I and II protein bands (peaks at 1651 and 1543 cm-1, respectively), the carboxylate group of proteins (1454 cm-1), lipid bands (1750 and 1395 cm-1), collagen band (1236 cm-1), glycoprotein bands (1081 and 1030 cm-1), and nucleic acid bands (1236, 1081 and 966 cm-1). Conclusion The novel technique of FTIR spectroscopy detects tissue biochemical "fingerprints" in fibrostenotic Crohn’s disease that demonstrate excellent discrimination for predicting clinical response to adalimumab therapy.

P0089 Infra-red microspectroscopy provides quantitative assessment of fibro-inflammation in Crohn’s disease strictures and predicts post-operative recurrence

Abstract Background Quantitative assessment of fibro-inflammation in Crohn’s disease strictures remains a challenge despite its role in clinical decision making using immunomodulatory drugs or endoscopic/surgical interventions. While histology remains the gold standard, no validated histological scoring systems for Crohn’s disease strictures exist and assessment is subject to significant intra- and inter-observer variability. Fourier-transform infra-red (FTIR) microspectroscopy is a reproducible, label-free and non-destructive approach for analysing tissue biochemistry via molecular vibrations. We have previously shown accurate assessment of inflammation severity using this technique in colitis models (1). The aim of this study was to assess Crohn’s disease strictures using FTIR spectroscopy and to evaluate if tissue spectra can predict post-operative Crohn’s disease recurrence. Methods Full-thickness Crohn’s stricture resections (n=72) were obtained in paraffin and sectioned for hyperspectral imaging using the Agilent Cary 670 FPA-IR coupled with the Agilent Cary 620 microscope in transflection mode in the wavenumber region 1800–800 cm-1. Nearly 8 million spectra in total were acquired from regions of interest in each of the bowel wall layers per sample (Figure 1). Spectra was transformed to the second derivative, normalised and reduced to 25 spectra per sample prior to analysis using partial least squares discriminant analysis (PLSDA) with internal cross-validation. Adjacent tissue sections were stained with H&amp;E, Masson’s trichrome and α-smooth muscle actin. Histology was scored based on the averaged scores of three independent clinical histopathologists for inflammation, fibrosis and muscular changes. Results Tissue infra-red spectra demonstrated excellent performance defined by area under the receiver operating characteristic curve (AUC) of ≥0.9 using PLSDA to classify severe from mild inflammation and good (AUC ≥0.8) to excellent (AUC ≥0.9) performance to classify severe from mild fibrosis and muscular hyperplasia/hypertrophy. In those with complete macroscopic Crohn’s disease resection (n=51), spectra from index stricture resections demonstrated good performance (AUC ≥0.8) in discriminating between future significant endoscopic recurrence defined by Rutgeert’s score ≥ i2 (2) and excellent performance for surgical recurrence (AUC ≥0.9) defined as future surgical resection of Crohn’s recurrence. Conclusion FTIR microspectroscopy can detect biochemical differences underlying inflammation, fibrosis and muscular changes in Crohn’s strictures and provide simultaneous quantitative assessment. The biochemical tissue fingerprints demonstrate good to excellent discrimination for predicting endoscopic and surgical Crohn’s recurrence, respectively.

The Dynamic Changes in Volatile Compounds During Wuyi Rock Tea (WRT) Processing: More than a Contribution to Aroma Quality

Wuyi Rock tea (WRT), originating from the northern region of Fujian province, has a good reputation for its distinctive Yan flavor and floral–fruity aroma. The aroma quality, an essential element of the Yan flavor, undergoes various changes during the manufacturing process of WRT. To enhance the understanding of the formation patterns of WRT aroma and its influence on the flavor quality of WRT, we utilized both manufactured WRT (Rougui tea) and primary tea as materials. Utilizing a sensory evaluation, detection of volatile compounds, and multivariate statistical analysis, we identified and characterized the distinctive volatile components present in WRT. The sensory evaluation and radar chart analysis revealed that the primary tea exhibited a strong and lasting aroma, along with a mellow taste and a prominent Yan flavor. Through gas chromatography time-of-flight mass spectrometry (GC-TOF MS), a total of 251 volatile compounds were identified. The odor activity value (OAV) was calculated to identify key aroma-active compounds in the primary tea. The results indicated that a total of 14 compounds had an OAV greater than 1.0, including (2-nitroethyl) benzene, indole, and geranylacetone. These compounds exhibited floral and fruity aroma attributes. They primarily formed and accumulated during the latter stages of WRT. Using a partial least squares discrimination analysis (PLS-DA) combined with a variable importance in projection (VIP) score greater than 1.0 as a criterion, a total of 89 compounds were identified. Furthermore, out of the selected compounds, 15 types, including geraniol, 1-nonanol, and 1-butyl-2-ethyl-cyclopropene, were found to exclusively exist during the enzymatic manufacturing stages, particularly during the intermediate and later phases of the turn-over process (the last-three-times turn-over treatments), exhibiting predominantly floral and sweet fragrances. In contrast, during the non-enzymatic stages, only four compounds, such as pentanoic acid and phenylmethyl ester, were detected, exhibiting a fruity aroma profile. These volatile compounds significantly influenced the quality attributes of the final tea product, resulting in strong and lasting characteristics, particularly marked by a pronounced floral and fruity aroma. This study revealed how the aroma quality in WRT is developed and pinpointed possible volatile compounds that react to post-harvest treatments, thereby offering valuable insights relating to the intelligent production strategies of WRT.

Rapid identification of the volatile markers in lotus seed using headspace-gas chromatography ion-mobility spectrometry

To rapidly identify the volatile markers in lotus seeds, this research compared the volatile organic compounds (VOCs) of five lotus seed samples using gas chromatography-ion mobility spectroscopy (GC-IMS) and chemometric analysis. The results revealed that 49 VOCs were identified from the lotus seed samples, including 16 aldehydes, 15 alcohols, 8 ketones, 3 esters, 3 acids, 3 terpenes, and 1 heterocyclic compound, respectively. Among these, 1-pentanol M, 2-methylbutan-1-ol M, 2-methylbutan-1-ol D, and 1-hexanol M were identified as the volatile markers. Based on the VOC analysis using GC-IMS, effective differentiation of lotus seeds was achieved through chemometric techniques, such as principal component analysis (PCA), cluster analysis (CA), and partial least squares discriminant analysis (PLS-DA). 2-furaldehyde D, hexanoic acid D, and 1-propanol M were found to be the most important components contributing to the differences among the five lotus seed samples. This research demonstrates that GC-IMS coupled with chemometric analysis provides valuable reference information for the identification and authenticity evaluation of lotus seeds, helping ensure their quality on the market and offering theoretical support for their identification and quality assessment.

Examination of SWIR hyperspectral imaging for estimating corewood – outerwood transition age for Douglas-fir grown at different planting densities

Abstract Silvicultural practices, particularly planting density (PD), significantly affect the wood quality of plantation-grown trees by influencing the age of transition from corewood to outerwood. Determining transition age (TA) is challenging. Near infrared hyperspectral imaging was used to assess the age of transition from corewood to outerwood in Douglas-fir (Pseudotsuga menziesii) grown under different PD. Wood samples were taken from permanent research plots with six PD ranging from 2,990 to 248 stems/hectare. Twenty trees were destructively sampled at age 25, and wood discs were collected at 10 % of total tree height. Pith-to-bark radial strips obtained from each disc were scanned using a compovision hyperspectral imaging system (1,002–2,350 nm). Partial least square discriminant analysis (PLS-DA) models were fitted to approximate TA using hyperspectral data representative of corewood and outerwood. PD influenced TA and the duration over which it occurred. Average age of transition was estimated to vary from 10 (closest spacing) to 14 years (widest). Closely spaced trees exhibited an abrupt transition, while widely spaced trees had a more gradual transition. However, PLS-DA likely underestimated the transition duration for widely spaced trees, as their average ring densities did not yet match those typical of outerwood based on X-ray densitometry data.

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.

Characterization of flavor profiles of water-boiled pork meatballs at different ultrasonic powers using solid-phase microextraction gas chromatography-mass spectrometry combined with electronic nose.

The objective of this study was to evaluate the flavor profiles of water-boiled pork meatballs at different ultrasonic powers (0, 150, 300, 450, 600, and 750W) using solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) combined with electronic nose (E-nose). A total of 36 volatile compounds were determined by SPME-GC-MS, including alcohols, aromatic hydrocarbons, aldehydes, terpenes, alkanes, phenols, ketones, and other. With the appropriate ultrasound treatment, the type and relative content of volatile compounds were significantly increased (P<0.05), and the ultrasound treatment at 450W had the better effect on the flavor characteristics of pork meatballs. E-nose analysis also showed that the 450W ultrasound treatment had the highest response values of W1S, W6S, and W2S sensors (sensitive to hydrocarbons, alcohols, aldehydes, and ketones) to volatile compounds of pork meatballs. The characteristic flavor compounds of pork meatballs, including m-cresol, limonene, nonanal, and linalool, were further confirmed by comparing the content, partial least squares discriminant analysis (PLS-DA), and odor activity value (OAV). Overall, appropriate ultrasound-assisted cooking (especially at 450W) could be a promising approach to enhance the flavor profiles of pork meatballs.

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.

Serum metabolic fingerprinting on Ag@AuNWs for traumatic brain injury diagnosis.

Accurate and rapid diagnosis of traumatic brain injury (TBI) is essential for high-quality medical services. Nonetheless, the current diagnostic platform still has challenges in rapidly and accurately analysing clinical samples. Here, we prepared a highly stable, repeatable and sensitive gold-plated silver core-shell nanowire (Ag@AuNWs) for surface-enhanced Raman spectroscopy (SERS) metabolic fingerprint diagnosis of TBI. The core-shell structure significantly enhanced SERS intensity and enables the direct detection of 10 μL serum within seconds. The principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squaresdiscriminant analysis (PLS-DA) are used to evaluate the classification effect of this technology on TBI, respectively. The diagnosis accuracy rate of PCA-LDA and PLS-DA is 73.3% and 86.7% for diagnosing TBI, respectively. Consequently, the PLS-DA model is the optimal selection for distinguishing between the TBI and sham groups. This research will facilitate the application-oriented creation of novel materials with tailored structural designs and the formulation of innovative precision medical protocols in the imminent future.&#xD.

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.

Year-to-year differentiation of black tea through spectroscopic and chemometric analysis

Abstract The compositions of food products such as tea can vary significantly from one harvest year to another, primarily due to factors such as shifting climatic conditions, and plant periodicity. These fluctuations in composition can significantly affect the overall product quality. Spectral methods combined with chemometric techniques can provide efficient tools to monitor and assess these variations. In this study, 205 black tea samples from two consecutive harvest years were analyzed using mid-infrared, UV–visible, and fluorescence spectroscopy. Mid-infrared spectra were collected for both infused and powdered samples, while only the infused samples were used for the other spectroscopic methods. The study used partial least-square discriminant (PLS-DA) and orthogonal partial least-square discriminant analyses (OPLS-DA) to differentiate samples by harvest year. These models, applied after various data transformations, achieved high correct classification rates. Mid-infrared spectroscopic data yielded rates of 93.33% and 90.33% for powdered and infused samples, respectively. Fluorescence and UV–visible spectra also showed excellent prediction accuracy, with success rates of 98.3% and 100%. The results indicate that these spectroscopic methods, combined with chemometric differentiation, are valuable tools for monitoring year-to-year changes in black tea.

A Study of Volatile Organic Compounds in Patients with Obstructive Sleep Apnea.

Background: Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by intermittent upper airway obstruction, leading to significant health consequences. Traditional diagnostic methods, such as polysomnography, are time-consuming and resource-intensive. Objectives: This study explores the potential of proton-transfer-reaction mass spectrometry (PTR-MS) in identifying volatile organic compound (VOC) biomarkers for the non-invasive detection of OSA. Methods: Breath samples from 89 participants, including 49 OSA patients and 40 controls, were analyzed using PTR-MS. Significance analysis was performed between OSA patients and controls to identify potential biomarkers for OSA. To as-sess the differences in VOC concentrations between OSA patients and control subjects, the Wilcoxon rank-sum test was employed. partial least squares discriminant analysis (PLS-DA) analysis and heatmap plot was conducted to visualize the differentiation between OSA patients and control subjects based on their VOC profiles.In order to further investigate the correlation between identified biomarkers and the severity of OSA measured by Apnea-Hypopnea Index (AHI), regression analysis was conducted between biomarkers and AHI Index. Results: The results identified specific VOCs, including m045 (acetaldehyde), m095.950, and m097.071, which showed significant differences between OSA patients and controls. Advanced statistical analyses, including PLS-DA and correlation mapping, highlighted the robustness of these biomarkers, with m045 (acetaldehyde) specifically emerging as a potential biomarker associated with the AHI Index. Conclusions: This study underscores the potential of VOCs as biomarkers for identifying patients with severe AHI levels. The analysis of VOCs using PTR-MS presents a rapid, non-invasive, and cost-effective method that could be seamlessly integrated into clinical practice, allowing clinicians to better stratify patients based on their need for polysomnography and prioritize those requiring earlier testing. Future studies are necessary to validate these findings in larger cohorts and to explore the integration of PTR-MS with other diagnostic modalities for improved accuracy and clinical utility.

Multimodal Metabolomics Analysis Reveals That Classic Decoction Mitigates Myocardial Ischemia-Induced Damage by Modulating Energy and Branched-Chain Amino Acid Metabolism.

Gualou-Xiebai-Banxia (GXB) decoction shows potential for treating myocardial ischemia (MI), although its underlying mechanism is not fully understood. In this study, a multimodal metabolomics approach, combining gas chromatography-mass spectrometry (GC-MS) and 1H-NMR, was employed to investigate the cardioprotective effects of GXB in a rat model of myocardial ischemia induced by ligation. ELISA assays and HE staining demonstrated that GXB effectively reduced myocardial injury, oxidative stress markers, and myocardial fibrosis. Orthogonal partial least-squares discriminant analysis identified 62 biomarkers, 20 of which were confirmed using standard compounds. The GC-MS method showed excellent linearity across a wide concentration range (0.004-29.7 μg/mL, R2 > 0.9995), with intra- and inter-day precision RSD values below 4.72% and 4.96%, respectively. Method recoveries ranged from 95.40% to 104.83%, with RSD values under 4.84%. Pathway enrichment analysis revealed that GXB decoction alleviates myocardial ischemia-induced damage primarily by modulating energy metabolism and branched-chain amino acid metabolism. These findings provide valuable support for the clinical application of GXB decoction in treating myocardial ischemia.

Evaluation of Peri-Implantitis through Fourier-Transform Infrared Spectroscopy on Saliva.

Peri-implantitis is characterized as a pathological change in the tissues around dental implants. Fourier-transform infrared spectroscopy (FTIR) provides molecular information from optical phenomena observed by the vibration of molecules, which is used in biological studies to characterize changes and serves as a form of diagnosis. this case-control study evaluated the peri-implant disease by using FTIR spectroscopy with attenuated total reflectance in the fingerprint region. 38 saliva samples were evaluated, 17 from the control group and 21 from the peri-implantitis group. Clinical data such as plaque index (PI), gingival index, probing depth (PS), and attachment level were assessed. The results of clinical parameters showed a statistical difference between the two groups regarding an excess of the PI. In the FTIR-ATR analysis, the main components revealed vibrational modes of fatty acids, histidine, lipid esters, nucleic acids, and tryptophan, with the main molecules contributing to spectral discrimination. The five-component partial least-squares discriminant analysis classification model had an accuracy of 81%, showing differences between healthy and diseased implants. the FTIR spectroscopy provides important molecular characteristics of the samples and the results in association with clinical data show the effectiveness of using this tool for diagnosing the disease.

Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds.

Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from 90.00% to 98.33%, with the NN model demonstrating the best performance. Specifically, it achieved 96.67% accuracy for single-maturity classification, 98.33% for single-variety classification, and 96.67% for dual maturity and variety classification, along with 98.09% precision, 97.92% recall, and 97.91% F1 score. Feature importance analysis indicated that the NN model exhibited the most balanced reliance on various VOCs, contributing to its optimal performance with the broad-spectrum VOC detection method, GC-IMS. Overall, these findings underscore the potential of NN modeling for accurate and efficient fruit classification based on integrated VOC profiles.