Partial Least Squares Discriminant Analysis Research Articles

Comprehensive assessment of fragrance profiles in peony flowers through HS-GC-IMS and metabolic pathway analysis

This study addresses the identification and comparison of volatile organic compounds (VOCs) in different peony varieties to elucidate their distinct aroma profiles and the common VOCs that constitute the unique fragrance of peony flowers, laying a foundation for breeding high-quality peony varieties and developing related products. Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) was employed to analyze and differentiate the VOCs of eight peony varieties: TAOXI, ZIHELIAN, BAITIANE, YAOHUANG, DAFUGUI, DAOJIN, HONGXIUQIU, and MANAOPAN. The study identified 87 VOCs, revealing significant differences in VOC compositions among the samples. Notably, BAITIANE displayed distinct differences in both color and VOC composition compared to the other seven varieties. It exhibited uniquely high levels of butanoic acid-M, butanoic acid-D, hexanal-D, ethanol-D, and ethyl acetate. Twelve key differential VOCs were identified as having significant impacts: ethanol-D, hexanal-D, (e)-2-hexenal-D, (e)-2-hexenal-M, hexanal-M, butanoic acid-M, acetic acid-D, acetic acid-M, butanoic acid-D, ethyl acetate, thiophene, and dimethyl sulfide. KEGG pathway analysis highlighted the roles of xenobiotics biodegradation, terpenoid metabolism, and amino acid metabolism in VOC biosynthesis. The findings are significant for the breeding of fragrant peony varieties and the development of floral-based products. The findings provide theoretical support for breeding new horticultural flower varieties and developing commercial peony products, enhancing the understanding of floral scent chemistry, and offering valuable information for the perfumery, floral food processing, and related industries.

Integrated Chemometrics and Data-Independent Acquisition Proteomics for the Discovery of Meat Authenticity Biomarkers: A Study on Early Post-Mortem Pectoralis major Muscle Proteomes of Ross 308 and Ranger Classic Chicken Produced by Organic versus Antibiotic-Free Farming Systems.

Many factors, such as the farming systems and preslaughter rearing practices, can influence the physiological and metabolic functions of poultry with consequent effects on poultry meat quality. In this trial, label-free shotgun proteomics was used to analyze the early post-mortem Pectoralis major muscle proteomes of Ross 308 and Ranger Classic chicken strains raised under two divergent farming systems these being organic and antibiotic-free. The combination of chemometrics using partial-least-square discriminant analysis (PLS-DA) and shotgun proteomics allowed clear discrimination between the different groups. Chicken strains were discriminated by differences in the abundance of 73 and 62 proteins within the antibiotic-free and organic farming systems, respectively. The abundances of 71 and 52 proteins were impacted by the farming system within the Ross 308 and Ranger Classic chicken strains, respectively. The analyses allowed for the proposal of several putative biomarkers of meat authenticity, which were found to be related to muscle structure and energy metabolism pathways. This study is a significant step forward in elucidating the potential of proteomics profiling and chemometrics in chicken meat, which may provide opportunities for the efficient assessment of chicken authenticity.

Comparative chemometric modeling of fresh and dry cannabis inflorescences using FT-NIR spectroscopy: Quantification and classification insights.

Cannabis sativa L. inflorescences are rich in cannabinoids and terpenes. Traditional chemical analysis methods for cannabinoids and terpenes, such as liquid and gas chromatography (using UV or MS detectors), are expensive and time-consuming. This study explores the use of Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometric approaches for classifying cannabis chemovars and predicting cannabinoid and terpene concentrations for the first time in freshly harvested (wet) cannabis inflorescence. The study also compares the performance of FT-NIR spectroscopy on wet versus dry cannabis inflorescences. Spectral data from 187 samples across seven cannabis chemovars were analyzed using partial least squares-discriminant analysis (PLS-DA) and partial least squares-regression (PLS-R) models. The PLS-DA models effectively classified chemovars and major classes using only two latent variables (LVs) with minimal overfitting risk, with sensitivity, specificity, and accuracy values approaching 1. Despite the high water content in wet cannabis inflorescence, the PLS-R models demonstrated good to excellent predictive capabilities for nine cannabinoids and eight terpenes using FT-NIR spectra for the first time, achieving cross-validation and prediction R-squared values greater than 0.7, ratio of performance to interquartile range (RPIQ) exceeding 2, and a RMSECV/RMSEC ratio below 1.24. However, the low-cannabidiolic acid submodel and (-)-Δ9-trans-tetrahydrocannabinol model showed poor predictive performance. Some cannabinoid and terpene prediction models in wet cannabis inflorescence exhibited lower predictive capabilities compared with previously published models for dry cannabis inflorescence. These findings suggest that FT-NIR spectroscopy can be a viable rapid on-site analytical tool for growers during the inflorescence flowering stage.

Classification of papyrus fragments by chemometric analysis of near-infrared spectra

Papyrus has been used for millennia to record information, for sophisticated works of art as well as mundane notes. The collection, identification, and translation of papyrus fragments therefore opens a gateway into the past. To aid the efforts to access the history recorded in papyri, we investigated the suitability of NIR spectroscopy to perform two tasks: One is to support the authentication of ancient papyri, by differentiation of papyri that were manufactured more recently and subjected to accelerated ageing to resemble the originals. The other is the extensive task to piece together papyrus fragments into readable texts again. In museums around the world, more than 100,000 ancient papyrus fragments still wait for their proper assembly, deciphering and publication. The papyrus writing-ground was analysed by near-infrared (NIR) spectroscopy, and the spectra were evaluated using principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares discriminant analysis (PLS-DA), and self-organizing maps (SOM). Cluster analysis and PLS-DA proved to be useful tools for distinguishing modern papyri from ancient papyri which were provided by collections in Vienna and Leipzig. Neither natural nor accelerated ageing affected the classification. A PLS-DA classification model, constructed from NIR spectra of 89 model scores, detected recent Papyri samples with 100 % sensitivity and specificity, even after accelerated ageing. The identification of groups of fragments of ancient papyri based on NIR spectra and chemometry is not straightforward. HCA, which focuses on the differences between samples, only grouped the fragments of 4 out of 20 papyri correctly. SOM, which rather focuses on the similarities, grouped 6 sets of fragments correctly. An automated grouping of fragments remains difficult, since the fragments themselves are heterogeneous while similarities between unrelated ancient papyri can be large.

Metabolomic and Lipidomic Analysis of Manganese-Associated Parkinsonism: a Case-Control Study in Brescia, Italy.

Excessive Manganese (Mn) exposure is neurotoxic and can cause Mn-Induced Parkinsonism (MnIP), marked by cognitive and motor dysfunction. Although metabolomic and lipidomic research in Parkinsonism (PD) patients exists, it remains limited. This study hypothesizes distinct metabolomic and lipidomic profiles based on exposure status, disease diagnosis, and their interaction. We used a case-control design with a 2×2 factorial framework to investigate the metabolomic and lipidomic alterations associated with Mn exposure and their link to PD. The study population of 97 individuals was divided into four groups: non-exposed controls (n=23), exposed controls (n=25), non-exposed with PD (n=26) and exposed with PD (n=23). Cases, defined by at least two cardinal PD features (excluding vascular, iatrogenic, and traumatic origins), were recruited from movement disorder clinics in four hospitals in Brescia, Northern Italy. Controls, free from neurological or psychiatric conditions, were selected from the same hospitals. Exposed subjects resided in metallurgic regions (Val Camonica and Bagnolo Mella) for at least 8 continuous years, while non-exposed subjects lived in low-exposure areas around Lake Garda and Brescia city. We conducted untargeted analyses of metabolites and lipids in whole blood samples using ultra-high-performance liquid chromatography (UHPLC) and mass spectrometry (MS), followed by statistical analyses including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Two-Way Analysis of Covariance (ANCOVA). Metabolomic analysis revealed modulation of alanine, aspartate, and glutamate metabolism (Impact=0.05, p=0.001) associated with disease effect; butanoate metabolism (Impact=0.03, p=0.004) with the exposure effect; and vitamin B6 metabolism (Impact=0.08, p=0.03) with the interaction effect. Differential relative abundances in 3-sulfoxy-L-Tyrosine (β=1.12, FDR p<0.001), glycocholic acid (β=0.48, FDR p=0.03), and palmitelaidic acid (β=0.30, FDR p<0.001) were linked to disease, exposure, and interaction effects, respectively. In the lipidome, ferroptosis (Pathway Lipids=11, FDR p=0.03) associated with the disease effect and sphingolipid signaling (Pathway Lipids=9, FDR p=0.04) associated with the interaction effect were significantly altered. Lipid classes triacylglycerols, ceramides, and phosphatidylethanolamines showed differential relative abundances associated with disease, exposure, and interaction effects, respectively. These findings suggest that PD and Mn exposure induce unique metabolomic and lipidomic changes, potentially serving as biomarkers for MnIP and warranting further study.

Overlooked Trace Molecules in Organic Aerosol Revealed by Gas Chromatography-Orbitrap Mass Spectrometry.

Molecular characterization of organic aerosol (OA) is crucial for understanding its sources and atmospheric processes. However, the chemical components of OA remain not well constrained. This study used gas chromatography-Orbitrap mass spectrometry (GC-Orbitrap MS) and GC-Quadrupole MS (GC-qMS) to investigate the organic composition in PM2.5 from Xi'an, Northwest China. GC-Orbitrap MS identified 335 organic tracers, including overlooked isomers and low-concentration molecules, approximately 1.6 times more than GC-qMS. The "molecular corridor" assessment shows the superior capability of GC-Orbitrap MS in identifying an expansive range of compounds with higher volatility and oxidation states, such as furanoses/pyranoses, di/hydroxy/ketonic acids, di/poly alcohols, aldehydes/ketones, and amines/amides. Seasonal variations in OA composition reflect diverse sources: increased di/poly alcohols in winter are derived from indoor emissions, furanoses/pyranoses and heterocyclics in spring and summer might be from biogenic emissions and secondary formation, and amides in autumn are probably from biomass burning. Integrating partial least squares discriminant analysis (PLS-DA) and potential source contribution function (PSCF) models, the source similarities and differences are further elucidated, highlighting the role of local emissions and transport from southern cities. This study offers new insights into the OA composition aided by the high mass resolution and sensitivity of GC-Orbitrap MS.

Longitudinal characterization of the muscle metabolome in dairy cows during the transition from lactation cessation to lactation resumption

Skeletal muscle is vital in maintaining metabolic homeostasis and adapting to the physiological needs of pregnancy and lactation. Despite advancements in understanding metabolic changes in dairy cows around calving and early lactation, there are still gaps in our knowledge, especially concerning muscle metabolism and the changes associated with drying off. This study aimed to characterize the skeletal muscle metabolome in the context of the dietary and metabolic changes occurring during the transition from the cessation of lactation to the resumption of lactation in dairy cows. Twelve Holstein dairy cows housed in tie stalls were dried off 6 weeks (wk) before the expected calving date. Cows were individually fed ad libitum total mixed rations composed of grass silage, corn silage, and concentrate during lactation and of corn silage, barley straw, and concentrate during the dry period. The metabolome was characterized in skeletal muscle samples (M. longissimus dorsi) collected on wk -7 (9 d before dry-off), -5 (6 d after dry-off), and wk -1, and 1 relative to calving. The targeted metabolomics approach was conducted using the MxP Quant 500 kit (Biocrates Life Sciences AG) with liquid chromatography, flow injection, and electrospray ionization triple quadrupole mass spectrometry. Statistical analysis on the muscle metabolite data was performed using MetaboAnalyst 5.0, which allowed us to conduct various multivariate analyses such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), informative heat map generation, and hierarchical clustering. The statistical analysis revealed a clear separation between pregnancy (wk -7, -5, and -1) and post-calving (wk 1). Starting 5 wk before calving and continuing through the first wk thereafter, the concentration of 3-methylhistidine (3-MH) in the muscle increased. This coincided with an increase in the concentrations of 11 AA (Phe, His, Tyr, Trp, Arg, Asn, Leu, Ile, Gly, Ser, and Thr) in the first wk after calving, whereas Gln decreased. l-arginine pathway metabolites (homoarginine, ornithine, citrulline, and asymmetric dimethylarginine), betaine, and sarcosine followed a similar pattern, increasing from wk -7 to -5, but decreasing from wk -1 to 1. The transition from pregnancy to lactation was associated with an increase in concentrations of the long-chain acylcarnitine species C16, C16:1, C18, and C18:1 in the muscle, whereas the concentrations of phosphatidylcholine and sphingomyelin in the muscle remained stable. The significant changes observed in the metabolome mainly concerned the AA and AA-related metabolites, indicating muscle protein breakdown in the first wk after calving. The metabolites produced by the L-Arg pathway might contribute to regulating skeletal muscle mass and function in periparturient dairy cows. The elevated concentrations of long-chain acylcarnitine species in the muscle in the first wk after calving suggest incomplete fatty acid oxidation, likely due to insufficient metabolic adaptation in response to the fatty acid load around the time of calving.

NMR-based metabolomics analysis reveals the effect of environmental contamination exposure on fishermen living around the Mundaú Lagoon in Maceió (Alagoas, Brazil)

The Mundaú lagoon in Maceió (Alagoas, Brazil) is a crucial resource for the local population, particularly fishing communities. Recent studies have revealed potential toxic metal contamination in the lagoon, particularly with mercury (Hg) levels exceeding the maximum regulated values. This inorganic contaminant may be impacting the health of fishermen and the local population. In this context, metabolomics, a study of small-molecule metabolites, can offer insights into the physiological impact of environmental contamination on humans. Thus, volunteers from the control and exposed groups were selected, considering the main exposure criteria primarily defined by their proximity and interaction with the lagoon. Blood and urine samples were collected from the volunteers and subjected to analysis using NMR spectroscopy. The data underwent Principal Component Analysis (PCA) and Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) based on metabolic patterns to establish group discrimination or identification. Metabolic pathways were assessed through enrichment analysis. The study revealed several metabolic disturbances in the exposed group's urine and plasma samples compared to control group. Noteworthy findings included arginine and proline metabolism disruptions, indicative of ammonia recycling and urea cycle impairment. These changes suggest compromised ammonia detoxification in the exposed group. Disturbances in the tricarboxylic acid (TCA) cycle and the transfer of acetyl groups into mitochondria suggested systemic metabolic stress in energy metabolism. Furthermore, elevated carnitine and ketone levels may indicate compensatory responses to low TCA cycle activity. Alterations in glutamate and glutathione metabolism and imbalances in glutathione levels indicate oxidative stress and impaired detoxification. This study highlights significant metabolic changes in fishermen exposed to contaminated environments, which can affect various metabolic pathways, including energy metabolism and antioxidant processes, potentially making individuals more vulnerable to the adverse effects of environmental contaminants. Finally, this work highlights insights into the relationship between environmental contamination and metabolic pathways, particularly in regions with limited studies.

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C–C–C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 milliliters of deionized water to prepare a single salt solution (0.02% - 1.00%) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86% and 99.66%, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at herd-level using mid-infrared spectral information from routine milk control

As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification.

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

Rapid Assessment of Metabolomic Fingerprinting of Recycled Sunflower By-Products via DART-HRMS.

To comply with a more circular and environmentally friendly European common agricultural policy, while also valorising sunflower by-products, an ultrasound assisted extraction (UAE) was tested to optimise ethanol-wash solutes (EWS). Furthermore, the capabilities of DART-HRMS as a rapid and cost-effective tool for determining the biochemical changes after valorisation of these defatted sunflower EWS were investigated. Three batches of EWS were doubly processed into optimised EWS (OEWS) samples, which were analysed via DART-HRMS. Then, the metabolic profiles were submitted to a univariate analysis followed by a partial least square discriminant analysis (PLS-DA) allowing the identification of the 15 most informative ions. The assessment of the metabolomic fingerprinting characterising EWS and OEWS resulted in an accurate and well-defined spatial clusterization based on the retrieved pool of informative ions. The outcomes highlighted a significantly higher relative abundance of phenolipid hydroxycinnamoyl-glyceric acid and a lower incidence of free fatty acids and diglycerides due to the ultrasound treatment. These resulting biochemical changes might turn OEWS into a natural antioxidant supplement useful for controlling lipid oxidation and to prolong the shelf-life of foods and feeds. A standardised processing leading to a selective concentration of the desirable bioactive compounds is also advisable.

Pilot Study on the Use of Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy for Diagnosing and Characterizing Cardiac Amyloidosis.

Amyloidosis diagnosis relies on Congo red staining with immunohistochemistry and immunofluorescence for subtyping but lacks sensitivity and specificity. Laser-microdissection mass spectroscopy offers better accuracy but is complex and requires extensive sample preparation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy offers a promising alternative for amyloidosis characterization. Cardiac tissue sections from nine patients with amyloidosis and 20 heart transplant recipients were analyzed using ATR-FTIR spectroscopy. Partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) models were used to differentiate healthy post-transplant cardiac tissue from amyloidosis samples and identify amyloidosis subtypes [κ light chain (n = 1), λ light chain (n = 3), and transthyretin (n = 5)]. Leave-one-out cross-validation (LOOCV) was employed to assess the performance of the PLS-DA model. Significant spectral differences were found in the 1700-1500 cm-1 and 1300-1200 cm-1 regions, primarily related to proteins. The PLS-DA model explained 85.8% of the variance, showing clear clustering between groups. PCA in the 1712-1711 cm-1, 1666-1646 cm-1, and 1385-1383 cm-1 regions also identified two clear clusters. The PCA and the HCA model in the 1646-1642 cm-1 region distinguished κ light chain, λ light chain, and transthyretin cases. This pilot study suggests ATR-FTIR spectroscopy as a novel, non-destructive, rapid, and inexpensive tool for diagnosing and subtyping amyloidosis. This study was limited by a small dataset and variability in measurements across different instruments and laboratories. The PLS-DA model's performance may suffer from overfitting and class imbalance. Larger, more diverse datasets are needed for validation.

Combination of Near-Infrared Spectroscopy and Multisource Data Fusion for the Geographical Origin Authentication of Panax notoginseng

Panax notoginseng (Burk.) F.H. Chen (P. notoginseng), a precious medicinal herb, provides both considerable values. There is confusion in the market regarding the geographical origins of P. notoginseng. It is of paramount importance to authenticate geographical origins of P. notoginseng, which will facilitate the rational utilization of resources and sustainable development. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the influence of the tissue and data fusion strategy for the authentication of P. notoginseng origins. The PLS-DA model employing the main root spectra demonstrated great potential for authenticating P. notoginseng origin. The PLS-DA training and test sets based on feature-level fusion (VIP) spectral data were 100% accurate, indicating excellent potential in authenticating P. notoginseng. The data fusion strategy improved the authentication. The article presents a reliable and efficacious novel methodology for authenticating P. notoginseng origins and a scientific foundation for its quality control.

A Pilot Study of Saliva MicroRNA Signatures in Children with Moderate-to-Severe Traumatic Brain Injury.

Background/Objectives: Traumatic brain injury (TBI) is a leading cause of death and disability in children. Currently, no biological test can predict outcomes in pediatric TBI, complicating medical management. This study sought to identify brain-related micro-ribosomal nucleic acids (miRNAs) in saliva associated with moderate-to-severe TBI in children, offering a potential non-invasive, prognostic tool. Methods: A case-control design was used, enrolling participants ≤ 18 years old from three pediatric trauma centers. Participants were divided into moderate-to-severe TBI and non-TBI trauma control groups. Saliva samples were collected within 24 h of injury, with additional samples at 24-48 h and >48 h post-injury from the TBI group. miRNA profiles were visualized with partial least squares discriminant analysis (PLSDA) and hierarchical clustering. Mann-Whitney testing was used to compare miRNAs between groups, and mixed models were used to assess longitudinal expression patterns. DIANA miRPath v3.0 was used to interrogate the physiological functions of miRNAs. Results: Twenty-three participants were enrolled (14 TBI, nine controls). TBI and control groups displayed complete separation of miRNA profiles on PLSDA. Three miRNAs were elevated (adj. p < 0.05) in TBI (miR-1255b-5p, miR-3142, and miR-4320), and two were lower (miR-326 and miR-4646-5p). Three miRNAs (miR-3907, miR-4254, and miR-1273g-5p) showed temporal changes post-injury. Brain-related targets of these miRNAs included the glutamatergic synapse and GRIN2B. Conclusions: This study shows that saliva miRNA profiles in children with moderate-to-severe TBI may differ from those with non-TBI trauma and exhibit temporal changes post-injury. These miRNAs could serve as non-invasive biomarkers for prognosticating pediatric TBI outcomes. Further studies are needed to confirm these findings.

Comparative transcriptomic analysis and volatile compound characterization of Aspergillus tubingensis and Penicillium oxalicum during their infestation of Japonica rice

Volatile organic compounds (VOCs), a byproduct of mold metabolism, have garnered increasing interest because the VOCs can be used to detect food early contamination. So far, the use of VOCs as indicators of rice mildew, specifically caused by Aspergillus tubingensis and Penicillium oxalicum, and the mechanisms of their generation are not well investigated. This study examines the VOCs produced by these molds during paddy storage, utilizing headspace solid-phase micro-extraction gas chromatography-mass spectrometry (HS-SPME-GC–MS). We further elucidate the mechanisms underlying the formation of these VOCs through a comparative transcriptomic analysis. The VOCs characteristic to A. tubingensis and P. oxalicum, identified with a VIP value > 1 in the partial least squares discriminant analysis (PLS-DA) model, are primarily alkenes. Our transcriptome analysis uncovers key metabolic pathways in both molds, including energy metabolism and pathways related to volatile substance formation, and identifies differentially expressed genes associated with alkane and alcohol formation.

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis

Seed viability is essential to have a homogeneous plant population. The seed industry cannot adopt traditional procedures for seed viability evaluation since they are destructive, time-consuming, and need chemicals. This study aimed to investigate the potential of combining hyperspectral and color image features to differentiate viable and non-viable paddy seeds. The hyperspectral and color image of the 355 paddy seeds was captured and later used to examine their viability. An image processing algorithm was developed to extract features from color images of paddy seeds and investigated significant differences in the retrieved feature data using variance analysis. The spectra were extracted from the selected region of interest (ROI) of the hyperspectral paddy seed image and averaged. In the next step, the partial least square discrimination analysis (PLS-DA) model was developed to distinguish viable and non-viable paddy seeds. Initially, the PLS-DA model was developed using spectral data with different preprocessing techniques, and the result obtained an accuracy of 88.9 % in the calibration set and 86.1 % in the prediction set using Savitzky-Golay 2nd derivative preprocessed spectra. With the fusion of spectral and significant color image features, the model's accuracy improved to 93.3 % and 90.9 % in the calibration and prediction sets, respectively. Results also showed that the fusion of selected color image features with Savitzky-Golay 2nd derivative preprocessed spectra could achieve higher F1-score, recall, and precision values. The visualization map for the viable and non-viable paddy seeds was also developed utilizing the most effective predictive model. The results demonstrate the possibility of using the fusion of the hyperspectral and color image features to sort seeds according to viability, which may be applied in developing an online seed sorting method.

Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability

Over the past decade, there has been overwhelming interest in rapid and routine origin tracing and authentication methods, such as near infrared (NIR) spectroscopy. In a systematic and comprehensive approach, this study coupled NIR with advanced machine learning models to explore the origin classification of coffee at various scales (continental to regional level). Speciality green coffee beans were sourced from three continents, eight countries, and 22 regions. The dispersive bulk NIR spectra were used for spectral registration in the reflectance mode, and the obtained spectra were preprocessed with extended multiplicative scatter correction and mean centering. The classical linear partial least squares-discriminant analysis adequately predicted origin at the continental and country level, and showed promise at the regional level. Non-linear machine learning models improved predictions further, with the best accuracy found using random forest with accuracies up to 0.99. Discriminating wavelength regions and constituents were identified at each origin scale, with more minor wavelength regions selected by random forest. This proof of concept work demonstrated the potential of NIR spectroscopy coupled with machine learning for rapid origin classification of coffee from the continental to the regional level.

Characteristics of Indonesian Stingless Bee Propolis and Study of Metabolomic Properties Based on Region and Species.

The chemical compounds found in propolis vary according to plant sources, species, and geographical regions. To date, Indonesian propolis has not yet become standardized in terms of its chemical constituents. Thus, this study aimed to identify the presence of marker compounds and determine whether different classes of Indonesian propolis exist. In this study, yields, total polyphenol content (TPC), total flavonoid content (TFC), and antioxidants were measured. Identification of chemical compounds was carried out with Fourier-transform infrared (FTIR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Metaboanalyst 6.0 was employed in conducting principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) using the results of the FTIR and LC-MS/MS. The propolis with the highest TFC, TPC, and antioxidant activity was Geniotrigona thoracica from North Sumatra. The results of propolis compound mapping based on region with discriminant analysis revealed that types of propolis from Java have similar characteristics. Then, based on species, the types of propolis from Tetragonula laeviceps and Heterotrigona itama have special characteristics; the samples from these species can be grouped according to similar characteristics. In conclusion, 10 potential marker compounds were identified in Indonesian propolis, enabling regional and species-specific varieties of Indonesian propolis to be classified based on chemical composition mapping.