Partial Least Squares Discriminant Analysis Research Articles

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PurposeThe duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.MethodsA total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi’an JiaoTong University were selected as the discovery set. One-to-one case–control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography–mass spectrometry (LC–MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.ResultsThe discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.ConclusionDisturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by near-Infrared Spectroscopy with Chemometrics.

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions. This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions. The models were produced under different conditions to distinguish the origins distinctly. Ten pre-processing methods have been used to pre-process the original spectra (OS) and to select the most optimal spectral pre-processing method. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables. The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate. The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua. The utilization of near-infrared spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating quality assurance measures.

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

Sex and disease severity-based analysis of steroid hormones in ME/CFS.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease characterized by persistent fatigue and decreased daily activity following physical and/or cognitive exertion. While ME/CFS affects both sexes, there is a higher prevalence in women. However, studies evaluating this sex-related bias are limited. Circulating steroid hormones, including mineralocorticoids (aldosterone), glucocorticoids (cortisol, corticosterone, 11-deoxycortisol, cortisone), androgens (androstenedione, testosterone), and progestins (progesterone, 17α-hydroxyprogesterone), were measured in plasma samples using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Samples were obtained from mild/moderate (ME/CFSmm; females, n=20; males, n=8), severely affected patients (ME/CFSsa; females, n=24; males, n=6), and healthy controls (HC, females, n=12; males, n=17). After correction for multiple testing, we observed that circulating levels of 11-deoxycortisol, 17α-hydroxyprogesterone in females, and progesterone in males were significantly different between HC, ME/CFSmm, and ME/CFSsa. Comparing two independent groups, we found that female ME/CFSsa had higher levels of 11-deoxycortisol (vs. HC and ME/CFSmm) and 17α-hydroxyprogesterone (vs. HC). In addition, female ME/CFSmm showed a significant increase in progesterone levels compared to HC. In contrast, our study found that male ME/CFSmm had lower circulating levels of cortisol and corticosterone, while progesterone levels were elevated compared to HC. In addition to these univariate analyses, our correlational and multivariate approaches identified differential associations between our study groups. Also, using two-component partial least squares discriminant analysis (PLS-DA), we were able to discriminate ME/CFS from HC with an accuracy of 0.712 and 0.846 for females and males, respectively. Our findings suggest the potential value of including steroid hormones in future studies aimed at improving stratification in ME/CFS. Additionally, our results provide new perspectives to explore the clinical relevance of these differences within specific patient subgroups.

Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging

Botrytis cinerea is a strawberry disease that causes economic loss worldwide. If a disease outbreak occurs during storage or transportation, it can spread rapidly to neighboring objects; thus, there is a need to develop early diagnostic techniques to prevent it. In this study, we developed a method to rapidly and nondestructively determine the infection stage in strawberry fruit using hyperspectral fluorescence imaging. ‘Keumsil’ cultivar strawberries were used, and hyperspectral fluorescence images were acquired over 144 h in control and inoculation groups. Strawberries were categorized into four infection stages based on visible mold spores: healthy, asymptomatic, infected, and after-infected. Hyperspectral fluorescence spectra were extracted to develop a one-dimensional convolutional neural network (1D-CNN) model based on partial least squares-discriminant analysis (PLS-DA), VGG-19, and ResNet-50; data augmentation techniques and six spectral preprocessing techniques were applied to the datasets. The application of data augmentation techniques improved the performances of the PLS-DA and 1D-CNN models in determining the infection stage. The performance of the ResNet-50-based 1D-CNN model with mean normalization data and data augmentation technique was the best, with 96.88% precision, 96.87% recall, 96.85% F1-score, and 96.86% accuracy. The results of this study showed that it is possible to determine the infection stage of Botrytis cinerea on strawberry fruit using hyperspectral fluorescence imaging and 1D-CNN techniques. This technology is expected to be applied for the early detection of Botrytis cinerea in strawberry growth, postharvest sorting and packing, and distribution stages.

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

Serum Levels of Adiponectin Are Strongly Associated with Lipoprotein Subclasses in Healthy Volunteers but Not in Patients with Metabolic Syndrome.

Metabolic syndrome (MS) is a widespread disease in developed countries, accompanied, among others, by decreased adiponectin serum levels and perturbed lipoprotein metabolism. The associations between the serum levels of adiponectin and lipoproteins have been extensively studied in the past under healthy conditions, yet it remains unexplored whether the observed associations also exist in patients with MS. Therefore, in the present study, we analyzed the serum levels of lipoprotein subclasses using nuclear magnetic resonance spectroscopy and examined their associations with the serum levels of adiponectin in patients with MS in comparison with healthy volunteers (HVs). In the HVs, the serum levels of adiponectin were significantly negatively correlated with the serum levels of large buoyant-, very-low-density lipoprotein, and intermediate-density lipoprotein, as well as small dense low-density lipoprotein (LDL) and significantly positively correlated with large buoyant high-density lipoprotein (HDL). In patients with MS, however, adiponectin was only significantly correlated with the serum levels of phospholipids in total HDL and large buoyant LDL. As revealed through logistic regression and orthogonal partial least-squares discriminant analyses, high adiponectin serum levels were associated with low levels of small dense LDL and high levels of large buoyant HDL in the HVs as well as high levels of large buoyant LDL and total HDL in patients with MS. We conclude that the presence of MS weakens or abolishes the strong associations between adiponectin and the lipoprotein parameters observed in HVs and disturbs the complex interplay between adiponectin and lipoprotein metabolism.

Metabolomics reveals high fructose-1,6-bisphosphate from fluoride-resistant Streptococcus mutans

BackgroundFluoride-resistant Streptococcus mutans (S. mutans) strains have developed due to the wide use of fluoride in dental caries prevention. However, the metabolomics of fluoride-resistant S. mutans remains unclear.ObjectiveThis study aimed to identify metabolites that discriminate fluoride-resistant from wild-type S. mutans.Materials and methodsCell supernatants from fluoride-resistant and wild-type S. mutans were collected and analyzed by liquid chromatography-mass spectrometry. Principal components analysis and partial least-squares discriminant analysis were performed for the statistical analysis by variable influence on projection (VIP > 2.0) and p value (Mann–Whitney test, p < 0.05). Metabolites were assessed qualitatively using the Human Metabolome Database version 2.0 (http://www.hmdb.ca), or Kyoto Encyclopedia of Genes and Genomes (http://www.kegg.jp), and Metaboanalyst 6.0 (https://www.metaboanalyst.ca).ResultsFourteen metabolites differed significantly between fluoride-resistant and wild-type strains in the early log phase. Among these metabolites, 5 were identified. There were 32 differential metabolites between the two strains in the stationary phase, 13 of which were identified. The pyrimidine metabolism for S. mutans FR was matched with the metabolic pathway.ConclusionsThe fructose-1,6-bisphosphate concentration increased in fluoride-resistant strains under acidic conditions, suggesting enhanced acidogenicity and acid tolerance. This metabolite may be a promising target for elucidating the cariogenic and fluoride resistant mechanisms of S. mutans.

Abstract PO5-13-03: Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling

Abstract Background: Breast cancer (BC) is the second leading cause of cancer death among women. Accordingly, early diagnosis is key to the successful treatment, management, and care of BC. Recent studies confirmed that plasma metabolites could be reliable cancer biomarkers, allowing for the development of a minimally invasive routine blood test that can be used for screening, as well as for monitoring disease evolution in patients. The exact metabolic pathways involved in early BC development remain unclear. Metabolomic profiling of women with BC may help to identify new biomarkers to predict breast cancer long before symptoms appear. The purpose of this study was to validate a plasma metabolomic biomarker panel for an improved risk assessment for early detection BC in 241 patients, and to understand the potential role and the relationship between BC subtypes and hormone receptor status. Methods: Our study included a total of 185 plasma samples from women with biopsy-confirmed BC and 56 plasma samples from healthy controls. A targeted, quantitative mass spectrometry (MS)-based metabolomics approach was used to analyze 138 metabolites in plasma samples using a combination of direct injection (DI) MS and reverse-phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and hormones receptor status were used to determine optimal biomarker sets. The same biomarkers and regression models were used and assessed on the validation models. The area under the receiver operator characteristic curves (AUROC), sensitivities and specificities at selected cut off points were calculated for each subgroup. Results: A large proportion of BC patients were at an early stage, with 98 at stage I (53.0 %), 70 at stage II (37.8%), and 17 at stage III (9.2%). The BC patients were of all subtypes: 138 luminal A (76,2%), 23 luminal B (12.7%), 5 hormone receptor-negative/HER2-positive (2.8%), and 15 triples negative (8.3%). A feature selection was performed on the training set using Partial Least-Squares Discriminant Analysis (PLS-DA), and the top performing metabolites were identified as the most important in discriminating BC from healthy subjects. The impact of age was also investigated. Features with &amp;gt;80% missing values were removed. To predict BC, the best signature comprised 9 variables implicated in fatty acid metabolism, amino acid metabolism, polyamine biosynthesis and related signaling pathways We further developed and validated a logistic regression with AUROC &amp;gt; 0.9 using these metabolites and other clinical data for detecting different stages and subtypes of BC. Conclusion: This study identified and validated a simple, high-performing, metabolite-based test for the early detection of BC. After confirmation in other independent cohort studies, our findings could provide the foundations for the development of a blood-based routine test for women at the highest risk for BC that is cost-effective, accurate and reliable. In addition, this approach could be used to complement other modalities especially for BC patients with dense breasts. Citation Format: Jean-Francois Haince, Lun Zhang, Rashid Ahmed Bux, Paramjit S. Tappia, Bram Ramjiawan, David Wishart, Andrew Maksymiuk. Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-03.

Detection and diagnosis of bacterial pathogens in urine using laser-induced breakdown spectroscopy

The presence of bacterial cells from three species has been detected in clinical specimens of human urine using laser-induced breakdown spectroscopy (LIBS) by using a partial least squares discriminant analysis (PLS-DA) of 360 spectra obtained from 12 specimens of infected urine and 239 spectra obtained from eight specimens of sterile urine. Nominally sterile urine specimens obtained from four patients at a local hospital after being screened negative for the presence of bacterial pathogens were spiked with known aliquots of Escherichia coli, Staphylococcus aureus, and Enterobacter cloacae to simulate clinical urinary tract infections. Fifteen emission line intensities measured from the LIBS spectra and 92 ratios of those line intensities were used as 107 independent variables in the PLS-DA for discrimination between bacteria-containing specimens and sterile specimens. The PLS-DA models possessed a 98.3% sensitivity and a 97.9% specificity for the detection of pathogenic cells in urine when single-shot LIBS spectra were tested. To increase the signal to noise ratio, thirty spectra acquired from a single specimen were also averaged together and the averaged spectra were used to construct a model. When each averaged spectrum was withheld from the model individually for testing, the diagnostic test possessed a 100% sensitivity and a 100% specificity for the detection of bacterial cells in urine, although the number of test spectra was necessarily reduced.The entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected. This PCA-ANN test possessed an overall sensitivity of 97.2%, an overall specificity of 98.6%, and an overall classification accuracy of 97.9% when using 80% of the data to build a model and withholding 20% for cross-validation testing. The PCA-ANN was also performed on each of the 12 bacteria-containing filters individually, using the other 11 filters to build the model. The average sensitivity of this test, calculated by averaging the sensitivities measured for each of the three bacterial species, was 70.9% and the average specificity was 85.5%. Based on these results, the average classification accuracy for the test when used to discriminate between the three microorganisms was 80.6%. These results indicate the potential usefulness of LIBS for rapidly detecting and possibly diagnosing urinary tract infections in a clinical setting.

Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5−year−old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400–2,500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

Benchtop NMR Coupled with Chemometrics: A Workflow for Unveiling Hidden Drug Ingredients in Honey-Based Supplements.

Recently, benchtop nuclear magnetic resonance (NMR) spectrometers utilizing permanent magnets have emerged as versatile tools with applications across various fields, including food and pharmaceuticals. Their efficacy is further enhanced when coupled with chemometric methods. This study presents an innovative approach to leveraging a compact benchtop NMR spectrometer coupled with chemometrics for screening honey-based food supplements adulterated with active pharmaceutical ingredients. Initially, fifty samples seized by French customs were analyzed using a 60 MHz benchtop spectrometer. The investigation unveiled the presence of tadalafil in 37 samples, sildenafil in 5 samples, and a combination of flibanserin with tadalafil in 1 sample. After conducting comprehensive qualitative and quantitative characterization of the samples, we propose a chemometric workflow to provide an efficient screening of honey samples using the NMR dataset. This pipeline, utilizing partial least squares discriminant analysis (PLS-DA) models, enables the classification of samples as either adulterated or non-adulterated, as well as the identification of the presence of tadalafil or sildenafil. Additionally, PLS regression models are employed to predict the quantitative content of these adulterants. Through blind analysis, this workflow allows for the detection and quantification of adulterants in these honey supplements.

The Management of Irrigation and Potassium Fertilization to Mitigate the Effect of Light Frosts on the Phenolic and Volatile Compounds in Virgin Olive Oils

The frequency of early frosts has increased in recent years, which are injurious to olive growing, causing losses in the yield and quality of virgin olive oil. In this research, it was studied how the management of agronomic factors mitigates frost damage in Arbequina olives, minimizing the loss of phenols and volatiles in virgin olive oil, at different fruit ripening stages. A Box–Behnken design and multivariate analysis were performed, with three levels of irrigation, potassium fertilization, and foliar copper application (15 treatments). Virgin olive oil was extracted from fresh and frozen olives. Light frost caused a significant decrease in the total phenols and secoiridoid compounds in and the antioxidant capacity of the frost-affected oils, which were perceived as more pungent and had the slight defect of “frostbitten olives”. According to the Box–Behnken design, an 86% reference evapotranspiration (ET0) or higher with 100 potassium oxide units (UK2O) and a 100% ET0 or higher with 250 UK2O would be required to minimize the effect of light frost on phenols and volatiles. Partial Least Squares Regression–Discriminant Analysis (PLS-DA) differentiated the virgin olive oils according to their ripening stage and fresh and frost conditions. Moreover, PLS-DA positively correlated a 75–100% ET0 and 0 Uk2O with the dialdehydic form of the decarboxymethyl ligstroside aglycone (p-HPEA-EDA), the dialdehydic form of the decarboxymethyl oleuropein aglycone (3,4-DHPEA-EDA), the dialdehydic form of the ligstroside aglycone (p-HPEA-EDA-DLA), and with fruity, pungent, and bitter attributes. Precision agronomic management based on the needs of the crop itself would avoid unnecessary stress on olive trees and oil damage.

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8%, 74.6% and 79.8%, respectively; but, the relative root mean square error of prediction was as high as 51.0%. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3%, 90.5% and 91.9%, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

PLS Discriminant Analysis for Classification of Caries Level

PLS Discriminant Analysis for Classification of Caries Level

Chemical composition and discrimination with volatile profiles of Yongchuan Xiuya green tea with different baking treatments

AbstractThis study aimed to explore the effects of different baking treatments on the quality of Yongchuan Xiuya (YCXY) green tea. It was found that a long time baking at low temperature was beneficial to keep green colour, faint scent, mellow and fresh taste quality, whereas baking under high temperature was adverse to the quality. Baking treatments slightly regulated (−)‐epigallocatechin, (−)‐epicatechin, (−)‐gallocatechin gallate, total chlorophyll and carotenoid. As the baking temperature rose, the content of tea polyphenols, gallic acid, catechins, caffeine, soluble sugar and polyphenols/amino acids ratio obviously decreased. A total of 40 kinds of volatiles were detected in these six tea samples. A model for aroma type discrimination was successfully established based on partial least squares‐discriminant analysis (PLS‐DA). Ten kinds of volatile substances were the key characteristic markers for the faint scent, chestnut aroma and high‐fired aroma types of YCXY green tea. 1‐hexanol, hotrienol and dimethyl sulfide were furtherly used to discriminant aroma type based on Fisher discriminant analysis (FDA).

Microbial community structure and carbon transformation characteristics of different aggregates in black soil.

Previous research on whole-soil measurements has failed to explain the spatial distribution of soil carbon transformations, which is essential for a precise understanding of the microorganisms responsible for carbon transformations. The microorganisms involved in the transformation of soil carbon were investigated at the microscopic scale by combining 16S rDNA sequencing technology with particle-level soil classification. In this experiment,16S rDNA sequencing analysis was used to evaluate the variations in the microbial community structure of different aggregates in no-tillage black soil. The prokaryotic microorganisms involved in carbon transformation were measured before and after the freezing and thawing of various aggregates in no-tillage black soil. Each sample was divided into six categories based on aggregate grain size: >5, 2-5, 1-2, 0.5-1, 0.25-0.5, <0.25 mm, and bulk soil. The relative abundance of Actinobacteria phylum in <0.25 mm aggregates was significantly higher compared to that in other aggregates. The Chao1 index, Shannon index, and phylogenetic diversity (PD) whole tree index of <0.25 mm aggregates were significantly smaller than those of in bulk soil and >5 mm aggregates. Orthogonal partial least-squares discrimination analysis showed that the microbial community composition of black soil aggregates was significantly different between <1 and >1 mm. The redundancy analysis (RDA) showed that the organic carbon conversion rate of 0.25-0.5 mm agglomerates had a significantly greater effect on their bacterial community structure. Moreover, humic acid conversion rates on aggregates <0.5 mm had a greater impact on community structure. The linear discriminant analysis effect size (LEfSe) analysis and RDA analysis were combined. Bradyrhizobium, Actinoplane, Streptomyces, Dactylosporangium, Yonghaparkia, Fleivirga, and Xiangella in <0.25 mm aggregates were positively correlated with soil organic carbon conversion rates. Blastococcus and Pseudarthrobacter were positively correlated with soil organic carbon conversion rates in 0.25-0.5 mm aggregates. In aggregates smaller than 1 mm, the higher the abundance of functional bacteria that contributed to the soil's ability to fix carbon and nitrogen. There were large differences in prokaryotic microbial community composition between <1 and >1 mm aggregates. The <1 mm aggregates play an important role in soil carbon transformation and carbon fixation. The 0.25-0.5 mm aggregates had the fastest organic carbon conversion rate and increased significantly more than the other aggregates. Some genus or species of Actinobacteria and Proteobacteria play a positive role in the carbon transformation of <1 mm aggregates. Such analyses may help to identify microbial partners that play an important role in carbon transformation at the micro scale of no-till black soils.

Target Cell Extraction and Spectrum-Effect Relationship Coupled with BP Neural Network Classification for Screening Potential Bioactive Components in Ginseng Extract with a Protective Effect against Myocardial Damage.

Cardiovascular disease has become a common ailment that endangers human health, having garnered widespread attention due to its high prevalence, recurrence rate, and sudden death risk. Ginseng possesses functions such as invigorating vital energy, enhancing vein recovery, promoting body fluid and blood nourishment, calming the nerves, and improving cognitive function. It is widely utilized in the treatment of various heart conditions, including palpitations, chest pain, heart failure, and other ailments. Although numerous research reports have investigated the cardiovascular activity of single ginsenoside, there remains a lack of systematic research on the specific components group that predominantly contribute to cardiovascular efficacy in ginseng medicinal materials. In this research, the spectrum-effect relationship, target cell extraction, and BP neural network classification were used to establish a rapid screening system for potential active substances. The results show that red ginseng extract (RGE) can improve the decrease in cell viability and ATP content and inhibit the increase in ROS production and LDH release in OGD-induced H9c2 cells. A total of 70 ginsenosides were identified in RGE using HPLC-Q-TOF-MS/MS analysis. Chromatographic fingerprints were established for 12 batches of RGE by high-performance liquid chromatography (HPLC). A total of 36 common ingredients were found in 12 batches of RGE. The cell viability, ATP, ROS, and LDH of 12 batches RGE were tested to establish gray relationship analysis (GRA) and partial least squares discrimination analysis (PLS-DA). BP neural network classification and target cell extraction were used to narrow down the scope of Spectral efficiency analysis and screen the potential active components. According to the cell experiments, RGE can improve the cell viability and ATP content and reduce the oxidative damage. Then, seven active ingredients, namely, Ginsenoside Rg1, Rg2, Rg3, Rb1, Rd, Re, and Ro, were screened out, and their cardiovascular activity was confirmed in the OGD model. The seven ginsenosides were the main active substances of red ginseng in treating myocardial injury. This study offers a reference for quality control in red ginseng and preparations containing red ginseng for the management of cardiovascular diseases. It also provides ideas for screening active ingredients of the same type of multi-pharmacologically active traditional Chinese medicines.

Plant Growth-Promoting Bacteria Influence Microbial Community Composition and Metabolic Function to Enhance the Efficiency of Hybrid pennisetum Remediation in Cadmium-Contaminated Soil

The green and efficient remediation of soil cadmium (Cd) is an urgent task, and plant-microbial joint remediation has become a research hotspot due to its advantages. High-throughput sequencing and metabolomics have technical advantages in analyzing the microbiological mechanism of plant growth-promoting bacteria in improving phytoremediation of soil heavy metal pollution. In this experiment, a pot trial was conducted to investigate the effects of inoculating the plant growth-promoting bacterium Enterobacter sp. VY on the growth and Cd remediation efficiency of the energy plant Hybrid pennisetum. The test strain VY-1 was analyzed using high-throughput sequencing and metabolomics to assess its effects on microbial community composition and metabolic function. The results demonstrated that Enterobacter sp. VY-1 effectively mitigated Cd stress on Hybrid pennisetum, resulting in increased plant biomass, Cd accumulation, and translocation factor, thereby enhancing phytoremediation efficiency. Analysis of soil physical-chemical properties revealed that strain VY-1 could increase soil total nitrogen, total phosphorus, available phosphorus, and available potassium content. Principal coordinate analysis (PCoA) indicated that strain VY-1 significantly influenced bacterial community composition, with Proteobacteria, Firmicutes, Chloroflexi, among others, being the main differential taxa. Redundancy analysis (RDA) revealed that available phosphorus, available potassium, and pH were the primary factors affecting bacterial communities. Partial Least Squares Discriminant Analysis (PLS-DA) demonstrated that strain VY-1 modulated the metabolite profile of Hybrid pennisetum rhizosphere soil, with 27 differential metabolites showing significant differences, including 19 up-regulated and eight down-regulated expressions. These differentially expressed metabolites were primarily involved in metabolism and environmental information processing, encompassing pathways such as glutamine and glutamate metabolism, α-linolenic acid metabolism, pyrimidine metabolism, and purine metabolism. This study utilized 16S rRNA high-throughput sequencing and metabolomics technology to investigate the impact of the plant growth-promoting bacterium Enterobacter sp. VY-1 on the growth and Cd enrichment of Hybrid pennisetum, providing insights into the regulatory role of plant growth-promoting bacteria in microbial community structure and metabolic function, thereby improving the microbiological mechanisms of phytoremediation.