Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy.

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Analysis of characteristic aromas of buckwheat with different germplasm using gas chromatography-mass spectrometry combined with chemometrics and multivariate statistical analysis.

In this study, the volatile components in 40 samples of Tartary buckwheat and common buckwheat from 6 major producing areas in China were analyzed. A total of 77 volatile substances were identified, among which aldehydes and hydrocarbons were the main volatile components. Odor activity value analysis revealed 26 aromatic compounds, with aldehydes making a significant contribution to the aroma of buckwheat. Seven key compounds that could be used to distinguish Tartary buckwheat from common buckwheat were identified. The orthogonal partial least squares-discriminant analysis was effectively used to classify Tartary buckwheat and common buckwheat from different producing areas. This study provides valuable information for evaluating buckwheat quality, breeding high-quality varieties, and enhancing rational resource development.

Characterization of Flavor Profile of Sauced Pork from Different Regions of China Based on E-Nose, E-Tongue and Gas Chromatography-Ion Mobility Spectroscopy.

This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography-ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork.

HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS.

To establish the fingerprint of Cibotii rhizoma using high-performance liquid chromatography (HPLC) and evaluate the quality of Cibotii rhizoma from different regions using chemometrics to identify the potential quality markers, thirteen batches of Cibotii rhizoma samples were analyzed. the similarity evaluation system of TCM chromatographic fingerprint similarity evaluation was used to confirm common peaks. The SPSS 27 software was used for hierarchical cluster analysis (HCA), and SIMCA 14.1 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, a batch of Cibotii rhizoma was selected for LC-MS analysis and speculated on 15 common components. HPLC fingerprint were established, 15 common peaks were matched, two chromatographic peaks were identified using standard substances (protocatechuic acid and protocatechuic aldehyde), and 13 common components were inferred through liquid chromatograph-mass spectrometer (LC-MS). The 13 batches of the samples showed good similarities (>0.910). The results of HCA, PCA and OPLS-DA showed that 13 batches of samples were divided into three groups, and different markers were selected. The method is simple, rapid and reproducible, and can provide a reference for the overall quality evaluation of Cibotii rhizoma.

Classification and characterization on sorghums based on HS-GC-IMS combined with OPLS-DA and GA-PLS.

Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) detected 206 and 186 samples of fresh and stored sorghums respectively with three major types in Baijiu industry. The fingerprints showed the differences of volatile compounds among fresh sorghum types by qualitative analysis and artificial recognition. Organic waxy sorghums had more contents of nonanal and 2-ethyl-1-hexanol but fewer ketones. The contents of acetoin in non-glutinous sorghums and organic non-glutinous sorghums were high. On the other hand, genetic algorithm-partial least squares (GA-PLS) selected 19 and 32 characteristic volatile compounds in fresh and stored sorghums. After centering and auto scaling to unit variance, the classification models with three major types of organic waxy sorghum, non-glutinous sorghum and organic non-glutinous sorghum were established based on orthogonal partial least squares-discriminant analysis (OPLS-DA). The goodness-of-fit (R2Y) and the goodness-of-prediction in cross-validation (Q2) in the model of fresh sorghum types all exceeded 0.9, in stored were over 0.8, the correct classification rates of external prediction were 95 % and 100 %, which revealed good performance and prediction. On this basis, the correct classification rates reached 87 % in organic waxy sorghums adulterated over 10 % ratio. GC-IMS combined with chemometrics is applicable in practical production for rapid identification of sorghum types and adulterations.

Quality evaluation of Lonicerae Japonicae Flos from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition.

INTRODUCTION: Lonicerae Japonicae Flos (LJF) is widely used in food and traditional Chinese medicine. To meet demand, Lonicera japonica Thunb. is widely cultivated in many provinces of China. However, reported studies on the quality evaluation of LJF only used a single or a few active components as indicators, which could not fully reflect the quality of LJF. OBJECTIVES: In the present study, we aimed to develop a methodology for comprehensively evaluating the quality of LJF from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition. MATERIALS AND METHODS: The HPLC method was developed for fingerprint analysis and was used to determine the contents of 19 components of LJF. To distinguish between samples and identify differential components, similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were performed. RESULTS: The HPLC fingerprint was established. Using the developed method, the contents of 19 components recognized in the fingerprint analysis were determined. Samples from different origins could be effectively distinguished. CONCLUSIONS: HPLC fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition is an efficient method for evaluating LJF.

Identification of Dalbergia odorifera and Its Counterfeits by HS-GC-MS / 中国实验方剂学杂志

ObjectiveTo screen the differential markers by analyzing volatile components in Dalbergia odorifera and its counterfeits， in order to provide reference for authentication of D. odorifera. MethodThe volatile components in D. odorifera and its counterfeits were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the GC conditions were heated by procedure（the initial temperature of the column was 50 ℃， the retention time was 1 min， and then the temperature was raised to 300 ℃ at 10 ℃ for 10 min）， the carrier gas was helium， and the flow rate was 1.0 mL·min-1， the split ratio was 10∶1， and the injection volume was 1 mL. The MS conditions used electron bombardment ionization（EI） with the scanning range of m/z 35-550. The compound species were identified by database matching， the relative content of each component was calculated by the peak area normalization method， and principal component analysis（PCA）， orthogonal partial least squares-discrimination analysis（OPLS-DA） and cluster analysis were performed on the detection results by SIMCA 14.1 software， and the differential components of D. odorifera and its counterfeits were screened out according to the variable importance in the projection（VIP） value>2 and P<0.05. ResultA total of 26， 17， 8， 22， 24 and 7 volatile components were identified from D. odorifera， D. bariensis， D. latifolia， D. benthamii， D. pinnata and D. cochinchinensis， respectively. Among them， there were 11 unique volatile components of D. odorifera， 6 unique volatile components of D. bariensis， 3 unique volatile components of D. latifolia， 6 unique volatile components of D. benthamii， 8 unique volatile components of D. pinnata， 4 unique volatile components of D. cochinchinensis. The PCA results showed that， except for D. latifolia and D. cochinchinensis， which could not be clearly distinguished， D. odorifera and other counterfeits could be distributed in a certain area， respectively. The OPLS-DA results showed that D. odorifera and its five counterfeits were clustered into one group each， indicating significant differences in volatile components between D. odorifera and its counterfeits. Finally， a total of 31 differential markers of volatile components between D. odoriferae and its counterfeits were screened. ConclusionHS-GC-MS combined with SIMCA 14.1 software can systematically elucidate the volatile differential components between D. odorifera and its counterfeits， which is suitable for rapid identification of them.

Geographical classification of opium samples from Myanmar and Afghanistan by NMR profiling and chemometrics.

This study presents a new strategy to discriminate between opium samples obtained from different geographical regions. Nuclear magnetic resonance (NMR) profiling and chemometrics were applied to geographical classification of opium originating from Myanmar and Afghanistan, which are two major opium producing countries in the world. A total of 50 Myanmar and 46 Afghanistan authentic opium samples were analyzed by 1 H-NMR, and the chemical profiles were characterized. Different sample preparation procedures, data processing methods, and chemometrics were compared to obtain the best classification effect. It was found that drying and the addition of buffer solutions were unnecessary for classification purposes; thus, the gum opium samples were extracted directly with CD3 OD, which shortened sample preparation time. A full discrimination between the two geographical origins was achieved by 1 H-NMR profiling and orthogonal partial least squares discriminant analysis. All 30 opium samples were classified correctly by the developed orthogonal partial least squares discriminant analysis model. Compared with traditional chromatography and mass spectrometry profiling methods, the 1 H-NMR profiling method was faster (with instrument analysis time of less than 3 min) and reproducible. This study provides new insights into the applying of NMR profiling and chemometrics to rapid drug profiling analysis.

Analysis of the Differences in Volatile Organic Compounds in Different Rice Varieties Based on GC-IMS Technology Combined with Multivariate Statistical Modelling.

In order to investigate the flavour characteristics of aromatic, glutinous, and nonaromatic rice, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyse the differences in volatile organic compounds (VOCs) amongst different rice varieties. The results showed that 103 signal peaks were detected in these rice varieties, and 91 volatile flavour substances were identified. Amongst them, 28 aldehydes (28.89~31.17%), 24 alcohols (34.85~40.52%), 14 ketones (12.26~14.74%), 12 esters (2.30~4.15%), 5 acids (7.80~10.85%), 3 furans (0.30~0.68%), 3 terpenes (0.34~0.64%), and 2 species of ethers (0.80~1.78%) were detected. SIMCA14.1 was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis, and some potential character markers (VIP > 1) were further screened out of the 91 flavour substances identified based on the variable important projections, including ethanol, 1-hexanol, hexanal, heptanal, nonanal, (E)-2-heptenal, octanal, trans-2-octenal, pentanal, acetone, 6-methyl-5-hepten-2-one, ethyl acetate, propyl acetate, acetic acid, and dimethyl sulphide. Based on the established fingerprint information, combined with principal component analysis and orthogonal partial least squares discriminant analysis, different rice varieties were also effectively classified, and the results of this study provide data references for the improvement in aromatic rice varieties.

Characterization of Key Compounds of Organic Acids and Aroma Volatiles in Fruits of Different Actinidia argute Resources Based on High-Performance Liquid Chromatography (HPLC) and Headspace Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS).

Actinidia arguta, known for its distinctive flavor and high nutritional value, has seen an increase in cultivation and variety identification. However, the characterization of its volatile aroma compounds remains limited. This study aimed to understand the flavor quality and key volatile aroma compounds of different A. arguta fruits. We examined 35 A. arguta resource fruits for soluble sugars, titratable acids, and sugar-acid ratios. Their organic acids and volatile aroma compounds were analyzed using high-performance liquid chromatography (HPLC) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). The study found that among the 35 samples tested, S12 had a higher sugar-acid ratio due to its higher sugar content despite having a high titratable acid content, making its fruit flavor superior to other sources. The A. arguta resource fruits can be classified into two types: those dominated by citric acid and those dominated by quinic acid. The analysis identified a total of 76 volatile aroma substances in 35 A. arguta resource fruits. These included 18 esters, 14 alcohols, 16 ketones, 12 aldehydes, seven terpenes, three pyrazines, two furans, two acids, and two other compounds. Aldehydes had the highest relative content of total volatile compounds. Using the orthogonal partial least squares discriminant method (OPLS-DA) analysis, with the 76 volatile aroma substances as dependent variables and different soft date kiwifruit resources as independent variables, 33 volatile aroma substances with variable importance in projection (VIP) greater than 1 were identified as the main aroma substances of A. arguta resource fruits. The volatile aroma compounds with VIP values greater than 1 were analyzed for odor activity value (OAV). The OAV values of isoamyl acetate, 3-methyl-1-butanol, 1-hexanol, and butanal were significantly higher than those of the other compounds. This suggests that these four volatile compounds contribute more to the overall aroma of A. arguta. This study is significant for understanding the differences between the fruit aromas of different A. arguta resources and for scientifically recognizing the characteristic compounds of the fruit aromas of different A. arguta resources.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

Modeling of soluble solid content of PE-packaged blueberries based on near-infrared spectroscopy with back propagation neural network and partial least squares (BP-PLS) algorithm.

Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (RP 2 = 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (RP 2 = 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (RP 2 = 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

Analysis of metabolite differences between South Korean and Chinese yellow goosefish (Lophius litulon) using capillary electrophoresis time-of-flight mass spectrometry.

The yellow goosefish is a benthic fish that belongs to the family Lophiidae and order Lophiiformes and is distributed in the Yellow and East China Seas. This study aimed to distinguish between yellow goosefish from different geographical origins by analyzing their metabolites. Capillary electrophoresis time-of-flight mass spectrometry was used to analyze metabolite profiles in the muscle tissues of yellow goosefish to distinguish between Korean and Chinese yellow goosefish. In total, 271 putative metabolites were extracted using 50% acetonitrile in water. Principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish different geographical origins using the metabolite profiles obtained. The R2 and Q2 values of the OPLS-DA model were 0.856 and 0.695, respectively, indicating that the model was well-fitted and had good predictability. The heat map revealed that nucleic acid and amino compounds differed between the Korean and Chinese fish, and the variable importance in the projection scores obtained from OPLS-DA showed that there were geographical differences in the primary metabolites (5'-methylthioadenosine, adenosine, uridine 5-diphosphate, guanosine 5-diphosphate, urea, homocarnosine, O-acetylcarnitine, cycloleucine, cycloleucine S-adenosylmethionine, S-adenosylhomocysteine, ethanolamine, myo-inositol 1-phosphate), which were identified as potential candidate biomarkers.

Distinct serum steroid profiles between adrenal Cushing syndrome and Cushing disease.

Background: Differentiating between adrenal Cushing syndrome (adrenal CS) and Cushing disease (CD) can be challenging if there are equivocal or falsely elevated adrenocorticotropic hormone (ACTH) values. We aim to investigate the diagnostic value of serum steroid profiles in differentiating adrenal CS from CD. Method: A total of 11 serum steroids in adrenal CS (n = 13) and CD (n = 15) were analyzed by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Age- and gender-specific steroid ratios were generated by dividing the actual steroid concentration by the upper limit of the relevant reference range. A principal component analysis (PCA) and an orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. Results: The PCA and OPLS-DA analyses showed distinct serum steroid profiles between adrenal CS and CD. Dehydroepiandrosterone sulfate (DHEA-S), dehydroepiandrosterone (DHEA), and androstenedione ratios were identified as biomarkers for discrimination by variable importance in projection (VIP) in combination with t-tests. The sensitivity and specificity of DHEA-S ratios <0.40 were 92.31% (95% CI 64.0%-99.8%) and 93.33% (95% CI 68.1%-99.8%), respectively, in identifying adrenal CS. The sensitivity and specificity of DHEA ratios <0.18 were 100% (95% CI 75.3%-100.0%) and 100% (95% CI 78.2%-100.0%), respectively, in identifying adrenal CS. Conclusion: Our data support the clinical use of the DHEA-S and DHEA ratios in the differential diagnosis of adrenal CS and CD, especially when falsely elevated ACTH is suspected.

[Simultaneous determination of eleven volatile components in Cinnamomi Oleum by GC-MS].

A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.

Rapid Identification of Benign Gallbladder Diseases Using Serum Surface-Enhanced Raman Spectroscopy Combined with Multivariate Statistical Analysis.

In this study, we looked at the viability of utilizing serum to differentiate between gallbladder (GB) stones and GB polyps using Surface-enhanced Raman spectroscopy (SERS), which has the potential to be a quick and accurate means of diagnosing benign GB diseases. Rapid and label-free SERS was used to conduct the tests on 148 serum samples, which included those from 51 patients with GB stones, 25 patients with GB polyps and 72 healthy persons. We used an Ag colloid as a Raman spectrum enhancement substrate. In addition, we employed orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component linear discriminant analysis (PCA-LDA) to compare and diagnose the serum SERS spectra of GB stones and GB polyps. The diagnostic results showed that the sensitivity, specificity, and area under curve (AUC) values of the GB stones and GB polyps based on OPLS-DA algorithm reached 90.2%, 97.2%, 0.995 and 92.0%, 100%, 0.995, respectively. This study demonstrated an accurate and rapid means of combining serum SERS spectra with OPLS-DA to identify GB stones and GB polyps.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling.

INTRODUCTION: This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation. METHOD: A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS). RESULTS: The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure. CONCLUSIONS: The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers.

BACKGROUND: Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes. METHODS: Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation. RESULTS: Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm-1 peak [νas(PO2-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group. CONCLUSIONS: Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Predictive models built upon annotated and validated intake biomarkers in urine using paired or unpaired analysis helped to classify cranberry juice consumers in a randomized, double-blinded, placebo-controlled, and crossover study.

Intake biomarkers of cranberry juice in women can assess consumption in clinical trials. Discriminant biomarkers in urine may explain urinary tract infection (UTI) preventive activities. We hypothesized that validated and annotated discriminant metabolites in human urine could be used as intake biomarkers in building predictive multivariate models to classify cranberry consumers. Urine samples were collected from 16 healthy women aged 18 to 29 years at baseline and after 3- and 21-day consumption of cranberry or placebo juice in a double-blind, crossover study. Urine metabolomes were analyzed using ultra high-performance liquid chromatography coupled with Orbitrap mass spectrometry. Paired and unpaired multivariate analyses were used to annotate or identify discriminant metabolic features after cranberry consumption. Twenty-six discriminant metabolic features (paired analysis) and 27 (unpaired analysis) after cranberry consumption in an open-label intervention were rediscovered in the blinded study. These metabolites included exogenous (quinic acid) and endogenous ones (hippuric acid). The paired analysis showed better model fitting with partial least-square discriminant analysis models built on all metabolites than the unpaired analysis. Predictive models built on shared metabolites by the unpaired analysis were able to classify cranberry juice consumers with 84.4% to 100% correction rates, overall better than the paired analysis (50%-100%). The double-blind study validated discriminant metabolites from a previous open-label study. These urinary metabolites may be associated with the ability of cranberries to prevent UTIs and serve as potential cranberry intake biomarkers. It reveals the importance of selecting the right predictive models to classify cranberry consumers with higher than 95% correction rates.

Multi-omics Analysis of the Role of PHGDH in Colon Cancer.

Objectives: Serine metabolism is essential for tumor cells. Endogenous serine arises from de novo synthesis pathways. As the rate-limiting enzyme of this pathway, PHGDH is highly expressed in a variety of tumors including colon cancer. Therefore, targeted inhibition of PHGDH is an important strategy for anti-tumor therapy research. However, the specific gene expression and metabolic pathways regulated by PHGDH in colon cancer are still unclear. Our study was aimed to clarified the role of PHGDH in serine metabolism in colon cancer to provide new knowledge for in-depth understanding of serine metabolism and PHGDH function in colon cancer. Methods: In this study, we analyzed the gene expression and metabolic remodeling process of colon cancer cells (SW620) after targeted inhibition of PHGDH by gene transcriptomics and metabolomics. LC-MS analysis was performed in 293T cells to PHGDH gene transcription and protein post-translational modification under depriving exogenous serine. Results: We found that amino acid transporters, amino acid metabolism, lipid synthesis related pathways compensation and other processes are involved in the response process after PHGDH inhibition. And ATF4 mediated the transcriptional expression of PHGDH under exogenous serine deficiency conditions. While LC-MS analysis of post-translational modification revealed that PHGDH produced changes in acetylation sites after serine deprivation that the K289 site was lost, and a new acetylation site K21was produced. Conclusion: Our study performed transcriptomic and metabolomic analysis by inhibiting PHGDH, thus clarifying the role of PHGDH in gene transcription and metabolism in colon cancer cells. The mechanism of high PHGDH expression in colon cancer cells and the acetylation modification that occurs in PHGDH protein were also clarified by serine deprivation. In our study, the role of PHGDH in serine metabolism in colon cancer was clarified by multi-omics analysis to provide new knowledge for in-depth understanding of serine metabolism and PHGDH function in colon cancer.