Weiying Lu

Identification and Quantification of Phytochemical Composition and Anti-inflammatory, Cellular Antioxidant, and Radical Scavenging Activities of 12 Plantago Species

Twenty-eight seed samples of 12 Plantago species were investigated for their chemical compositions and anti-inflammatory, cellular antioxidant, and radical scavenging properties. A new UPLC-UV procedure was developed and applied to quantify acteoside and geniposidic acid, the characteristic constituents of the genus Plantago. The amounts of acteoside and geniposidic acid ranged from 0.07 to 15.96 mg/g and from 0.05 to 10.04 mg/g in the tested samples, respectively. Furthermore, 26 compounds were tentatively identified by UPLC/Q-TOF-MS analysis. The Plantago samples significantly differed in their phytochemical compositions. The extracts of Plantago seeds also showed inhibitory effects on LPS-induced IL-1β, IL-6, and COX-2 mRNA expression in RAW 264.7 mouse macrophage cells. Additionally, significant variations were observed among different samples on cellular antioxidant activities in HepG2 cells, as well as DPPH and hydroxyl radical scavenging capacities. The results from this study may be used to promote the use of the genus Plantago in improving human health.

Partial Least-Squares-Discriminant Analysis Differentiating Chinese Wolfberries by UPLC–MS and Flow Injection Mass Spectrometric (FIMS) Fingerprints

Lycium barbarum L. fruits (Chinese wolfberries) were differentiated for their cultivation locations and the cultivars by ultraperformance liquid chromatography coupled with mass spectrometry (UPLC-MS) and flow injection mass spectrometric (FIMS) fingerprinting techniques combined with chemometrics analyses. The partial least-squares-discriminant analysis (PLS-DA) was applied to the data projection and supervised learning with validation. The samples formed clusters in the projected data. The prediction accuracies by PLS-DA with bootstrapped Latin partition validation were greater than 90% for all models. The chemical profiles of Chinese wolfberries were also obtained. The differentiation techniques might be utilized for Chinese wolfberry authentication.

Simultaneous HPLC quantification of five major triterpene alcohol and sterol ferulates in rice bran oil using a single reference standard

A high performance liquid chromatography (HPLC) method was developed for simultaneous quantification of five major triterpene alcohol and sterol ferulates in rice bran oils (RBO) with a single internal standard, cycloartenyl ferulate. The five compounds are cycloartenyl ferulate (1), 24-methylene cycloartanyl ferulate (2), campesteryl ferulate (3), sitosteryl ferulate (4) and stigmastanyl ferulate (5). All five compounds had good linear concentration-measurement relationships (r(2) ≥ 0.9995) and possessed similar relative response factors. The relative deviation of this method was less than 2.5% for intra- and inter-day assays, and the average recovery varied from 95.1% to 99.4%. The new method was validated by comparing the amount of 24-methylene cycloartanyl ferulate (2) in 17 RBO samples obtained with this method and that with an external standard method. This method was also successfully applied to determine five major triterpene alcohol and sterol ferulates in 17 batches of RBO samples. The results demonstrated that the present method could be utilised for quality control of RBO since some of the reference standards are not commercially available.

Ignitable liquid identification using gas chromatography/mass spectrometry data by projected difference resolution mapping and fuzzy rule-building expert system classification

The gasoline and kerosene collected from different locations in the United States were identified by gas chromatography/mass spectrometry (GC/MS) followed by chemometric analysis. Classifications based on two-way profiles and target component ratios were compared. The projected difference resolution (PDR) mapping was applied to measure the differences among the ignitable liquid (IL) samples by their GC/MS profiles quantitatively. Fuzzy rule-building expert systems (FuRESs) were applied to classify individual ILs. The FuRES models yielded correct classification rates greater than 90% for discriminating between samples. PDR mapping, a new method for characterizing complex data sets was consistent with the FuRES classification result.

Differentiating organically and conventionally grown oregano using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), headspace gas chromatography with flame ionization detection (headspace-GC-FID), and flow injection mass spectrum (FIMS) fingerprints combined with multivariate data analysis.

Ultraperformance liquid chromatography mass spectrometry (UPLC-MS), flow injection mass spectrometry (FIMS), and headspace gas chromatography (headspace-GC) combined with multivariate data analysis techniques were examined and compared in differentiating organically grown oregano from that grown conventionally. It is the first time that headspace-GC fingerprinting technology is reported in differentiating organically and conventionally grown spice samples. The results also indicated that UPLC-MS, FIMS, and headspace-GC-FID fingerprints with OPLS-DA were able to effectively distinguish oreganos under different growing conditions, whereas with PCA, only FIMS fingerprint could differentiate the organically and conventionally grown oregano samples. UPLC fingerprinting provided detailed information about the chemical composition of oregano with a longer analysis time, whereas FIMS finished a sample analysis within 1 min. On the other hand, headspace GC-FID fingerprinting required no sample pretreatment, suggesting its potential as a high-throughput method in distinguishing organically and conventionally grown oregano samples. In addition, chemical components in oregano were identified by their molecular weight using QTOF-MS and headspace-GC-MS.

Slice transform-based weight updating strategy for PLS

A modified partial least squares (PLS) algorithm is presented on the basis of a novel weight updating strategy. The new weight can handle situations with directions in X space having large variance unrelated to Y, whereas the linear PLS may not work well. In the proposed algorithm, the slice transform technique is introduced to provide a piecewise linear representation of the weight vectors. Then, the corresponding mapping functions are estimated by a least square criterion of the inner relation between the observed variables and the score of response variables. At last, weight vectors are updated by the obtained mapping functions, and the corresponding scores and loadings are calculated with the new weights. An optimal piecewise linear replacements of the PLS weights are achieved by the proposed method. The predictive performances of the new approach and other methods are compared statistically using the Wilcoxon signed rank test. Experimental results show that the proposed method can achieve simpler models, whereas the model performances are at least comparable with PLS and other methods. Copyright

Triacylglycerol compositions of sunflower, corn and soybean oils examined with supercritical CO2 ultra-performance convergence chromatography combined with quadrupole time-of-flight mass spectrometry

A supercritical CO2 ultra-performance convergence chromatography (UPC2) system was utilized with a quadrupole time-of-flight mass spectrometry (Q-TOF MS) to examine the triacylglycerol compositions of sunflower, corn and soybean oils. UPC2 provided an excellent resolution and separation for the triacylglycerols, while the high performance Q-TOF MS system was able to provide the molecular weight and fragment ions information for triacylglycerol compound characterization. A total of 33 triacylglycerols were identified based on their elementary compositions and MS2 fragment ion profiles, and their levels in the three oils were estimated. The combination of UPC2 and Q-TOF MS may determine triacylglycerol compositions for oils and fats, and provide sn-position information for fatty acids, which may be important for food nutritional value and stability.

Technical note: Nontargeted detection of adulterated plant proteins in raw milk by UPLC-quadrupole time-of-flight mass spectrometric proteomics combined with chemometrics

We built and validated a chemometric model to detect possible milk adulteration with plant proteins. Specifically, we extracted proteins in raw milk, treated with tryptic digestion, and obtained peptide fingerprints by UPLC-quadrupole time-of-flight-mass spectrometry with proteomics to differentiate authentic milks from their counterparts adulterated with nonmilk proteins. This approach is able to detect soybean and pea powder-adulterated milks at as low as 1% (wt/wt). Additionally, we obtained the characteristic peptide sequences for milk authentication by principal component analysis. The prediction accuracies for milk authentication by partial least-squares-discriminant analysis were greater than 95%. These results indicated that peptide fingerprints with the chemometric analysis could be successfully applied for milk quality control.

A discriminant based charge deconvolution analysis pipeline for protein profiling of whole cell extracts using liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry.

A discriminant based charge deconvolution analysis pipeline is proposed. The molecular weight determination (MoWeD) charge deconvolution method was applied directly to the discrimination rules obtained by the fuzzy rule-building expert system (FuRES) pattern classifier. This approach was demonstrated with synthetic electrospray ionization-mass spectra. Identification of the tentative protein biomarkers by bacterial cell extracts of Salmonella enterica serovar typhimurium strains A1 and A19 by liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) was also demonstrated. The data analysis time was reduced by applying this approach. In addition, this method was less affected by noise and baseline drift.

Rapid detection of milk adulteration using intact protein flow injection mass spectrometric fingerprints combined with chemometrics

Flow injection mass spectrometry (FIMS) combined with chemometrics was evaluated for rapidly detecting economically motivated adulteration (EMA) of milk. Twenty-two pure milk and thirty-five counterparts adulterated with soybean, pea, and whey protein isolates at 0.5, 1, 3, 5, and 10% (w/w) levels were analyzed. The principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and support vector machine (SVM) classification models indicated that the adulterated milks could successfully be classified from the pure milks. FIMS combined with chemometrics might be an effective method to detect possible EMA in milk.