Analysis Of Multiple Components Research Articles

Envirotyping helps in better understanding the root cause of success and limitations of rainfed production systems

The current diagnostic agronomy study of the Bankura region of West Bengal, India, examined the variations in crop yields through a socio-ecological analysis of multiple production system components. Envirotyping for root cause analysis was employed to delve into the variables that affect the performance of rainfed production systems. Mother Earth, man, machine, management, and materials (5Ms concept) were the five indicators under which the variables were grouped. Findings demonstrated the fragility of the region’s soils due to its undulating terrain, unpredictable rainfall patterns, and frequent drought scenarios. The LULC’s NDVI showed that the agricultural area is about 60% and 43% of the total geographical area in the Hirbandh and Ranibandh blocks, respectively. Soils are acidic and diagnosed with deficiency of both macro and micronutrients (phosphorous, sulfur, and boron) having poor water holding capacity (35 to 55 mm for a 50 cm soil depth). The sand and soil organic carbon contents ranged between 43.04%–82.32% and 0.17%–1.01%, respectively with a low bacterial population. These factors are the root cause for low cropping intensity (106%) and low paddy productivity (3,021 kg/ha). Overall, the study contributes to designing and scaling-up of sustainable landscape management practices that could ensure higher cropping intensity and system productivity in similar agro-ecologies with limited evidence.

Machine learning approaches for simultaneous spectrophotometric determination of heavy metal ions in water samples

In this study, the simultaneous determination of Co, Cd, Ni, Cu, and Pb was carried out as a color complex with 4-(2-pyridylazo) resorcinol in an aqueous solution under the assesting of machine learning. A partial least-squares multivariate linear regression and artificial neuron network for the analysis of mixtures of metals were developed. MATLAB is a powerful software machine learning program that was used to support matrix calculations and displays. The benefit of MATLAB in the construction of the machine learning model allows the development of a rapid and highly effective analysis of multiple components in the mixtures without separation and enrichment. For individual determinations, the working ranges were discovered as the important information for choosing the initial concentration of each heavy metal in a mixture, r. The results of analysis of Ni2+, Pb2+, and Cd2+ by two methods Partial Least Squares - PLS and Artificial Neural Networks - ANN are sensitive and accurate for simultaneous determination of the concentration of these ions in the synthesis mixture with a high regression coefficient of 0.993, respectively, 0.997, 0.997 for Ni2+, Pb2+ and Cd2+. As for Cu2+ and Co2+, the accuracy is higher when using the ANN method.

Application of Tchebichef image moment method for simultaneous quantitative analysis of multiple components in B-vitamins

Application of Tchebichef image moment method for simultaneous quantitative analysis of multiple components in B-vitamins

Quantitative analysis of multi-components by single marker method combined with UPLC-PAD fingerprint analysis based on saikosaponin for discrimination of Bupleuri Radix according to geographical origin.

Background: Saikosaponins are regarded as one of the most likely antipyretic constituents of Bupleuri Radix, establishing a comprehensive method that can reflect both the proportion of all constituents and the content of each saikosaponin is critical for its quality evaluation. Methods: In this study, the combination method of quantitative analysis of multiple components with a single marker (QAMS) and fingerprint was firstly established for simultaneous determination of 7 kinds of saikosaponins in Bupleuri Radix by ultra-high performance liquid chromatography (UPLC). Results: The results showed that saikosaponin d was identified as the optimum IR by evaluating the fluctuations and stability of the relative calibration factors (RCFs) under four different conditions. The new QAMS method has been confirmed to accurately quantify the 7 kinds of saikosaponins by comparing the obtained results with those obtained from external standard method and successfully classify the 20 batches of Bupleuri Radix from 8 provinces of China. The experimental time of fingerprint was significantly reduced to approximate 0.5h through UPLC-PAD method, a total of 17 common peaks were identified. Conclusion: The QAMS-fingerprint method is feasible and reliable for the quality evaluation of Bupleuri Radix. This method could be considered to be spread in the production enterprises of Bupleuri Radix.

Quantitative, multi-species gas sensing using broadband terahertz time-domain spectroscopy

The broadband terahertz wave, with its correspondence to the fingerprint spectra of gas molecules and relatively high transmittance through smoke, aerosol, and combustion environments, bears great potential for gas detection and combustion diagnostics. While access to rotational spectral fingerprints in the terahertz region provides opportunities for species-selective diagnostics with minimized background and cross interference, few studies have been devoted to direct, quantitative, and simultaneous analysis of multiple components exploiting the terahertz region. In this work, we achieve quantitative measurements of CO, NH3 and H2O gas concentrations at standard temperatures and pressures over a bandwidth of 1 THz, using direct absorption spectrum from femtosecond-laser-based terahertz time-domain spectroscopy. Spectral fitting of the fully resolved rotational lines yields good precision and accuracy with validation against calibrated mixtures. The estimated detection limits of the multi-species sensing system are 250 ppm m, 7 ppm m and 4 ppm m for CO, NH3 and H2O, respectively. The demonstration of quantitative, multi-species gas sensing indicates the feasibility and practical value of using broadband terahertz absorption spectroscopy for real-time, quantitative analysis and speciation of multicomponent gases in complicated practical environments such as combustion and multi-phase flows.

A rapid localization and analysis method for isoquinoline alkaloids with fluorescence in Coptis chinensis Franch. By fabricating the nano-silver sol as a substrate for surface-enhanced Raman spectroscopy

BackgroundThe quality of traditional Chinese medicines (TCMs) directly impacts their clinical efficacy and drug safety, making standardization a critical component of modern TCMs. Surface-enhanced Raman spectroscopy (SERS) is an effective physical detection method with speed, sensitivity, and suitability for large sample analyses. In this study, a SERS analysis method was developed using a nano-silver sol as the matrix to address the interference of fluorescence components in TCMs and overcome the limitations of traditional detection methods. ResultsThe higher sensitivity and efficiency of SERS was used, enabling detection of a single sample within 30 s. Coptis chinensis Franch. (CCF) was chosen as the model medicine, the nano-silver sol was used as the matrix, and CCF’s fourteen main fluorescent alkaloids were tested as index components. Typical signal peaks of the main components in CCF corresponded to the bending deformation of the nitrogen-containing ring plane outer ring system, methoxy stretching vibration, and isoquinoline ring deformation vibration. Through SERS detection of different parts, the distribution content of the main active components in the cortex of CCF was found to be lower than that in the xylem and phloem. Additionally, rapid quality control analyses indicated that among the nine batches of original medicinal materials purchased from Emei and Guangxi, the main active ingredient showed a higher content. SignificanceA SERS-based method for the rapid localization and analysis of multiple components of TCMs was established. The findings highlight the potential of SERS as a valuable tool for the analysis and quality control of TCMs, especially for fluorescent components.

Qualitative and Quantitative Analysis of Five Indoles or Indazole Amide Synthetic Cannabinoids in Suspected E-Cigarette Oil by GC-MS.

To establish the GC-MS qualitative and quantitative analysis methods for the synthetic cannabinoids, its main matrix and additives in suspicious electronic cigarette (e-cigarette) oil samples. The e-cigarette oil samples were analyzed by GC-MS after diluted with methanol. Synthetic cannabinoids, its main matrix and additives in e-cigarette oil samples were qualitatively analyzed by the characteristic fragment ions and retention time. The synthetic cannabinoids were quantitatively analyzed by using the selective ion monitoring mode. The linear range of each compound in GC-MS quantitative method was 0.025-1 mg/mL, the matrix recovery rate was 94%-103%, the intra-day precision relative standard deviations (RSD) was less than 2.5%, and inter-day precision RSD was less than 4.0%. Five indoles or indazole amide synthetic cannabinoids were detected in 25 e-cigarette samples. The main matrixes of e-cigarette samples were propylene glycol and glycerol. Additives such as N,2,3-trimethyl-2-isopropyl butanamide (WS-23), glycerol triacetate and nicotine were detected in some samples. The content range of synthetic cannabinoids in 25 e-cigarette samples was 0.05%-2.74%. The GC-MS method for synthesizing cannabinoid, matrix and additive in e-cigarette oil samples has good selectivity, high resolution, low detection limit, and can be used for simultaneous qualitative and quantitative analysis of multiple components; The explored fragment ion fragmentation mechanism of the electron bombardment ion source of indole or indoxamide compounds helps to identify such substances or other compounds with similar structures in cases.

Evaluation of the Quality of Selected White and Red Wines Produced from Moravia Region of Czech Republic Using Physicochemical Analysis, FTIR Infrared Spectroscopy and Chemometric Techniques.

The FTIR-ATR method coupled with the multivariate analysis of specific spectral areas of samples was developed to characterize two white grape varieties (Sauvignon Blanc and Hibernal) and two blue grape varieties (André and Cabernet Moravia) of wine planted and harvested in the Moravia region, Czech Republic. Principal component analysis and hierarchical cluster analysis were performed using fingerprint regions of FTIR spectra for all wines. The results obtained by principal component analysis in combination with linear discriminant analysis (PCA-LDA) scores yielded clear separation between the four classes of samples and showed very good discrimination between the wine samples, with a 91.7% overall classification rate for the samples. The conducted FTIR spectroscopy studies coupled with chemometrics allowed for the swift analysis of multiple wine components with minimal sample preparation. These methods can be used in research to improve specific properties of these wines, which will undoubtedly enhance the quality of the final wine samples obtained.

Quality evaluation of Hawk tea from different months and regions based on quantitative analysis of multiple components with a single marker (QAMS) combined with HPLC fingerprint.

Hawk tea, a medicinal and edible plant, has been consumed for thousands of years in Southwest China. To date, no unified food safety standard for Hawk tea has been established, and systematic research on the quality of Hawk tea is lacking. The aim of this study was to develop a comprehensive evaluation method for the quality of Hawk tea based on inclusions content, high-performance liquid chromatography (HPLC) fingerprinting combined with the quantitative analysis of multiple components with a single marker (QAMS) method. The contents of total flavonoids, total phenols, total polysaccharides, and total protein were determined using the colorimetric method. An effective comprehensive evaluation method was established to classify the 16 batches of samples based on HPLC fingerprint analysis combined with similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least-squares discrimination analysis (PLS-DA), and the QAMS method. Flavonoids were the main chemical components of Hawk tea. The accuracy of the QAMS method was verified by comparing the calculated results with those of the external standard method (ESM). No significant differences were found between the two methods. Additionally, the fingerprint of Hawk tea was also established. The method established in this study can be used for the comprehensive quality evaluation of Hawk tea and can also provide a reference for the quality evaluation of other herbal medicines.

Holistically assessing the quality consistency of compound liquorice tablets from similarities of both all chemical fingerprints and the integrated dissolution curves by systematically quantified fingerprint method

In recent years, traditional analytical methods have failed to meet the widespread use of multi-component Chinese pharmaceutical formulations. To solve this problem, this study proposed a comprehensive analytical strategy using compound liquorice tablets (CLTs) as an example, both in terms of chemical quality and dissolution curve consistency. Firstly, the peak purity of the two wavelengths was checked using dual-wavelength absorbance coefficient ratio spectra (DARS) to avoid the fingerprint bias caused by peak purity. Secondly, liquid-phase dual-wavelength tandem fingerprint (DWTF) of 38 batches of CLTs was established for the first time. The two analytical methods were also evaluated using the systematically quantified fingerprint method (SQFM), and the 38 batches of samples were classified into two grades with good quality consistency. Quantitative analysis of the five markers of CLTs was performed simultaneously using the standard curve method (SCM) and the quantitative analysis of multiple components by single marker (QAMS). The results showed no significant differences between the two analytical methods (p > 0.5). In addition, the in vitro dissolution of CLTs in two media (pure water and pH = 4.5 medium) was determined by the total UV fingerprint dissolution assay. The similarity of the dissolution curves was also analyzed by combining the f2 factor and the dissolution-systematically quantified fingerprint method (DSQFM). The result showed that most of the samples had f2 > 50 and Pm satisfied the range of 70–130%. Finally, a principal component analysis (PCA) model was developed to combine the evaluation parameters of chemical fingerprint and dissolution curves for comprehensive analysis of the samples. In this study, a chromatographic and dissolution-based quality analysis method was proposed, which effectively overcomes the shortcomings of previous analytical methods and provides a scientific analytical method for the quality control of natural drugs.

Quantitative determination of rhamnolipid using HPLC-UV through carboxyl labeling.

Rhamnolipid, as a low-toxic, biodegradable and environmentally friendly biosurfactant, has broad application prospects in many industries. However, the quantitative determination of rhamnolipid is still a challenging task. Here, a new sensitive method for the quantitative analysis of rhamnolipid based on a simple derivatization reaction was developed. In this study, 3-[3'-(l-rhamnopyranosyloxy) decanoyloxy] decanoic acid (Rha-C10-C10) and 3-[3'-(2'-O-α-l-rhamnopyranosyloxy) decanoyloxy] decanoic acid (Rha-Rha-C10-C10) were utilized as the representative rhamnolipids. Liquid chromatography-mass spectrometry and high-performance liquid chromatography-ultra violet results showed that these two compounds were successfully labeled with 1 N1-(4-nitrophenyl)-1,2-ethylenediamine. There was an excellent linear relationship between rhamnolipid concentration and peak area of labeled rhamnolipid. The detection limits of the Rha-C10-C10 and Rha-Rha-C10-C10 were 0.018mg/L (36nmol/L) and 0.014mg/L (22nmol/L), respectively. The established amidation method was suitable for the accurate analysis of rhamnolipids in the biotechnological process. The method had good reproducibility with the relative standard deviation of 0.96% and 0.79%, respectively, and sufficient accuracy with a recovery of 96%-100%. This method was applied to quantitative analysis of 10 rhamnolipid homologs metabolized by Pseudomonas aeruginosa LJ-8. The single labeling method was used for the quantitative analysis of multiple components, which provided an effective method for the quality evaluation of other glycolipids with carboxyl groups.

Resolving Analytical Challenges in Pharmaceutical Process Monitoring Using Multivariate Analysis Methods: Applications in Process Understanding, Control, and Improvement

Multivariate analysis (MVA) refers to an assortment of statistical tools developed to handle situations in which more than one variable is involved. MVA is indispensable for data interpretation and for extraction of meaningful data, especially from fast acquisition instruments and spectral imaging techniques. This article reviews trends in the application of MVA to pharmaceutical manufacturing and control. The MVA models most commonly used in drug analysis are compared. The potential of MVA to resolve analytical challenges, such as overcoming matrix effects, extracting reliable data from dynamic matrices, clustering data into meaningful groups, removing noise from analytical response, resolving spectral overlaps, and providing simultaneous analysis of multiple components, are tackled with examples. Industrial applications of MVA capabilities are described, with special emphasis on process analytical technology (PAT) and how MVA can aid in process understanding and control. A scheme for selecting an MVA model according to the available data and the required information is proposed.

Recognition and Estimation for Frequency-Modulated Continuous-Wave Radars in Unknown and Complex Spectrum Environments

The recognition and estimation of frequency-modulated continuous-wave (FMCW) radar signals is critical for both military electronic countermeasures (ECM) and civilian autonomous driving. However, the increasingly complex radio environment poses two new challenges for detection equipment. First, multiple FMCW radars can share the same frequency band, resulting in overlap of received FMCW signals in the time-frequency domain. Second, unexpected signals in unknown spectrum environments can affect the cognitive performance of FMCW signals. This paper proposes a semantic-based learning network (SLN) that simultaneously learns modulation classification and parameter regression of FMCW signals. By integrating recognition and estimation into a single network, the system can be optimized end-to-end as a whole. Additionally, instance-level semantic learning facilitates the parallel analysis of multiple components in overlapping signals. Finally, contrastive clustering in SLN achieves suppression of unexpected signals. Numerous comparative experimental results demonstrate that SLN has the desirable ability to simultaneously recognize and estimate FMCW signals in real-time, even in unknown spectrum environments.

Divisive Refinement of Metal Fiber at the PM0.1 Classification Stage for PM0.5-0.1 Sampling with Nanosampler

Multi-component analysis of PM0.5-0.1 collected by Nanosampler II metal (Steel Use Stainless: SUS) wool fiber at the PM0.1 classification stage is challenging owing to difficulties in SUS wool fiber separation after collection. A 4-division (4D) cartridge was developed to enable quantitative analysis of multiple components by dividing the SUS wool fibers into four sections. The performance of the 4D cartridge was evaluated using sulfate ion concentrations of actual atmospheric particulate matter (PM). Observations using the 4D cartridge revealed that the relative standard deviation (RSD) of sulfate ions was lower for mesh with a large open area. In the case of the mesh with a large opening area, the partition did not block too much of the cross-sectional area of the SUS wool fiber, thus minimizing its effect on the airflow and possibly suppressing particle adhesion on the stainless-steel mesh. The PM0.1 classification efficiency test was conducted, and a linear analysis of the total filling mass of SUS wool fiber along with classifying efficiency of 100 nm PM was performed. In the 4D cartridge, it was estimated that 8.7 mg of SUS wool fiber was required to achieve a classification efficiency of 100 nm at 50% cutoff diameter because the apparent volume fraction of SUS wool fiber increases with the introduction of the partition. Using optimal mesh and amount of SUS wool fiber, the average RSD of sulfate ions was 5.6%, which was within the acceptable range (± 15%) for reanalysis of the Ministry of the Environment in Japan due to changes in analytical sensitivity of ionic components, confirming that PM was evenly collected from the four pieces of SUS wool fiber. This enabled multi-component analysis of all particle sizes including PM0.5-0.1 through the classified collection of PM using Nanosampler II.

Parallel detection of chemical reactions in a microfluidic platform using hyperpolarized nuclear magnetic resonance.

The sensitivity of NMR may be enhanced by more than four orders of magnitude via dissolution dynamic nuclear polarization (dDNP), potentially allowing real-time, in situ analysis of chemical reactions. However, there has been no widespread use of the technique for this application and the major limitation has been the low experimental throughput caused by the time-consuming polarization build-up process at cryogenic temperatures and fast decay of the hyper-intense signal post dissolution. To overcome this limitation, we have developed a microfluidic device compatible with dDNP-MR spectroscopic imaging methods for detection of reactants and products in chemical reactions in which up to 8 reactions can be measured simultaneously using a single dDNP sample. Multiple MR spectroscopic data sets can be generated under the same exact conditions of hyperpolarized solute polarization, concentration, pH, and temperature. A proof-of-concept for the technology is demonstrated by identifying the reactants in the decarboxylation of pyruvate via hydrogen peroxide (e.g. 2-hydroperoxy-2-hydroxypropanoate, peroxymonocarbonate and CO2). dDNP-MR allows tracing of fast chemical reactions that would be barely detectable at thermal equilibrium by MR. We envisage that dDNP-MR spectroscopic imaging combined with microfluidics will provide a new high-throughput method for dDNP enhanced MR analysis of multiple components in chemical reactions and for non-destructive in situ metabolic analysis of hyperpolarized substrates in biological samples for laboratory and preclinical research.

Identification of anti-inflammatory components in Dioscorea nipponica Makino based on HPLC-MS/MS, quantitative analysis of multiple components by single marker and chemometric methods

The rhizome of Dioscorea nipponica Makino (RDN) is a widely used herbal medicine, which has significant anti-inflammatory activities on various inflammatory diseases. However, the bioactive compositions responsible for the anti-inflammatory activity of RDN are still unknown. This study aimed to identify the anti-inflammatory bioactive compounds in RDN using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q/TOF-MS), quantitative analysis of multiple components by single marker (QAMS) and chemometric methods. Firstly, an HPLC-Q/TOF-MS method was employed for identification of bioactive steroidal saponins in RND, and a total of twelve steroid saponins were identified. Then, QAMS method was employed to determine the contents of seven bioactive steroidal saponins, including protodioscin, protogracillin, methyl protodioscin, pseudoprotodioscin, pseudoprogracillin, dioscin and gracillin in RND samples using dioscin as the reference analyte. The anti-inflammatory effects of RDN samples were then evaluated by inhibition of NO production in LPS-induced RAW264.7 cells. Furthermore, chemometric methods, including Pearson correlation analysis and partial least squares regression (PLSR) were employed to investigate the correlations between chemical components and anti-inflammatory activities, and explore the potential anti-inflammatory bioactive compounds of RDN. The results indicated that protodioscin, dioscin and gracillin were selected as the major anti-inflammatory compounds in RND. The further verification experiments showed that protodioscin, dioscin and gracillin exhibited great inhibition on NO production with IC50 values (the half maximal inhibitory concentration) of 0.712 μM, 0.469 μM and 0.815 μM, respectively. They also significantly reduced the levels of TNF-α, IL-1β, and IL-6 in LPS-induced RAW264.7 cells. The present study provided evidences for the anti-inflammatory activity of RND and identification of the anti-inflammatory components in RDN.

Noninvasive and simultaneous quantitative analysis of multiple human blood components based on the grey analysis system

Noninvasive detection of human blood components is the dream of human beings and the goal of clinical detection. From the perspective of mathematical analysis, based on the grey analysis system, the principle of spectral chemical quantitative analysis and the solution method of multivariate linear equation, this paper pioneers the spectrum elimination method, and obtains a complete, high-precision, synchronous and noninvasive detection system for a variety of human blood components. The spectral elimination method applies the principle of elimination method in mathematics to the noninvasive quantitative analysis of human blood components by spectral method, reduces the influence of non-target components on the detection of target components, and improves the accuracy of noninvasive quantitative analysis of human blood components. To demonstrate the effectiveness of the method, taking the analysis of the contents of seven blood components (hemoglobin, red blood cell count, neutrophils, lymphocytes, monocytes, eosinophils and basophils) in blood as an example, fourteen models were established by two different methods. From the comparison of modeling results, it can be concluded that when the seven models established by spectral elimination method predict the corresponding seven components of all samples, the predicted correlation coefficients are more than 0.9500. The experimental results show that the spectral elimination method and non-invasive detection system proposed can predict the content of human blood components with high accuracy. This paper studies a high-precision, simultaneous and noninvasive quantitative analysis system of multiple human blood components for the first time, which not only makes great progress in the non-invasive chemical quantitative analysis of human blood components by spectroscopy, but also has great application value for clinical medical treatment and disease diagnosis.

Dual control system for comprehensive evaluation of quality consistency of herbal preparations

This study was undertaken to establish an integrated quality control method based on the fingerprint of different testing instruments and a four-dimensional antioxidant activity profile. Firstly, the comprehensive ratio quantitative fingerprint method was used to rapidly calculate the similarity of GC fingerprint, UV-vis quantized fingerprint, HPLC fingerprint and ATR-FT-IR quantized fingerprint. In addition, a comprehensive evaluation strategy was proposed using the variation coefficient weighting algorithm, incorporating four complementary inputs, to achieve highly accurate analysis. At the same time, multi-markers assay by monolinear method was used for the first time for the quantitative analysis of multiple components. Based on this, the fingerprint-efficacy relationship was investigated, and substances that might be biologically active and the wavelength range of their distribution were predicted using the online-HPLC-DPPH-FIA method in conjunction with a PLS model built from individual and combined data matrices. The results showed that all 20 batches of samples were within 1–3 grades, with good quality consistency. According to index E, the samples' overall antioxidant capacity was also highly correlated with the year of production. The four-dimensional antioxidant activity profile also led to the conclusion that variations in antioxidant characteristics were caused by differences in the concentration and volatility of particular bioactive chemicals. Finally, mid-level data fusion produced better classification results than a single technique, further demonstrating the synergistic effects that may occur when the four types of data were combined. This study demonstrated that the combination of GC, UV–vis, ATR-FT-IR and HPLC can be used for the consistency control of herbal preparations and can elucidate the potential in predicting the antioxidant capacity of herbal preparations.

A fully functional palmtop microchip electrophoresis analyzer with laser-induced fluorescence detection

Microchip electrophoresis (MCE) is an ideal tool for rapid on-site analysis of multiple components, therefore the demand for portable MCE devices is increasing. In this work, a fully functional palmtop MCE analyzer was built, integrating units of sample injection, laser-induced fluorescence (LIF) detection, high-voltage (HV), imaging and data acquisition. Frames were fabricated with 3D printing to encapsulate miniaturized components selected with balanced consideration on size, cost and performance. The LIF unit contains a laser diode as the excitation light source, a collimating lens used in the commercial laser diode as the focusing lens and an avalanche photodiode (APD) for the laser distance meter as the photoelectric converter. The sample injection was achieved by negative pressure induced pinching. The total cost of the device is approximately $600 with a size of 89 × 98 × 98 mm3 and a weight of 350 g. It can be powered by a laptop or a mobile power bank. The limit of detection (LOD) measured under a typical MCE condition for sodium fluorescein could be 110 pM. The applicability of the device was demonstrated with the detection of rhodamines and DNA fragments.

Comparative Analysis of the Phenolic Profile of Lycium barbarum L. Fruits from Different Regions in China.

Lycium barbarum L. (LB) fruits have high nutritive values and therapeutic effects. The aim of this study was to comprehensively evaluate the differences in phenolic composition of LB fruits from different geographical regions. Different methods of characterization and statistical analysis of data showed that different geographic sources of China could be significantly separated from each other. The highest total phenolic compound (TPC) content was observed in LB fruits from Ningxia (LBN), followed by those from Gansu (LBG) and Qinghai (LBQ). The Fourier transform infrared (FTIR) spectra of LB fruits revealed that LBQ had a peak at 2972 cm−1 whereas there was no similar peak in LBG and LBQ. A new HPLC method was established for the simultaneous determination of 8 phenolic compounds by quantitative analysis of multiple components by a single marker (QAMS), including 4 phenolic acids (chlorogenic acid, caffeic acid, 4-hydroxycinnamic acid, and ferulic acid), 1 coumarin (scopoletin), and 3 flavonoids (kaempferol-3-O-rutinoside, rutin, and narcissoside). It was showed that rutin was the most dominant phenolic compound in LBQ, although the average content of 4 phenolic acids was also high in LBQ, and scopoletin was the richest in LBG. UHPLC-Q-TOF-MS was used to qualitatively analyze the phenolics, which showed LBN was abundant in phenolic acids, LBQ was rich in flavonoids, and coumarins were the most plentiful in LBG. In conclusion, this study can provide references for the quality control and evaluation of phenolics in LB fruits and their by-products.