Simultaneous Quantitative Analysis Research Articles

Label-free scattering image-spectro-microscopy for single cell and extracellular vesicle analysis

Single-cell or particle characterization typically requires multiple technologies for simultaneous physical and chemical quantitative analysis. Despite the tremendous development of microscopy, multiparameter measurements are challenged by stains or labels, resolution limitations, and quantification of molecular contents. Here we report the Scattering Image-Spectro-Microscopy (SISM) with single-wavelength excitation that enables label-free, quantitative determination of both morphological and molecular contents of single cells or particles. The SISM offers simultaneous measurements of the side elastic light scattering image and the spontaneous Raman scattering spectra from label-free samples. Here elastic scattering imaging performs high-resolution imaging and refractive index quantification for the microscale and nanoscale particles by solving the challenging inverse scattering problem, while Raman scattering provides high molecular sensitivity. We demonstrate the applications of SISM for label-free differentiation of normal cells, clinical leukemic cells, leukemic cell lineage cells, and the nanoscale small extracellular vesicles (EVs) from the plasma of healthy and hepatoma cell-treated mice. Our SISM provides a label-free, multiscale, and quantitative description of single cells or small EVs, which is foreseen to have significant applications in many fields, such as cancer detection and nanoparticle analysis.

Liposomal doxorubicin and free doxorubicin in vivo quantitation method developed on CE-LIF and its application in pharmacokinetic analysis.

As a novel drug delivery system, liposomes were used to improve pharmacokinetics/pharmacodynamics (PK/PD) characters, minimize toxicity, and enhance drug-target selectivity. However, heterogeneity of drug releasing process and liposome itself challenged traditional pharmaceutical analytical techniques, especially in vivo pharmacokinetic studies. In this study, a novel liposomal doxorubicin (L-DOX) pharmacokinetic analysis strategy was developed with capillary electrophoresis coupled with laser-induced fluorescence (CE-LIF) detector. Thebackground electrolyte (BGE) system was composed of borate and sodium dodecyl sulfate (SDS), which was optimized to successfully achieve simultaneous online separation and quantitative analysis of free DOX and liposome-encapsulated DOX. The method was applied to the in vivo pharmacokinetic study of L-DOX in rats. The results showed that the concentration of total DOX (T-DOX) was gradually decreasing, while the concentration of L-DOX was relatively stable, with a concentration of 31.6±4.8 µg/mL within 24h. It was the first time to achieve liposomal drugs in vivo analysis with CE-LIF. CE-LIF was proved as potential rapidly real-time analytical methods for liposomal drugs in vivo occurrence monitoring.

Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil

Blended vegetable oils are highly prized by consumers for their comprehensive nutritional profile. Therefore, there is an urgent need for a rapid and accurate method to identify the true content of blended oils. This study combined Raman spectroscopy with three deep learning models (CNN-LSTM, improved AlexNet, and ResNet) to simultaneously quantify extra virgin olive oil (EVOO), soybean oil, and sunflower oil in olive blended oil. The results demonstrate that all three deep learning models exhibited superior predictive ability compared to traditional chemometric methods. Specifically, the CNN-LSTM model achieved a coefficient of determination (R2p) of over 0.995 for each oil in the quantitative analysis of three-component blended oils, with a mean square error of prediction (RMSEP) of less than 2%. This study presents a novel approach for the simultaneous quantitative analysis of multi-component blended oils, providing a rapid and accurate method for the identification of falsely labeled blended oils.

Simultaneous Quantification of 16 Bisphenol Analogues in Food Matrices.

Exposure to bisphenol analogues can occur in several ways throughout the food production chain, with their presence at higher concentrations representing a risk to human health. This study aimed to develop effective analytical methods to simultaneously quantify BPA and fifteen bisphenol analogues (i.e., bisphenol AF, bisphenol AP, bisphenol B, bisphenol BP, bisphenol C, bisphenol E, bisphenol F, bisphenol G, bisphenol M, bisphenol P, bisphenol PH, bisphenol S, bisphenol Z, bisphenol TMC, and tetramethyl bisphenol F) present in canned foods and beverages. Samples of foods and beverages available in the Swiss and EU markets (n = 22), including canned pineapples, ravioli, and beer, were prepared and analyzed using QuEChERS GC-MS. The quantification method was compared to a QuEChERS LC-MS/MS analysis. This allowed for the selective and efficient simultaneous quantitative analysis of bisphenol analogues. Quantities of these analogues were present in 20 of the 22 samples tested, with the most frequent analytes at higher concentrations: BPA and BPS were discovered in 78% and 48% of cases, respectively. The study demonstrates the robustness of QuEChERS GC-MS for determining low quantities of bisphenol analogues in canned foods. However, further studies are necessary to achieve full knowledge of the extent of bisphenol contamination in the food production chain and its associated toxicity.

Spectrophotometric thermodynamic analysis of methylene blue aggregation in water by chemometrics; what comes in the presence of different cyclodextrins?

Spectrophotometry has been utilized to characterize the thermodynamic/dynamic properties of self-aggregation of methylene blue (MB) in water, particularly while interacting with a modulator like different cyclodextrins (α-, β-, hydroxypropyl-β- (HP-β-), and γ-CDs). These systems comprise many interactions that make such chemical systems sophisticated. We developed a mathematical modeling-fitting analysis for the simultaneous quantitative analysis of thermodynamic parameters of chemical reactions, relying on the fitting algorithm. Through analyzing simulated photometric titration data, we demonstrate the simultaneous determination of thermodynamic parameters of the different guest/host interactions. This first has brought the need for the calculation of the visible-light absorption spectrum and the thermodynamic parameters for the pure dimerization system. Therefore, the multiwavelength spectral-mole ratio data of aqueous solutions of MB over a concentration range of 2.5 × 10−5 to 4.5 × 10−5 M while temperature is changing; or being titrated with CDs solutions at various temperatures were collected, augmented, and then have been fed to solid mathematical routines to determine the potential existence of dimeric aggregates. The results of thermodynamics indicated that the positions of the monomer/dimer equilibria do not alter by the presence of α-CD. The apparent dimerization was suppressed upon addition of β- or HP-β-CDs, while the addition of γ-CD enhanced the dimerization.

Identification and quantitative analysis of the chemical constituents of Gandouling tablets using ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry.

Gandouling tablets are used in a clinical agent for the treatment of hepatocellular degeneration; however, their chemical constituents have not been elucidated. Here, we screened and identified the chemical constituents of Gandouling tablets using ultra-high-performance liquid chromatography (UHPLC)-quadrupole time of flight/mass spectrometry. A method for the quality evaluation of Gandouling tablets was developed by combining the UHPLC fingerprints and the simultaneous quantitative analysis of multiple active ingredients. For fingerprint analysis, 20 shared peaks were identified to assess the similarities among the 10 batches of Gandouling tablets and the similarity was>0.9. The levels of nine representative active ingredients were simultaneously determined to ensure consistency in quality. A total of 99 chemical components were identified, including 18 alkaloids, 20 anthraquinones, 13 flavonoids, 11 phenolic acids, 9 polyphenols, 7 phenanthrenes, 5 sesquiterpenes, 3 curcuminoids, 2 lignans, 2 isoflavones, 2 dianthranones, and 7 other components. The retention times, molecular formulae, and secondary fragmentation information of these compounds were analyzed, and the cleavage pathways and characteristic fragments of some of the representative compounds were elucidated. This systematic analysis used to identify the chemical components of Gandouling tablets lays the foundation for its further quality control and research on their pharmacodynamic substances.

The Development and Validation of Simultaneous Quantitative Analysis Reversed-phase High-performance Liquid Chromatography for Sitagliptin Phosphate Monohydrate and Dapagliflozin Propanediol Monohydrate Fixed-dose Combination Dual-layered Tablet

Background: Sitagliptin phosphate monohydrate-dapagliflozin propanediol hydrate fixeddose combination (FDC) dual-layered tablet is used for type 2 diabetes treatment. Simultaneous quantitative analysis can shorten the analysis time of sitagliptin phosphate monohydrate-dapagliflozin propanediol monohydrate FDC dual-layered tablets and increase their efficiency. Objective: This study aimed to develop the simultaneous quantitative analysis for sitagliptin phosphate monohydrate-dapagliflozin propanediol monohydrate FDC dual-layered tablet, a type 2 diabetes treatment. Methods: Simultaneous quantitative analysis using the rapid and selective reversed-phase highperformance liquid chromatography (RP-HPLC) method was developed and validated using method validation. RP-HPLC analysis was conducted using an ultraviolent absorption spectrophotometer and a Zorbax C18 column (4.6 x 150 mm, 5 μm). The flow rate and injection volume were set to 1.5 mL min-1 and 20 μL, respectively. The wavelength was set at 205 nm. Results: The retention times of sitagliptin phosphate monohydrate and dapagliflozin propanediol monohydrate were 2.28 mins and 10.65 mins, respectively. The relative standard deviations of the system suitability for validation of simultaneous quantitative analysis were 0.03% for sitagliptin phosphate monohydrate and dapagliflozin propanediol monohydrate. The chromatogram confirmed that there was no peak interference between the two main components and between the main component and the excipients. In addition, It revealed a favorable linearity with correlation coefficients of 0.9999 in the concentration range of 20-120% compared to the standard solution. Conclusion: The developed simultaneous quantitative analysis shortened the analysis time and high efficiency of the sitagliptin phosphate monohydrate-dapagliflozin propanediol monohydrate FDC bilayer tablet. The validity of the analytical method was verified through accuracy and precision, detection and quantitation limits, and solution stability tests. In addition, it was thought that it would be helpful in developing an analytical method by referring to the simultaneous quantitative analysis method for developing other FDC dual-layered tablets.

Sandwich-type electrochemical aptasensor based on polydopamine-Au-metal ions as signal label and coralloid Au-conductive carbon architecture-modified electrode for the simultaneous detection of cardiac troponin I and myoglobin

The determination of cardiac troponin I (cTnI) and myoglobin (Myo) is increasingly significant considering their potential in acute myocardial infarction (AMI) diagnosis. Here, we constructed a sandwich-type electrochemical aptasensor for the simultaneous detection of cTnI and Myo. The polydopamine-Au-metal ions (PDA-Au-Pb2+/Cu2+) were synthesized and used as the labels of second aptamers (TrO6 and S232). The labels displayed different peaks at different potential sites, thus making the simultaneous detection method available. Meanwhile, the synthesized zirconium-based carbon architecture (CC) was well-conductive originating from the annealing process. After the modification of CC on the bare electrode (screen-printed carbon electrode, SPCE), the coralloid Au (CAu) was further decorated on the electrode by electro-deposition, thus forming CAu/CC/SPCE with high conductivity. Hence, the aptasensor proposed here showed admirable simultaneous quantitative analysis performance towards cTnI and Myo with wide linear ranges (1 μg/mL-0.01 pg/mL, 1 μg/mL-0.1 pg/mL) and low detection limits (3.8 fg/mL, 35 fg/mL). These results demonstrated that the aptasensor held the potential application in clinical diagnosis.

Development and Validation of Novel High-Performance Liquid Chromatography Method for Simultaneous Estimation of <i>p</i>-Cymene and Aloe-emodin

The objective of the present investigation was to develop a novel, accurate, precise, and linear High-Performance Liquid Chromatographic (HPLC) method for the simultaneous estimation of p-Cymene and aloe-emodin in the novel topical herbal formulation and validated as per ICH guidelines. In the current study, good chromatographic separation was achieved in isocratic mode using an HPLC C18 column (250mm × 4.6), 5 μm, and a mobile phase consisting of acetonitrile:water in the ratio of 80:20, at a flow rate of 1.0 mL/min and column temperature maintained at 25°C. The response obtained was monitored at 225 nm wavelength with a UV-Visible detector. The retention times of Aloe-emodin and p-Cymene were found to be 4.3 min and 9.0 min respectively. Linearity was established for both p-Cymene and aloe-emodin in the range of 10-90 µg/mL, respectively. For the method, % Recovery was found in the range of 99.67-100.51 % for p-Cymene and 98.68-100.4 % for aloe-emodin respectively. The LOD and LOQ were found to be 0.01 and 0.04 for p-Cymene and 0.12 and 0.36 for aloe-emodin respectively. This method can be successfully employed for simultaneous quantitative analysis of p-Cymene and Aloe-emodin in the novel topical herbal formulation.

Simultaneous quantitative chiral analysis of four isomers by ultraviolet photodissociation mass spectrometry and artificial neural network.

Although mass spectrometry (MS) has its unique advantages in speed, specificity and sensitivity, its application in quantitative chiral analysis aimed to determine the proportions of multiple chiral isomers is still a challenge. Herein, we present an artificial neural network (ANN) based approach for quantitatively analyzing multiple chiral isomers from their ultraviolet photodissociation mass spectra. Tripeptide of GYG and iodo-L-tyrosine have been applied as chiral references to fulfill the relative quantitative analysis of four chiral isomers of two dipeptides of L/D His L/D Ala and L/D Asp L/D Phe, respectively. The results show that the network can be well-trained with limited sets, and have a good performance in testing sets. This study shows the potential of the new method in rapid quantitative chiral analysis aimed at practical applications, with much room for improvement in the near future, including selecting better chiral references and improving machine learning methods.

Simultaneous quantitative analysis and in vitro anti-arthritic effects of five polyphenols from Terminalia chebula.

Background: The fruit of Terminalia chebula has been widely used for a thousand years for treating diarrhea, ulcers, and arthritic diseases in Asian countries. However, the active components of this Traditional Chinese medicine and their mechanisms remain unclear, necessitating further investigation. Objectives: To perform simultaneous quantitative analysis of five polyphenols in T. chebula and evaluate their anti-arthritic effects including antioxidant and anti-inflammatory activity in vitro. Materials and methods: Water, 50% water-ethanol, and pure ethanol were used as extract solvents. Quantitative analysis of gallic acid, corilagin, chebulanin, chebulagic acid, and ellagic acid in the three extracts was performed using high-performance liquid chromatography (HPLC). Antioxidant activity was assessed by the 2,2-diphenylpicrylhydrazyl (DPPH) radical-scavenging assay, and anti-inflammatory activity was evaluated by detecting interleukin (IL)-6 and IL-8 expression in IL-1β-stimulated MH7A cells. Results: The 50% water-ethanol solvent was the optimal solvent yielding the highest total polyphenol content, and the concentrations of chebulanin and chebulagic acid were much higher than those of gallic acid, corilagin, and ellagic acid in the extracts. The DPPH radical-scavenging assay showed that gallic acid and ellagic acid were the strongest antioxidative components, while the other three components showed comparable antioxidative activity. As for the anti-inflammatory effect, chebulanin and chebulagic acid significantly inhibited IL-6 and IL-8 expression at all three concentrations; corilagin and ellagic acid significantly inhibited IL-6 and IL-8 expression at high concentration; and gallic acid could not inhibit IL-8 expression and showed weak inhibition of IL-6 expression in IL-1β-stimulated MH7A cells. Principal component analysis indicated that chebulanin and chebulagic acid were the main components responsible for the anti-arthritic effects of T. chebula. Conclusion: Our findings highlight the potential anti-arthritic role of chebulanin and chebulagic acid from T. chebula.

Simultaneous quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium using surface-enhanced Raman spectroscopy coupled with partial least squares regression and artificial neural networks

Simultaneous detection of mixed bacteria accurately and sensitively is a major challenge in microbial quality control field. In this study, we proposed a label-free SERS technique coupled with partial least squares regression (PLSR) and artificial neural networks (ANNs) for quantitative analysis of Escherichia coli, Staphylococcus aureus and Salmonella typhimurium simultaneously. SERS-active and reproducible Raman spectra can be acquired directly upon the bacteria and Au@Ag@SiO2 nanoparticle composites on the surface of gold foil substrates. After applying different preprocessing models, SERS-PLSR and SERS-ANNs quantitative analysis models were developed to map SERS spectra of concentrations of the Escherichia coli, Staphylococcus aureus and Salmonella typhimurium, respectively. Both models achieved high prediction accuracy and low prediction error, while the performance of SERS-ANNs model in both quality of fit (R2 > 0.95) and accuracy of predictions (RMSE < 0.06) was superior to SERS-PLSR model. Therefore, it is feasible to develop simultaneous quantitative analysis of mixed pathogenic bacteria by proposed SERS methodology.

Integrated SERS-Vertical Flow Biosensor Enabling Multiplexed Quantitative Profiling of Serological Exosomal Proteins in Patients for Accurate Breast Cancer Subtyping.

Protein profiles of exosomes (EXOs) in clinical samples of cancer patients have become a promising diagnostic and therapeutic biomarker. However, simultaneous quantitative analysis of multiple exosomal proteins of interest remains challenging. To address the unmet need, we develop a paper-based surface-enhanced Raman spectroscopy (SERS)-vertical flow biosensor, named iREX (integrated Raman spectroscopic EXO) biosensor, for multiplexed quantitative profiling of exosomal proteins in clinical serum samples of patients. Utilizing this iREX biosensor, we are able to quantitatively profile MUC1, HER2 and CEA in EXO samples derived from various breast cancer cell subtypes. The results show discriminative expression profiles of the three exosomal proteins in these cell subtypes, which allows for accurate diagnosis and molecular subtyping of breast cancer. We further validate the clinical utility of the iREX biosensor for simultaneous quantitative analysis of MUC1, HER2 and CEA in patient's blood serums, thereby aiding in noninvasive breast cancer subtyping and longitudinal treatment monitoring. Our iREX biosensor integrating the SERS detection in a vertical flow diagnostic device offers great advantages of high sensitivity, molecular specificity, powerful multiplexing capability, and high diagnostic accuracy. We believe that the iREX biosensor could be a promising clinical tool for comprehensive analysis of exosomal proteins in clinical samples for personalized diagnosis and precise management of breast cancer.

Simultaneous Quantitative Analysis of Salmeterol and Fluticasone in Inhalation Spray Using HPLC and a Fast Spectrophotometric Technique Combined with a Time Series Neural Network and Multivariate Calibration Methods.

Chromatographic methods have been used for the simultaneous determination of salmeterol (SMT) and fluticasone (FLU). The methods take a lot of analysis time, need sample pre-treatment and large amounts of solvents, and are costly. The aim of this paper was to propose a simple, quick, and low-cost method for the determination of SMT and FLU using a time series neural network and multivariate calibration methods, including partial least-squares (PLS) and principal component regression (PCR). The simultaneous spectrophotometric determination of SMT and FLU in binary mixtures and anti-asthma spray was performed by applying multivariate calibration methods and an intelligent approach. The coefficient of determination (R2) of the time series neural network was obtained: 1 and 0.9997 for SMT and FLU, respectively. The mean recovery of PLS was found to be 99.29% and 99.84% for SMT and FLU, respectively. Also, the mean recovery related to the PCR method was 102.05% and 103.72% for SMT and FLU, respectively. The inhalation spray was assessed using HPLC and the results were compared with chemometrics methods via an analysis of variance (ANOVA) test. Intelligent and multivariate calibration methods were proposed. Simultaneous spectrophotometric determination of salmeterol and fluticasone was studied in an anti-asthma spray. HPLC as a reference method was performed and compared with chemometrics methods. Rapid, simple, low-cost, and accurate are the benefits of the proposed approaches.

Pentafluoropropionic Anhydride Derivatization and GC-MS Analysis of Histamine, Agmatine, Putrescine, and Spermidine: Effects of Solvents and Starting Column Temperature.

Gas chromatography-mass spectrometry (GC-MS) is useful for the quantitative determination of the polyamines spermidine (SPD) and putrescine (PUT) and of the biogenic amine agmatine (AGM) in biological samples after derivatization. This GC-MS method involves a two-step extraction with n-butanol and hydrochloric acid, derivatization with pentafluoropropionic anhydride (PFPA) in ethyl acetate, and extraction of the pentafluoropropionic (PFP) derivatives by toluene of SPD, PUT, and AGM. We wanted to extend this GC-MS method for the biogenic amine histamine (HA), but we faced serious problems that did not allow reliable quantitative analysis of HA. In the present work, we addressed this issue and investigated the derivatization of HA and the effects of toluene and ethyl acetate, two commonly used water-insoluble organic solvents in GC-MS, and oven temperature program. Derivatization of unlabelled HA (d0-HA) and deuterium-labelled HA (d4-HA) with PFPA in ethyl acetate (PFPA-EA, 1:4, v/v; 30 min, 65 °C) resulted in the formation of d0-HA-(PFP)2 and d4-HA-(PFP)2 derivatives. d4-HA and 13C4-SPD were used as internal standards for the amines after standardization. Considerable quantitative effects of toluene and ethyl acetate were observed. The starting GC column temperature was also found to influence considerably the GC-MS analysis of HA. Our study shows the simultaneous quantitative analysis of HA as HA-(PFP)2, AGM as AGM-(PFP)3, PUT as PUT-(PFP)2, and SPD as SPD-(PFP)3 derivatives requires the use of ethyl acetate for their extraction and injection into the GC-MS apparatus and a starting GC column temperature of 40 °C instead of 70 °C. The PFP derivatives of HA, AGM, PUT, and SPD were found to be stable in ethyl acetate for several hours at room temperature. Analytically satisfactory linearity, precision, and accuracy were observed for HA, AGM, PUT, and SPD in biologically relevant ranges (0 to 700 pmol). The limits of detection of AGM, PUT, and SPD were about two times lower in ethyl acetate compared to toluene (range, 1-22 fmol). The limits of detection were 1670 fmol for d0-HA and 557 fmol for d4-HA. Despite the improvements achieved in the study for HA, its analysis by GC-MS as a PFP derivative is challenging and less efficient than that of PUT, AGM, and SPD.

Accurate Quantitative Analysis of LIBS With Image Form Spectra by Using a Hybrid Deep Learning Model of a Convolutional Block Attention Module-Convolutional Neural Network-Long Short-Term Memory

With the development of miniaturized Laser-induced breakdown spectroscopy (LIBS) instruments, the desire for accuracy and stability of quantitative analysis of portable LIBS systems is growing. In this research, a hybrid deep learning model of a convolutional block attention module combined with a convolutional neural network and a long short-term memory (CBAM-CNN-LSTM) was for the first time used for quantitative analysis in a LIBS system with a portable spectrometer. Among them, the spectra collected by LIBS were in the form of the image, the CNN module was responsible for feature extraction of image spectra, mining deep features through the CBAM, and the simultaneous accurate quantitative analysis of Ca, Mg, Na and Ba was realized by the LSTM module. Meanwhile, a linear regression (LR), a CNN and a CNN-LSTM model were compared to the CBAM-CNN-LSTM model. The results showed that the performance of this model was much better than the LR model. More importantly, compared to CNN and CNN-LSTM, the average relative error of CBAM-CNN-LSTM was reduced by 80.5% and 68.1%, the average root mean square error was reduced by 56.7% and 53.4%, and the average stability was increased by 62.3% and 58.8%, respectively. In addition, the feature visualization results of CNN-LSTM and CBAM-CNN-LSTM displayed that CBAM can more effectively extract the features of characteristic peaks of corresponding elements and suppress the irrelative features. It indicated that the model can achieve an accurate and stable quantitative analysis of LIBS, which has the potential to be applied in the in-site analysis.

Simultaneous qualitative and quantitative analysis of flavonols in Kaempferia galangal L. and honey by machine learning-based fluorescence sensor array

Multiple flavonols with similar structures or chemical properties are often present in complex samples, which challenges real sample detection. A two-element based array (Al3+-CD@Cu and Mg2+-CD@Cu) was constructed for simultaneous identification of multiple flavonols. Flavonols obtained significant fluorescence emission enhancement after complexation with metal ions (Al3+ and Mg2+) and wrapping by cyclodextrin (CD) on the CD@Cu surface. This sensor array successfully classified multiple flavonols in the samples using the SVM classification algorithm. However, a critical problem of insufficient quantification capability exists in current sensor array research. The sensor array achieved the quantitative analysis of kaempferol, quercetin, and myricetin by leveraging machine learning regression algorithm.

A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy

A novel method based on GC–MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What’s more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.

Simultaneous quantitative analysis of 11 constituents in Viticis Fructus by HPLC-HRMS and HPLC-DAD combined with chemometric methods.

Viticis Fructus is the dried ripe fruit of Vitex trifolia L. (VTF) or V. trifolia subsp. litoralis Steenis (VTLF). Different botanical sources of the same herbal medicines may have different clinical efficacies, but few studies have reported the comparative identification of VTF and VTLF. To establish a high-performance liquid chromatography (HPLC) method for the simultaneous assay of 11 constituents in Viticis Fructus, to compare the chemical compositions of VTF and VTLF, and to identify chemical markers for the discrimination and quality evaluation of the two botanical origins of Viticis Fructus. An HPLC-diode array detection (DAD)-high-resolution mass spectrometry (HRMS) method was developed for the simultaneous separation and quantification of 11 constituents in 21 batches of Viticis Fructus samples from different sources in China. Moreover, chemometrics were performed to compare and discriminate VTF and VTLF samples. The results from 11 batches of VTF and 10 batches of VTLF were compared for 11 components, of which 3,4-dicaffeoylquinic acid and 3,5-dicaffeoylquinic acid were identified and quantified in Viticis Fructus for the first time. The quantitative analysis showed significantly higher chlorogenic acid and casticin contents in VTLF than in VTF, and the chemometric analysis indicated that chlorogenic acid and casticin were responsible for the significant differences between VTF and VTLF; these two compounds might be used as chemical markers to distinguish the two original plant sources of Viticis Fructus. The present work provides useful information for understanding the chemical differences between VTF and VTLF. This work also provides feasible methods for the quality evaluation and discrimination of herbal medicines originating from multiple botanical sources.

Simple dilated convolutional neural network for quantitative modeling based on near infrared spectroscopy techniques

A simple deep convolutional neural network architecture had been developed and applied to the establishment of a quantitative model based on near infrared spectroscopy techniques. The network architecture only contained general convolutional layers and dilated convolutional layers. The determination coefficient was used as a criterion to stop the training process of the neural network. Three data sets were used to test the performance of the neural network. The major results are (1) quantitative model can be established within one million iterations; (2) the quantitative model established on one spectrometer can be applied to other spectrometers of same manufacturer and even the same kind of spectrometer of different manufacturer; (3) simultaneous quantitative analysis of four components in grain samples. This study provided a new strategy to apply techniques of deep neural network in quantitative analysis.