Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

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

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

HS-SPME-GC-MS and Chemometrics for the Quality Control and Clustering of Monovarietal Extra Virgin Olive Oil: A 3-Year Study on Terpenes and Pentene Dimers of Italian Cultivars.

Terpenes and pentene dimers are less studied volatile organic compounds (VOCs) but are associated with specific features of extra virgin olive oils (EVOOs). This study aimed to analyze mono- and sesquiterpenes and pentene dimers of Italian monovarietal EVOOs over 3 years (14 cultivars, 225 samples). A head space-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) method recently validated was used for terpene and pentene dimer quantitation. The quantitative data collected were used for both the characterization and clustering of the cultivars. Sesquiterpenes were the molecules that most characterized the different cultivars, ranging from 3.908 to 38.215 mg/kg; different groups of cultivars were characterized by different groups of sesquiterpenes. Pentene dimers (1.336 and 3.860 mg/kg) and monoterpenes (0.430 and 1.794 mg/kg) showed much lower contents and variability among cultivars. The application of Kruskal-Wallis test-PCA-LDA-HCA to the experimental data allowed defining 4 clusters of cultivars and building a predictive model to classify the samples (94.3% correct classification). The model was further tested on 33 EVOOs, correctly classifying 91% of them.

Identifying the geographical origin and processing technology of Moyao () on the basis of near-infrared spectroscopy combined with chemometrics.

OBJECTIVE: To evaluate the quality of Moyao (Myrrh) in the identification of the geographical origin and processing of the products. METHODS: Raw Moyao (Myrrh) and two kinds of Moyao (Myrrh) processed with vinegar from three countries were identified using near-infrared (NIR) spectroscopy combined with chemometric techniques. Principal component analysis (PCA) was used to reduce the dimensionality of the data and visualize the clustering of samples from different categories. A classical chemometric algorithm (PLS-DA) and two machine learning algorithms [K-nearest neighbor (KNN) and support vector machine] were used to conduct a classification analysis of the near-infrared spectra of the Moyao (Myrrh) samples, and their discriminative performance was evaluated. RESULTS: Based on the accuracy, precision, recall rate, and F1 value in each model, the results showed that the classical chemometric algorithm and the machine learning algorithm obtained positive results. In all of the chemometric analyses, the NIR spectrum of Moyao (Myrrh) preprocessed by standard normal variation or Multivariate scattering correction combined with KNN achieved the highest accuracy in identifying the geographical origins, and the accuracy of identifying the processing technology established by the KNN method after first-order derivative pretreatment was the best. The best accuracy of geographical origin discrimination and processing technology discrimination were 0.9853 and 0.9706 respectively. CONCLUSIONS: NIR spectroscopy combined with chemometric technology can be an important tool for tracking the origin and processing technology of Moyao (Myrrh) and can also provide a reference for evaluations of its quality and the clinical use.

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods.

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

[Analysis of chemical compositions in different parts of Isodon japonicus using UPLC-Q-TOF-MS technique combined with chemometrics].

In order to analyze the similarities and differences of chemical compositions between the roots and stems and leaves of Isodon japonicus(IJ), this study utilized UPLC-Q-TOF-MS technology to systematically characterize its chemical compositions, analyzed and identified the structure of its main compounds, and established a method for simultaneous determination of its content by refe-rence substance. A total of 34 major compounds in IJ, including 14 reference compounds, were identified or predicted online. Moreover, an UPLC-UV content determination method was developed for 11 compounds [danshensu, caffeic acid, vicenin-2,(1S,2S)-globoidnan B, rutin,(+)-rabdosiin,(-)-rabdosiin,(1S,2S)-rabdosiin, shimobashiric acid C, rosmarinic acid, and pedalitin]. The method exhibited excellent separation, stability, and repeatability, with a wide linear range(0.10-520.00 µg·mL~(-1)) and high linearity(R~2>0.999). The average recovery rates ranged from 94.72% to 104.2%. The principal component analysis(PCA) demonstrated a clear difference between the roots and stems and leaves of IJ, indicating good separation by cluster. Furthermore, the orthogonal partial least squares discriminant analysis(OPLS-DA) model was employed, and six main differentially identified compounds were identified: rosmarinic acid, shimobashiric acid C, epinodosin, pedalitin, rutin, and(1S,2S)-rabdosiin. In summary, this study established a strategy and method for distinguishing different parts of IJ, providing a valuable tool for quality control of IJ and a basis for the ratio-nal utilization and sustainable development of IJ.

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis.

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach.

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat. The NIRS data could not be resolved using Savitzky-Golay smoothing. Nevertheless, the application of a random forest and support vector machine successfully classified the samples with 100% accuracy. Quantification of adulteration using partial least squares (PLS), Lasso, Ridge, elastic Net, and RF regressions provided R2 higher than 0.96 and root mean square error <2.6. Coupling PLS with the Boruta algorithm produced the most reliable regression model (R2 = 1, RMSE = 0.0000). Finally, an online application was prepared to facilitate the determination of adulterants in the cocoa powder.

Formulation analysis of functional fragrance via polar-gradient extraction method and chemometrics pattern recognition.

In this study, characteristic components of 15 natural flavors was analyzed by the polar-gradient extraction (PGE) technique in combination with GC-MS and chemometrics pattern recognition. The obtained results were utilized for the traceability of 4 functional fragrance formulations. The optimal PGE system consisting of 5 different polar solvents, was developed based on similarity-intermiscibility theory. Four chemometrics pattern recognition models including PCA, HCA, PLS-DA, and OPLS-DA were constructed based on the characteristic component database constituting 15 natural flavors. These models were used to trace 4 functional fragrance formulations. The experimental results obtained were found to be satisfactory and accurate. The combination of PGE technique and chemometric pattern recognition methods provides theoretical guidance for the analysis of characteristic components of natural flavors and the traceability of functional fragrance formulations. This approach can be promoted in various fields such as food, traditional Chinese medicine, and cosmetics.

NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation.

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

Review on the Discovery of Water Absorbance Spectral Pattern in Aquaphotomics based on Chemometrics Analytical Tools.

Aquaphotomics, as a new discipline is a powerful tool for exploring the relationship between the structure of water and the function of matter by analyzing the interaction between water and light of various frequencies. However, chemometric tools, especially the Water Absorbance Spectral Pattern (WASP) determinations, are essential in this kind of data mining. In this review, different state-of-the-art chemometrics methods were introduced to determine the WASP of aqueous systems. We elucidate the methods used for identifying activated water bands in three aspects, namely: 1) improving spectral resolution; the complexity of water species in aqueous systems leads to a serious overlap of NIR spectral signals, therefore, we need to obtain reliable information hidden in spectra, 2) extracting spectral features; sometimes, certain spectral information cannot be revealed by simple data processing, it is necessary to extract deep data information, 3) overlapping peak separation; since the spectral signal is produced by multiple factors, overlapping peak separation can be used to facilitate the extraction of spectral components. The combined use of various methods can characterize the changes of different water species in the system with disturbance and can determine the WASP. WASPs of research systems vary from each other, and it is visually displayed in the form of the aquagram. As a new omics family member, aquaphotomics could be applied as a holistic marker in multidisciplinary fields.

Prediction of essential oil content in spearmint (Mentha spicata) via near-infrared hyperspectral imaging and chemometrics.

Spearmint (Mentha spicata L.) is grown for its essential oil (EO), which find use in food, beverage, fragrance and other industries. The current study explores the ability of near infrared hyperspectral imaging (HSI) (935 to 1720 nm) to predict, in a rapid, nondestructive manner, the essential oil content of dried spearmint (0.2 to 2.6% EO). Spectral values of spearmint samples varied considerably with spatial coordinates, and so the use of averaging the spectral values of a surface scan was warranted. Data preprocessing was done with Multiplicative Scatter Correction (MSC) or Standard Normal Variate (SNV). Selection of spectral input variables was done with Least Absolute Shrinkage and Selection Operator (LASSO), Principal Component Analysis (PCA) or Partial Least Squares (PLS). Regression was executed with linear regression (LASSO, PLS regression, PCA regression), Support Vector Machine (SVM) regression, and Multilayer Perceptron (MLP). The best prediction of EO concentration was achieved with the combination of MSC or SNV preprocessing, PLS dimension reduction, and MLP regression (1 hidden layer with 6 nodes), achieving a good prediction with a ratio of performance to deviation (RPD) of 2.84 ± 0.07, an R2 of prediction of 0.863 ± 0.008, and a RMSE of prediction of 0.219 ± 0.005% EO. These results show that NIR-HSI is a viable method for rapid, nondestructive analysis of EO concentration. Future work should explore the use of NIR in the visible spectrum, the use of HSI for determining EO in other plant materials and the potential of HSI to determine individual compounds in these solid plant/food matrices.

Combination of RT-LAMP and fluorescence spectroscopy using chemometric techniques for an ultra-sensitive and rapid alternative for the detection of SARS-CoV-2.

The increased spread of COVID-19 caused by SARS-CoV-2 has made it necessary to develop more efficient, fast, accurate, specific, sensitive and easy-to-use detection platforms to overcome the disadvantages of gold standard methods (RT-qPCR). Here an approach was developed for the detection of the SARS-CoV-2 virus using the loop-mediated isothermal amplification (LAMP) technique for SARS-CoV-2 RNA target amplification in samples of nasopharyngeal swabs. The discrimination between positive and negative SARS-CoV-2 samples was achieved by using fluorescence spectra generated by the excitation of the LAMP's DNA intercalator dye at λ497 nm in a fluorescence spectrophotometer and chemometric tools. Exploratory analysis of the 83 sample spectra using principal component analysis (PCA) indicated a trend in differentiation between positive and negative samples resulting from the peak emission of the fluorescent dye. The classification was performed by partial least squares discriminant analysis (PLS-DA) achieving a sensitivity, a specificity and an accuracy of 100%, 95% and 89%, respectively for the discrimination between negative and positive samples from 1.58 to 0.25 ng L-1 after LAMP amplification. Therefore, this study indicates that the use of the LAMP technique in fluorescence spectroscopy may offer a fast (<1 hour), sensitive and low-cost method.

Digital image colorimetry in combination with chemometrics for the detection of carbaryl based on the peroxidase-like activity of nanoporphyrins and the etching process of gold nanoparticles.

Carbaryl is a typical carbamate pesticide that plays an essential role in agricultural production, but its residues cause serious harm to the environment and human health. Here, we developed a polychromatic colorimetric sensor based on ZnTPyP-DTAB peroxidase activity and gold nano-bipyramids (Au NBPs) etching to detect carbaryl. ZnTPyP-DTAB catalyzes the decomposition of H2O2 to hydroxyl radicals, and Au NBPs are etched. The coordination of zinc and nitrogen in nanometer porphyrins was affected by the steric effects of carbaryl, which resulted in decreased activity of ZnTPyP-DTAB peroxidase. The detection limit of carbaryl was 0.26 mg/kg. The recoveries of carbaryl in reaal sample ranged from 91 % to 107% (RSD ≤ 0.7%). The sensor platform displayed a series of high-resolution multicolor variations of rainbow colors within the above concentration range. The rich color variation facilitates the acquisition of digital images. RGB value transformation combined with partial least squares regression model can accurately and quantitatively detect carbaryl in vegetables, fruits and Chinese medicinal materials.

Elemental Chemometrics as Tools to Depict Stalked Barnacle (Pollicipes pollicipes) Harvest Locations and Food Safety.

The stalked barnacle Pollicipes pollicipes is an abundant species on the very exposed rocky shore habitats of the Spanish and Portuguese coasts, constituting also an important economical resource, as a seafood item with high commercial value. Twenty-four elements were measured by untargeted total reflection X-ray fluorescence spectroscopy (TXRF) in the edible peduncle of stalked barnacles sampled in six sites along the Portuguese western coast, comprising a total of 90 individuals. The elemental profile of 90 individuals originated from several geographical sites (N = 15 per site), were analysed using several chemometric multivariate approaches (variable in importance partial least square discriminant analysis (VIP-PLS-DA), stepwise linear discriminant analysis (S-LDA), linear discriminant analysis (LDA), random forests (RF) and canonical analysis of principal components (CAP)), to evaluate the ability of each approach to trace the geographical origin of the animals collected. As a suspension feeder, this species introduces a high degree of background noise, leading to a comparatively lower classification of the chemometric approaches based on the complete elemental profile of the peduncle (canonical analysis of principal components and linear discriminant analysis). The application of variable selection approaches such as the VIP-PLS-DA and S-LDA significantly increased the classification accuracy (77.8% and 84.4%, respectively) of the samples according to their harvesting area, while reducing the number of elements needed for this classification, and thus the background noise. Moreover, the selected elements are similar to those selected by other random and non-random approaches, reinforcing the reliability of this selection. This untargeted analytical procedure also allowed to depict the degree of risk, in terms of human consumption of these animals, highlighting the geographical areas where these delicacies presented lower values for critical elements compared to the standard thresholds for human consumption.

Elucidating the Color of Rosé Wines Using Polyphenol-Targeted Metabolomics.

The color of rosé wines is extremely diverse and a key element in their marketing. It is due to the presence of anthocyanins and of additional pigments derived from them and from other wine constituents. To explore the pigment composition and determine its links with color, 268 commercial rosé wines were analysed. The concentration of 125 polyphenolic compounds was determined by a targeted metabolomics approach using ultra high-performance liquid chromatography coupled to triple quadrupole mass spectrometry (UHPLC-QqQ-MS) analysis in the Multiple Reaction Monitoring (MRM) mode and the color characterised by spectrophotometry and CieLab parameters. Chemometrics analysis of the composition and color data showed that although color intensity is primarily determined by polyphenol extraction (especially anthocyanins and flavanols) from the grapes, different color styles correspond to different pigment compositions. The salmon shade of light rosé wines is mostly due to pyranoanthocyanin pigments, resulting from reactions of anthocyanins with phenolic acids and pyruvic acid, a yeast metabolite. Redness of intermediate color wines is related to anthocyanins and carboxypoyranoanthocyanins and that of dark rosé wines to products of anthocyanin reactions with flavanols while yellowness of these wines is associated to oxidation.

Microwave-Assisted Extraction Coupled to HPLC-UV Combined with Chemometrics for the Determination of Bioactive Compounds in Pistachio Nuts and the Guarantee of Quality and Authenticity.

Two novel microwave-assisted extraction (MAE) methods were developed for the isolation of phenols and tocopherols from pistachio nuts. The extracts were analyzed by reversed-phase high-pressure liquid chromatography coupled with a UV detector (RP-HPLC-UV). In total, eighteen pistachio samples, originating from Greece and Turkey, were analyzed and thirteen phenolic compounds, as well as α-tocopherol, (ß + γ)-tocopherol, and δ-tocopherol, were identified. The analytical methods were validated and presented good linearity (r2 > 0.990) and a high recovery rate over the range of 82.4 to 95.3% for phenols, and 93.1 to 96.4% for tocopherols. Repeatablility was calculated over the range 1.8-5.8%RSD for intra-day experiments, and reproducibility over the range 3.2-9.4%RSD for inter-day experiments, respectively. Principal component analysis (PCA) was employed to analyze the differences between the concentrations of the bioactive compounds with respect to geographical origin, while agglomerative hierarchical clustering (AHC) was used to cluster the samples based on their similarity and according to the geographical origin.

Comparison of chemometric assisted targeted and untargeted approaches for the prediction of radical scavenging activity of ylang-ylang essential oils.

Essential oils have been used for centuries for their preservative properties. An example is ylang-ylang Cananga odorata [Lam.] Hook. f. & Thomson essential oil, which exists in four different distillation grades, where the fraction with the longest distillation time has the highest radical scavenging activity (RSA). Gas chromatography mass spectrometry (GC-MS) followed by multivariate statistical analysis is a powerful approach for determination of RSA. Herein the performance of such multivariate statistical analysis using three data sets derived from gas chromatography mass spectrometry (GC-MS) analysis, is compared to that achieved using two direct and fast spectroscopic techniques, for the prediction of RSA using partial least squares (PLS) regression analysis. The three GC-MS data sets were, 'full chemical composition', 'total chromatogram average mass spectra (TCAMS)' and 'segment average mass spectra (SAMS)', whilst two spectroscopic techniques, namely attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and Raman spectroscopy, provided the spectroscopic data sets for comparison. PLS models created using ATR-FTIR and 'full chemical composition' data sets provided the lowest relative error of prediction (REP) and mean error of prediction (MEP) in validation, whilst in independent test sets, the PLS models created using ATR-FTIR and SAMS data sets delivered the lowest REP and MEP. The three GC-MS derived data sets were further compared for value in determination of compounds contributing to the RSA. PLS regression analysis of the full chemical composition data set revealed that germacrene D and (E,E)-α-farnesene were the major contributors to the RSA, whilst average mass spectrum based data sets, TCAMS and SAMS, also highlighted eugenol as another contributor to the RSA.

Characteristic profiling of Aconiti Lateralis Radix for distinguishing it from compatible herbal pair using UPLC-Q-TOF-MS coupled with chemometrics.

A method combining ultra-high-performance liquid chromatograph/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and chemometrics was established to evaluate the differences in chemical composition between Aconiti Lateralis Radix (Fuzi in Chinese) before and after combination with Glycyrrhizae Radix et Rhizoma (Gancao in Chinese). UPLC-Q-TOF-MS was used to characterize the chemical components before and after the combination of Fuzi with Gancao, and genetic algorithm selection variables were applied to extract important variables. Partial least square discriminant analysis was used to verify the reliability of the variables obtained by genetic algorithm selection in differentiating Fuzi and combinations with Gancao, and nine potential chemical markers were obtained. The changes in content of chemical markers in Fuzi before and after combination were visualized using a heat map and hierarchical cluster analysis. Based on the chemical markers, characteristic profiling of UPLC-Q-TOF-MS data was developed, then unsupervised principal components analysis and a supervised counter-propagation artificial neural network were used to validate the characteristic profiling approach and showed that it performed well in differentiating between Fuzi and combinations with Gancao.

Investigation on the Q-markers of Bushen Huoxue Prescriptions for DR treatment based on chemometric methods and spectrum-effect relationship.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic retinopathy (DR) is a kind of complex complication of late diabetes mellitus with high incidence and risk of blindness. Bushen Huoxue Prescription (BHP), which consists of Rehmanniae radix (RR), Salviae miltiorrhizae radix et rhizoma (SMRR), Ginseng radix et rhizome (GRR) and Puerariae lobatae radix (PLR), has an active effect on the treatment of DR. However, the quality markers (Q-markers) of BHP are not entirely clear. PURPOSE: This study aimed to screen the Q-markers of BHP for DR treatment based on the establishment of spectrum-effect relationship and verified experiment. MATERIALS AND METHODS: In this study, 12 BHP samples (S1-S12) for fingerprint analysis and pharmacological evaluation were prepared according to a four-factor and twelve-level uniform design. High performance liquid chromatography-ultraviolet detector-evaporative light scattering detector (HPLC-UV-ELSD) was employed to analyze the fingerprint on the basis of the characteristics of BHP components. The evaluation of sample similarity was carried out by similarity analysis (SA) and hierarchical cluster analysis (HCA). The pharmacological indicators, including expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) in the retina of Sprague Dawley (SD) rats induced by streptozotocin (STZ), were detected by enzyme-linked immunosorbent assay (ELISA). Besides, the spectrum-effect relationship between common peaks of fingerprints and the pharmacological results was investigated by partial least squares regression (PLSR) and canonical correlation analysis (CCA). The results of spectrum-effect relationship were verified by the expression of VEGF and HIF-1α on primary culture retinal Müller cells induced by hyperglycemia and hypoxia. RESULTS: In the HPLC-UV-ELSD fingerprint, 23 common peaks in UV and 14 common peaks in ELSD were identified. The pharmacological results indicated that the expression of VEGF and HIF-1α in the retina of SD rats was inhibited by 12 BHP samples to varying degrees compared with the model group. Based on SA and heatmap of HCA, S4 and S8 were clearly distinguished from other samples. The results of PLSR and CCA revealed that the contents of puerarin, daidzin, salvianolic acid B and ginsenoside Rb1 were inversely correlated with the expression of VEGF and HIF-1α. Hence, the four compounds may be the main active components to prevent and treat DR. The results of intervention on primary culture retinal Müller cells showed that puerarin, daidzin, salvianolic acid B, and ginsenoside Rb1 can significantly inhibit the expression of VEGF and HIF-1α. CONCLUSIONS: The spectrum-effect relationship of BHP was successfully established, and the Q-markers of BHP for the prevention and treatment of DR were preliminarily confirmed. It provides a feasible method for the research of quality control.