Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders.

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

Comparative physiology and transcriptome response patterns in cold-tolerant and cold-sensitive varieties of Solanum melongena.

BACKGROUND: Climate change has led to severe cold events, adversely impacting global crop production. Eggplant (Solanum melongena L.), a significant economic crop, is highly susceptible to cold damage, affecting both yield and quality. Unraveling the molecular mechanisms governing cold resistance, including the identification of key genes and comprehensive transcriptional regulatory pathways, is crucial for developing new varieties with enhanced tolerance. RESULTS: In this study, we conducted a comparative analysis of leaf physiological indices and transcriptome sequencing results. The orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted peroxidase (POD) activity and soluble protein as crucial physiological indicators for both varieties. RNA-seq data analysis revealed that a total of 7024 and 6209 differentially expressed genes (DEGs) were identified from variety "A" and variety "B", respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of DEGs demonstrated that the significant roles of starch and sucrose metabolism, glutathione metabolism, terpenoid synthesis, and energy metabolism (sucrose and starch metabolism) were the key pathways in eggplant. Weighted gene co-expression network analysis (WGCNA) shown that the enrichment of numerous cold-responsive genes, pathways, and soluble proteins in the MEgrep60 modules. Core hub genes identified in the co-expression network included POD, membrane transporter-related gene MDR1, abscisic acid-related genes, growth factor enrichment gene DELLA, core components of the biological clock PRR7, and five transcription factors. Among these, the core transcription factor MYB demonstrated co-expression with signal transduction, plant hormone, biosynthesis, and metabolism-related genes, suggesting a pivotal role in the cold response network. CONCLUSION: This study integrates physiological indicators and transcriptomics to unveil the molecular mechanisms responsible for the differences in cold tolerance between the eggplant cold-tolerant variety "A" and the cold-sensitive variety "B". These mechanisms include modulation of reactive oxygen species (ROS), elevation in osmotic carbohydrate and free proline content, and the expression of terpenoid synthesis genes. This comprehensive understanding contributes valuable insights into the molecular underpinnings of cold stress tolerance, ultimately aiding in the improvement of crop cold tolerance.

GC-MS metabolomics of French lettuce (Lactuca Sativa L. var capitata) leaves exposed to bisphenol A via the hydroponic media.

INTRODUCTION: Bisphenol A (BPA), an organic compound used to produce polycarbonate plastics and epoxy resins, has become a ubiquitous contaminant due to its high-volume production and constant release to the environment. Plant metabolomics can trace the stress effects induced by environmental contaminants to the variation of specific metabolites, making it an alternative way to study pollutants toxicity to plants. Nevertheless, there is an important knowledge gap in metabolomics applications in this area. OBJECTIVE: Evaluate the influence of BPA in French lettuce (Lactuca Sativa L. var capitata) leaves metabolic profile by gas chromatography coupled to mass spectrometry (GC-MS) using a hydroponic system. METHODS: Lettuces were cultivated in the laboratory to minimize biological variation and were analyzed 55 days after sowing (considered the plant's adult stage). Hexanoic and methanolic extracts with and without derivatization were prepared for each sample and analyzed by GC-MS. RESULTS: The highest number of metabolites was obtained from the hexanoic extract, followed by the derivatized methanolic extract. Although no physical differences were observed between control and contaminated lettuce leaves, the multivariate analysis determined a statistically significant difference between their metabolic profiles. Pathway analysis of the most affected metabolites showed that galactose metabolism, starch and fructose metabolism and steroid biosynthesis were significantly affected by BPA exposure. CONCLUSIONS: The preparation of different extracts from the same sample permitted the determination of metabolites with different physicochemical properties. BPA alters the leaves energy and membrane metabolism, plant growth could be affected at higher concentrations and exposition times.

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.

Integrative Salt Selection and Formulation Optimization: Perspectives of Disproportionation and Microenvironmental pH Modulation.

We report a novel utilization of a pH modifier as a disproportionation retardant in a tablet formulation. The drug molecule of interest has significant bioavailability challenges that require solubility enhancement. In addition to limited salt/cocrystal options, disproportionation of the potential salt(s) was identified as a substantial risk. Using a combination of Raman spectroscopy with chemometrics and quantitative X-ray diffraction in specially designed stress testing, we investigated the disproportionation phenomena. The learnings and insight drawn from crystallography drove the selection of the maleate form as the target API. Inspired by the fumarate form's unique stability and solubility characteristics, we used fumaric acid as the microenvironmental pH modulator. Proof-of-concept experiments with high-risk (HCl) and moderate-risk (maleate) scenarios confirmed the synergistic advantage of fumaric acid, which interacts with the freebase released by disproportionation to form a more soluble species. The resultant hemifumarate helps maintain the solubility at an elevated level. This work demonstrates an innovative technique to mediate the solubility drop during the "parachute" phase of drug absorption using compendial excipients, and this approach can potentially serve as an effective risk-mitigating strategy for salt disproportionation.

Raman spectroscopy coupled with chemometrics for identification of adulteration and fraud in muscle foods: a review.

Muscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields.

Sex and blood group determination from hair using ATR-FTIR spectroscopy and chemometrics.

Examination of hair with its intact root is commonly used for DNA profiling of the donor. However, its use for gathering other types of information is less explored. Using attenuated total reflectance-Fourier transform infrared spectroscopy, the present study aims to explore other relevant aspects in a non-destructive manner for forensics. Determining the sex and blood group of human hair samples were the major goals of the study. Sex determination was accomplished by analyzing the differential vibrational intensities and stretching of various chemical groups associated with hair and its proteins. Statistical inference of spectral data was performed using chemometric algorithms such as PCA and PLS-DA. The PLS-DA model determined sex with 100% accuracy and blood grouping with an average accuracy of 95%. The present study is the first of its kind to determine sex and blood grouping from human scalp hair shafts, as far as the author knows. By acting as a preliminary screening test, this study could have significant implications for forensic analysis of crime scene samples. Human and synthetic hair were used in validation studies, resulting in 100% accuracy, specificity, and sensitivity, with 0% false positives and false negatives. The technique ATR FTIR spectroscopy could complement the currently used methods of hair analysis such as physical examination and mitochondrial or genomic DNA analysis.

A novel approach using ATR-FTIR spectroscopy and chemometric analysis to distinguish male and female human hair samples.

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.

Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety.

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

Forensic profiling of smokeless powders (SLPs) by gas chromatography-mass spectrometry (GC-MS): a systematic investigation into injector conditions and their effect on the characterisation of samples.

Smokeless powders (SLPs) are composed of a combination of thermolabile and non-thermolabile compounds. When analysed by GC-MS, injection conditions may therefore play a fundamental role on the characterisation of forensic samples. However, no systematic investigations have ever been carried out. This casts doubt on the optimal conditions that should be adopted in advanced profiling applications (e.g. class attribution and source association), especially when a traditional split/splitless (S/SL) injector is used. Herein, a study is reported that specifically focused on the evaluation of the liner type (Ltype) and inlet temperature (Tinj). Results showed that both could affect the exhaustiveness and repeatability of the observed chemical profiles, with Ltype being particularly sensitive despite typically not being clarified in published works. Perhaps as expected, degradation effects were observed for the most thermolabile compounds (e.g. nitroglycerin) at conditions maximising the heat transfer rates (Ltype = packed and Tinj ≥ 200 °C). However, these did not seem to be as influential as, perhaps, suggested in previous studies. Indeed, the harshest injection conditions in terms of heat transfer rate (Ltype = packed and Tinj = 260 °C) were found to lead to better performances (including better overall %RSDs and LODs) compared to the mildest ones. This suggested that implementing conditions minimising heat-induced breakdowns during injection was not necessarily a good strategy for comparison purposes. The reported findings represent a concrete step forward in the field, providing a robust body of data for the development of the next generation of SLP profiling methods.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning.

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

High-coverage characterization and discovery of molecular markers for quality control of natural fragrant plant extracts using UPLC-HRMS-based untargeted metabolomics.

The chemical components of natural fragrant plant extracts are of high complexity, and the strategies for quality control (QC) and further discovery of fragrance mechanisms still need to be further investigated. This study integrated the strategies and methods of untargeted metabolomics and chemometrics and statistical modeling to attain the goal. The techniques of reversed-phase and HILIC analysis of ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS) were simultaneously used to collect data in both positive and negative ion modes. The pattern analysis of fingerprints and discovery of characteristic molecular markers for QC analysis were comprehensively employed to reach in-depth analysis of the quality variation and discovery of differential molecules among natural fragrant plant extracts. The former uses fingerprint technique to analyze their overall similarities and differences, and the latter comprehensively discovers molecular substances characterizing the chemical characteristics of fragrant extracts with the help of metabolomics and univariate and multivariate methods. The findings are expected to be used as the molecular markers in product manufacturing, sales, and consumption to achieve accurate quality control and recognition of targeted molecules for potential quality monitoring using spectroscopy techniques. In this work, 27 natural fragrant extracts were applied as examples, and their chemical components were comprehensively analyzed with discovery of markers for quality control. After data integration, 1178 molecules were annotated, and 267 differential metabolite molecules with the values of variable importance in the projection (VIP) larger than 1.0 were found. The results show that the method proposed in this work is of great significance for high-coverage analysis, QC marker discovery, and aroma mechanism elucidation, which has potential applications in the areas of food, cosmetics, pharmaceuticals, tobacco, and others.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives.

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Volatile Organic Compounds from Ceratocystis cacaofunesta, a Causal Agent of Ceratocystis Wilt of Cacao.

Fungi of the genus Ceratocystis are aggressive tree pathogens that cause serious diseases in several crops around the world. Ceratocystis wilt disease caused by C. cacaofunesta has been shown to be responsible for severe reductions in cacao production. In this study, headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) was used in combination with chemometric analysis for monitoring volatile organic compounds (VOCs) released from C. cacaofunesta. Low-molecular-weight esters, alcohols, ketones, and sulphur compounds were identified in the liquid broth. Monitoring the volatile profile over five days of fungal growth revealed that the concentrations of alcohol and esters were inversely proportional. Acetate esters were responsible for the intense fruity aroma of the C. cacaofunesta culture produced within the first hours after fungal inoculation, which decreased over time, and are likely associated with the attraction of insect vectors to maintain the life cycle of the pathogen. PCA revealed that 3-methylbutyl acetate was the metabolite with the highest factor loading for the separation of the VOC samples after 4 h of fungal growth, whereas ethanol and 3-methylbutan-1-ol had the highest factor loadings after 96 and 120 h. 3-Methylbutan-1-ol is a phytotoxic compound that is likely associated with host cell death since C. cacaofunesta is a necrotrophic fungus. Fungal VOCs play important roles in natural habitats, regulating developmental processes and intra- and interkingdom interactions. This is the first report on the volatiles released by C. cacaofunesta.

Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications.

Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.

UPLC-quadrupole time-of-flight-tandem mass spectrometry combined with chemometrics and network pharmacology to differentiate Coreopsis tinctoria Nutt.

Coreopsis tinctoria Nutt. (C. tinctoria) is a traditional medicinal plant, primarily found in plateau areas with altitudes exceeding 3000 m. The efficacy of C. tinctoria appears to be intricately tied to its quality. However, there is a scarcity of studies focused on evaluating the quality of C. tinctoria from diverse geographical locations. In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry to analyze and identify the prevalent chemical components in 12 batches of C. tinctoria sourced from Xinjiang, Qinghai, Tibet, and Yunnan provinces in China. By using cluster analysis and discriminant analysis of partial least squares, we assessed the similarity and identified varying components in the 12 batches of C. tinctoria. Subsequently, their quality was further evaluated. Utilizing network pharmacology, we identified potential active components for the treatment of diabetes mellitus. The findings revealed the presence of 16 flavonoids, 3 phenylpropanes, 2 sugars, 2 amino acids, and 7 hydrocarbons in the analyzed samples. Through variable importance screening, 17 constituents were identified as quality difference markers. Marein and flavanomarein emerged as pivotal markers, crucial for distinguishing variations in C. tinctoria. In addition, network pharmacology predicted 187 targets for 9 common active components, including marein and flavanomarein. Simultaneously, 1747 targets related to diabetes mellitus were identified. The drug-component-disease target network comprised 91 nodes and 179 edges, encompassing 1 drug node, 9 component nodes, and 81 target nodes. In summary, marein and flavanomarein stand out as key biomarkers for assessing the quality of C. tinctoria, offering a scientific foundation for the quality evaluation of C. tinctoria Nutt.

Identification of Euphorbiae pekinensis Radix and its counterfeit and adulterated products based on DNA barcode, UPLC-Q-TOF-MS, UPLC fingerprint, and chemometrics.

Euphorbiae pekinensis Radix (EPR) is a traditional Chinese herb commonly used to treat edema, pleural effusion, and ascites. However, counterfeit and adulterated products often appear in the market because of the homonym phenomenon, similar appearance, and artificial forgery of Chinese herbs. This study comprehensively evaluated the quality of EPR using multiple methods. The DNA barcode technique was used to identify EPR, while the UPLC-Q-TOF-MS technique was utilized to analyze the chemical composition of EPR. A total of 15 tannin and phenolic acid components were identified. Furthermore, UPLC fingerprints of EPR and its common counterfeit products were established, and unsupervised and supervised pattern recognition models were developed using these fingerprints. The backpropagation artificial neural network and counter-propagation artificial neural network models accurately identified counterfeit and adulterated products, with a counterfeit ratio of more than 25%. Finally, the contents of the chemical markers 3,3'-di-O-methyl ellagic acid-4'-O-ß-D-glucopyranoside, ellagic acid, 3,3'-di-O-methyl ellagic acid-4'-O-ß-d-xylopyranoside, and 3,3'-di-O-methyl ellagic acid were determined to range from 0.05% to 0.11%, 1.95% to 8.52%, 0.27% to 0.86%, and 0.10% to 0.42%, respectively. This proposed strategy offers a general procedure for identifying Chinese herbs and distinguishing between counterfeit and adulterated products.

Effect of Noise Filtering in an Ultraviolet Detector of UHPLC on the Statistical Reliability of Chemometrically Evaluated Repeatability.

A noise filter, which is usually attached to a detector for chromatography, was applied for the improvement of a signal-to-noise ratio (S/N) on a chromatogram. The objective of this paper is to elucidate the effect of noise filtering in an UV detector of ultra HPLC (UHPLC) on the statistical reliability of chemometrically evaluated repeatability by the function of mutual information (FUMI) theory. To examine the statistical reliability of chemometrically evaluated repeatability in the UHPLC system associated with noise filtering, the standard deviation (SD) values of the area in baseline fluctuations with peak region k (s(k)) were obtained from six chromatograms with noise filtering. Further, the average of s(k) values (σ̂) was calculated from the s(k) values (n = 6) to be alternatively applied as the population SD. All s(k)/σ̂ values were within the 95% confidence intervals (CIs) at the freedom degree of 50, indicating the chemometrically estimated relative SD (RSD) of a peak area and RSD by repeated measurements of at least 50 times had equivalent reliability.