Chemometrics tools used in analytical chemistry: An overview

Spectrophotometric determination of celecoxib and tramadol in the new approved formulated dosage form using principle component regression assistive model

Celecoxib and tramadol have been combined in a novel FDA-approved medication to address acute pain disorders requiring opioid treatment when other analgesics proved either intolerable or ineffective. The absorbance spectra of celecoxib and tramadol exhibit significant overlap, posing challenges for their individual quantification. This study introduces a spectrophotometric quantification approach for celecoxib and tramadol using a principle component regression assistive model to assist resolving the overlapped spectra and quantifying both drugs in their binary mixture. The model was constructed by establishing calibration and validation sets for the celecoxib and tramadol mixture, employing a five-level, two-factor experimental design, resulting in 25 samples. Spectral data from these mixtures were measured and preprocessed to eliminate noise in the 200-210 nm range and zero absorbance values in the 290-400 nm range. Consequently, the dataset was streamlined to 81 variables. The predicted concentrations were compared with the known concentrations of celecoxib and tramadol, and the errors in the predictions were evidenced calculating root mean square error of cross-validation and root mean square error of prediction. Validation results demonstrate the efficacy of the models in predicting outcomes; recovery rates approaching 100 % are demonstrated with relative root mean square error of prediction (RRMSEP) values of 0.052 and 0.164 for tramadol and celecoxib, respectively. The selectivity was further evaluated by quantifying celecoxib and tramadol in the presence of potentially interfering drugs. The model demonstrated success in quantifying celecoxib and tramadol in laboratory-prepared tablets, producing metrics consistent with those reported in previously established spectrophotometric methods.

Ratiometric fluorescence sensing and discrimination of tetracycline analogs by using coumarin-embedded Eu-MOF nanosensor

As widely used antibiotics, tetracycline residues exist in food and environmental media, which pose certain hidden dangers and negative effects on public health. Therefore, the sensing and discrimination of tetracycline analogs (TCs) have great significance for improving food safety and preventing environmental pollution. Herein, a 7-hydroxycoumarin-3-carboxylic acid-embedded Eu-MOF (HC@Eu-MOF) material was constructed and then developed for the detection of TCs. Upon addition of TCs, the synthesized sensor displays opposite fluorescence changes at two different wavelengths due to the simultaneous presence of the inner filter effect (IFE) and the antenna effect (AE), and achieves a stable ratio signal response within 90 s. In addition, six important tetracycline analogs, including chlortetracycline (CTC), oxytetracycline (OTC), tetracycline (TC), metacycline (MC), doxycycline (DC) and demeclocycline (DMC) can be discriminated with 100 % accuracy through the principal component analysis even in extremely complicated mixtures. Further, a smartphone-assisted portable device was applied for visual sensing of TCs. The as-developed platform possessed the characteristics of simple synthesis, fast response, high sensitivity, and high stability, which further lays a further foundation for the on-site visual detection and discrimination of TCs in real samples.

Quantification of Lanthanides on a PMMA Microfluidic Device with Three Optical Pathlengths Using PCR of UV-Visible, NIR, and Raman Spectroscopy

Microfluidic devices (MFDs) offer customizable, low-cost, and low-waste platforms for performing chemical analyses. Optical spectroscopy techniques provide nondestructive monitoring of small sample volumes within microfluidic channels. Optical spectroscopy can probe speciation, oxidation state, and concentration of analytes as well as detect counterions and provide information about matrix composition. Here, ultraviolet-visible (UV-vis) absorbance, near-infrared (NIR) absorbance, and Raman spectroscopy are utilized on a custom poly(methyl methacrylate) (PMMA) MFD for the detection of three lanthanide nitrates in solution. Absorbance spectroscopies are conducted across three pathlengths using three portions of a contiguous channel within the MFD. Univariate and chemometric multivariate modeling, specifically Beer's law regression and principal component regression (PCR), respectively, are utilized to quantify the three lanthanides and the nitrate counterion. Models are composed of spectra from one or multiple pathlengths. Models are also constructed from multiblock spectra composed of UV-vis, NIR, and Raman spectra at one or multiple pathlengths. Root-mean-square errors (RMSE), limit of detection (LOD), and residual predictive deviation (RPD) values are compared for univariate, multivariate, multi-pathlength, and multiblock models. Univariate modeling produces acceptable results for analytes with a simple signal, such as samarium cations, producing an LOD of 5.49 mM. Multivariate and multiblock models produce enhanced quantification for analytes that experience spectral overlap and interfering nonanalyte signals, such as holmium, which had an LOD reduction from 7.21 mM for the univariate model down to 3.96 mM for the multiblock model. Multi-pathlength models are developed that maintain model errors in line with single-pathlength models. Multi-pathlength models have RPDs from 9.18 to 46.4, while incorporating absorbance spectra collected at optical paths of up to 10-fold difference in length.

Mapping Advantages and Challenges in Analytical Development for Fixed Dose Combination Products, a Review

Formulations containing more than one active ingredient are increasingly gaining popularity due to advantages with regard to patient convenience as well as reduced cost of production, packaging, and transportation. Such fixed-dose combinations (FDCs) demand for enhanced analytical methodologies and tools to efficiently achieve quality control of these complex products as compared to the conventional products containing only one active constituent. Highly efficient analytical methods can measure multiple constituents at once, improving their quality control. This review article discusses the challenges in the development of such methods due to the similarities or differences in the chemical identity of the participating drug molecules in an FDC. The latest developments in multiple analyte determination using various analytical techniques (HPLC, LC-MS, NMR, IR, powder XRD and DSC) are discussed, with a focus on special considerations in each case. The article discusses challenges with sample preparation of complex FDC products, and the use of Chemometrics and Quality by Design to develop efficient analytical methods. Lastly, an equation-based approach is proposed and demonstrated to arrive at a parameter referred to as "percentage efficiency gain" that would be useful in directly accessing the relevance and commercial benefits of a simultaneous method vis-a-vis separate methods for individual components.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.

1H-NMR Metabolomic Study of the Mushroom Pleurotus djamor for the Identification of Nematocidal Compounds

Due to the increasing populations of anthelmintic-resistant gastrointestinal nematodes and as a consequence of the adverse effects of synthetic drugs, this study focuses on the search for secondary metabolites with nematocidal activity from the edible mushroom Pleurotus djamor using The proton nuclear magnetic resonance (1H-NMR) metabolomics. The highest activity was shown by the ethyl acetate fractions of mycelium (EC50 290.8 µg/mL) and basidiomes (EC50 282.7 µg/mL). Principal component analysis (PCA) and hierarchical data analysis (HCA) of the 1H-NMR metabolic profiles data showed that the ethanolic extracts, the ethyl acetate, butanol, and water fractions from mycelium have different metabolic profiles than those from basidiomes, while low polarity (hexane) fractions from both stages of fungal development show similar profiles. Orthogonal partial least squares discriminant analysis (OPLS-DA) allowed the identification of signals in the 1H-NMR metabolic profile associated with nematocidal activity. The signals yielded via OPLS-DA and bidimensional NMR analysis allowed the identification of uracil as a component in the ethyl acetate fraction from basidiomes, with an EC50 of 237.7 µg/mL. The results obtained showed that chemometric analyses of the 1H-NMR metabolic profiles represent a viable strategy for the identification of bioactive compounds from samples with complex chemical profiles.

Current Applications of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) in Pharmaceutical Analysis: Review

The present review encompasses various applications of multivariate curve resolution- alternating least squares (MCR-ALS) as a promising data handling, which is issued by analytical techniques in pharmaceutics. It involves different sections starting from a concise theory of MCR-ALS and four detailed applications in drugs analysis. Dissolution, stability, polymorphism, and quantification are the main four detailed applications. The data generated by analytical techniques associated with MCR-ALS deals accurately with different challenges compared to other chemometric tools. For each reviewed purpose, it was explained how MCR-ALS was applied and detailed information was given. Different approaches were introduced to overcome challenges that limit the use of MCR-ALS efficiently in pharmaceutical mixture were also discussed.

Determination of per- and polyfluoroalkyl compounds in paper recycling grades using ultra-high-performance liquid chromatography-high-resolution mass spectrometry

Globally, per- and polyfluoroalkyl substances (PFAS)-related research on paper products has focused on food packaging with less consideration on the presence of PFAS at different stages of the paper recycling chain. This study analysed the prevalence of PFAS in paper grades used for the manufacture of recycled paperboard. The presence of PFAS was attributed to the use of PFAS-containing additives, consumer usage, exposure to packed goods as well as contamination during mingling, sorting, collection, and recovery of paper recycling material. Q Orbitrap mass spectrometry was used to analyse the paper samples after accelerated solvent extraction and solid phase extraction. The distribution and possible propagation of 22 PFAS were determined in pre-consumer, retail and post-consumer paper products. Post-consumer samples had the highest combined average concentration (ΣPFAS) at 213 ng/g, while the ΣPFAS in retail (159 ng/g) and pre-consumer samples (121 ng/g) was detected at lower concentrations. This study showed that waste collection and recycling protocols may influence PFAS propagation and that measures must be developed to minimise and possibly eliminate exposure opportunities.

Effects of sample pre-treatments on the analysis of liquid organic manures by visible and near-infrared spectrometry

Proper application of a fertilizer requires precise knowledge of its nutrient composition. In the case of liquid organic manures (LOM), this information is often lacking due to heterogeneous nature of these fertilizers. Published "book values" of nutrient contents present the average from a wide range of possible nutrient characteristics, but usually differ considerably from the concentration in a particular manure. Thus, chemical analyses are recommended before applying the specific LOM. Unfortunately, this is usually too costly and time-intensive in practical farming. On-farm analysis by optical spectrometry in the visible and near-infrared (Vis-NIR) range is considered as an efficient alternative. However, calibration of Vis-NIR spectrometry for LOM is challenging as shown in many studies. One reason is LOMs' tendency to rapidly segregate into a fuzzy continuum with liquid and solid properties. By separating LOM into well-defined liquid and solid phases and measuring them separately, calibration of Vis-NIR spectrometry might be improved. In this study, the effects of four sample pre-treatment techniques on the prediction accuracy of macronutrients (N, P, K, Mg, Ca, S), micronutrients (B, Mn, Fe, Cu, Zn), dry matter and pH of LOM using visible and near infrared spectrometry were comprehensively investigated. The concentrations were referred either to wet basis or to dry matter basis. For the study, a total of 163 samples, separated in two similar LOM sets (pig, cattle, digestates), were either dried, filtered, or centrifuged and always compared to non-treated samples. The experiments demonstrate that in comparison to raw samples (Ø r2 = 0.85) neither filtering (Ø r2 = 0.76 for filtrates and Ø r2 = 0.71 for filter residues), centrifugation (Ø r2 = 0.59 for supernatants and Ø r2 = 0.79 for pellets), nor drying (Ø r2 = 0.74) revealed to be a helpful preparation step significantly improving prediction results, independent from referring to wet or dry basis concentrations.

Adulteration Detection and Quantification in Olive Oil Using Excitation-Emission Matrix Fluorescence Spectroscopy and Chemometrics

This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.

Towards the rapid detection of haze-forming proteins

Protein haze in white wine can be a serious quality defect because consumers perceive hazy wines as "spoiled". Unfortunately, a specific method for the detection, or selective treatment, of such proteins in affected wines does not exist. Herein we investigate on the development of an easy-to-use sensor device that allows detection of haze-forming proteins (HFPs). Such a device is expected to overcome the limitations of the "heat test" currently used to assess the protein content in wine and the amount of bentonite needed to remove such proteins. To this aim, three different approaches were explored. Firstly, an impedimetric immunosensor against chitinases was developed and its performance assessed. Secondly, the exploitation of the dual role of HFPs as biorecognition element and analyte to develop an impedimetric biosensor was evaluated, in what can be considered a very unique strategy, representing a new paradigm in biosensing. Lastly, Fourier transform infrared (FT-IR) spectra were collected for various wine samples and chemometric tools such as discrete wavelet transform (DWT) and artificial neural networks (ANNs) were used to achieve the quantification of HFPs. Detection of HFPs at the μg/L level was achieved with both impedimetric biosensors, whereas the FT-IR-based approach allowed their quantification at the mg/L level in wine samples directly. The sensitivity of the developed methods may enable the rapid assessment of wine protein content.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

An improved successive projections algorithm version to variable selection in multiple linear regression

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review

Low-field nuclear magnetic resonance (NMR) has three general modalities: spectroscopy, imaging, and relaxometry. In the last twelve years, the modality of spectroscopy, also known as benchtop NMR, compact NMR, or just low-field NMR, has undergone instrumental development due to new permanent magnetic materials and design. As a result, benchtop NMR has emerged as a powerful analytical tool for use in process analytical control (PAC). Nevertheless, the successful application of NMR devices as an analytical tool in several areas is intrinsically linked to its coupling with different chemometric methods. This review focuses on the evolution of benchtop NMR and chemometrics in chemical analysis, including applications in fuels, foods, pharmaceuticals, biochemicals, drugs, metabolomics, and polymers. The review also presents different low-resolution NMR methods for spectrum acquisition and chemometric techniques for calibration, classification, discrimination, data fusion, calibration transfer, multi-block and multi-way.

A review on matcha: Chemical composition, health benefits, with insights on its quality control by applying chemometrics and multi-omics

This review discussed the origin, manufacturing process, chemical composition, factors affecting quality and health benefits of matcha (Camellia sinensis), and the application of chemometrics and multi-omics in the science of matcha. The discussion primarily distinguishes between matcha and regular green tea with processing and compositional factors, and demonstrates beneficial health effects of consuming matcha. Preferred Reporting Items for Systematic Reviews and Meta-Analyses was adopted to search for relevant information in this review. Boolean operators were incorporated to explore related sources in various databases. Notably, climate, cultivar, maturity of tea leaves, grinding process and brewing temperature impact on the overall quality of matcha. Besides, sufficient shading prior to harvesting significantly increases the contents of theanine and chlorophyll in the tea leaves. Furthermore, the ground whole tea leaf powder delivers matcha with the greatest benefits to the consumers. The health promoting benefits of matcha are mainly contributed by its micro-nutrients and the antioxidative phytochemicals, specifically epigallocatechin-gallate, theanine and caffeine. Collectively, the chemical composition of matcha affected its quality and health benefits significantly. To this end, more studies are required to elucidate the biological mechanisms of these compounds for human health. Chemometrics and multi-omics technologies are useful to fill up the research gaps identified in this review.

Prediction of fatty acid composition in intact and minced fat of European autochthonous pigs breeds by near infrared spectroscopy

The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.

Soybean Replacement by Alternative Protein Sources in Pig Nutrition and Its Effect on Meat Quality

Soybean is one of the most expensive and limiting feed ingredients in diet formulations; however, in pig farming, it represents the main source of protein. The production and supply of soybean are critical steps due to their environmental impact and feed/food competition for land use. Therefore, research is focusing on finding alternatives to replace soybean partially or totally. However, alternative ingredients should ensure similar growth performance, carcass traits, and meat quality characteristics compared to conventional soybean-based diets. The objective of this review was to evaluate the impact of different alternative protein sources to soybean in pig nutrition and their effects on growth performance, carcass, and meat quality traits. The review process was performed on Scopus^®, and it considered research findings published from 2012 to the present on the Sus scrofa species. Articles without a control group fed with soybean were discarded. The main alternative protein sources identified were other legumes and distillers' dried grain with solubles (fish and animal proteins, oilseed by- and co-products). Interesting innovative protein sources included by-products from other industries (residues), microalgae and insects. Nevertheless, in dietary formulations, close attention must be paid to address the nutritional requirements, balance the supply of amino acids, avoid anti-nutritional or toxic compounds occasionally present in alternative protein sources, as well as determine the availability of protein feed in specific geographical areas.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Optimizing extraction solvents for deoxynivalenol analysis in maize via infrared attenuated total reflection spectroscopy and chemometric methods

Farmers, cereal suppliers and processors demand rapid techniques for the assessment of mould-associated contamination. Deoxynivalenol (DON) is among the most important Fusarium toxins and related to human and animal diseases besides causing significant economic losses. Routine analytical techniques for the analysis of DON are either based on chromatographic or immunoanalytical techniques, which are time-consuming and frequently rely on hazardous consumables. The present study evaluates the feasibility of infrared attenuated total reflection spectroscopy (IR-ATR) for the analysis of maize extracts via different solvents optimized for the determination of DON contamination along the regulatory requirements by the European Union (EU) for unprocessed maize (1750 μg kg^-1). Reference analysis was done by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The studied maize samples were either naturally infected or had been artificially inoculated in the field with Fusarium graminearum, Fusarium culmorum or Fusarium verticillioides. Principal component analysis demonstrated that water and methanol-water (70 : 30% v) were optimum solvents for differentiating DON contamination levels. Supervised partial least squares discriminant analysis resulted in excellent classification accuracies of 86.7% and 90.8% for water and methanol-water extracts, respectively. The IR spectra of samples with fungal infection and high DON contamination had distinct spectral features, which could be related to carbohydrates, proteins and lipid content within the investigated extracts.

Methods of Analyses for Biodegradable Polymers: A Review

Biodegradable polymers are materials that can decompose through the action of various environmental microorganisms, such as bacteria and fungi, to form water and carbon dioxide. The biodegradability characteristics have led to a growing demand for the accurate and precise determination of the degraded polymer composition. With the advancements in analytical product development, various analytical methods are available and touted as practical and preferable methods of bioanalytical techniques, which enable the understanding of the complex composition of biopolymers such as polyhydroxyalkanoates and poly(lactic acid). The former part of this review discusses the definition and examples of biopolymers, followed by the theory and instrumentation of analytical methods applicable to the analysis of biopolymers, such as physical methods (SEM, TEM, weighing analytical balance, etc.), chromatographic methods (GC, THM-GC, SEC/GPC), spectroscopic methods (NMR, FTIR, XRD, XRF), respirometric methods, thermal methods (DSC, DTA, TGA), and meta-analysis. Special focus is given to the chromatographic methods, because this is the routine method of polymer analysis. The aim of this review is to focus on the recent developments in the field of biopolymer analysis and instrument application to analyse the various types of biopolymers.

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

Exploration of Habitat-Related Chemical Markers for Stephania tetrandra Applying Multiple Chromatographic and Chemometric Analysis

Geo-authentic herbs refer to medicinal materials produced in a specific region with superior quality. Stephania tetrandra S. Moore (S. tetrandra) is cultivated in many provinces of China, including Anhui, Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Guangdong, Hainan, and Taiwan, among which Jiangxi is the geo-authentic origin. To explore habitat-related chemical markers of herbal medicine, an integrated chromatographic technique including gas chromatography-mass spectrometry (GC-MS), ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) and ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) combined with chemometric analysis was established. The established methods manifested that they were clearly divided into two groups according to non-authentic origins and geo-authentic origins, suggesting that the metabolites were closely related to their producing areas. A total of 70 volatile compounds and 50 non-volatile compounds were identified in S. tetrandra. Meanwhile, tetrandrine, fangchinoline, isocorydine, magnocurarine, magnoflorine, boldine, and higenamine as chemical markers were accurately quantified and suggested importance in grouping non-authentic origins and geo-authentic origins samples. The discriminatory analysis also indicated well prediction performance with an accuracy of 80%. The results showed that the multiple chromatographic and chemometric analysis technique could be used as an effective approach for discovering the chemical markers of herbal medicine to fulfill the evaluation of overall chemical consistency among samples from different producing areas.

Quality assessment of African herbal medicine: A systematic review and the way forward

BACKGROUND

In Africa, herbalism supplements allopathic medicine's efforts to ensure Universal Health Coverage attainment. This review was conducted to identify and to summarise current literature on methodological approaches used for quality control of herbal medicines in Africa, to evaluate the gaps associated with existing strategies within context of best practices, and make recommendations for future improvements.

METHODS

A systematic search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles were screened and assessed for eligibility.

RESULTS

118 articles were included into the study. There was a high preference for impurity profiling tests (77%) indicating the prioritization for tests that guarantee safety despite the limited analytical resources available. Other classes of tests reported included identification tests (29%), physicochemical tests (18%), and content assays (12%). Although standard methods exist in preparing samples for impurity tests, different techniques were observed in different studies, and this could lead to differences in analytical outcomes. Content assays focused on single marker assessments, which may be inadequate to comprehensively assess the quality of products.

CONCLUSION

This review provides knowledge of existing strengths and challenges for herbal medicine quality assessments in Africa. For future it is recommended to implement more studies on contaminants (e.g. mycotoxins) and pharmaceutical adulterants. The use of chemometrics to develop analytical methods should be promoted. Also, stakeholders in the medicine quality industry in Africa need to effectively collaborate to establish a well co-ordinated and harmonized system to provide a sustainable framework for the GACP and GMP guided production and quality assurance of herbal medicines.

A hybrid variable selection and modeling strategy for the determination of target compounds in different spectral datasets

In this paper, a hybrid technique is proposed to establish quantitative models for the determination of target compounds in different spectral datasets. The proposed hybrid method is the hybridization of interval partial least squares (iPLS) method with gradient descent (GD) algorithm. Here, the novelty of the proposed method is that the iPLS method is applied to variable selection and the GD algorithm is employed to establish quantitative models based on the selected optimal variables. In the application of the hybrid iPLS-GD method, the factors, i.e., the number of the interval for the iPLS method and the learning rate, the number of iterations for the GD method, that affect the quantitative accuracy of the method are optimized and determined. Then three spectral datasets, including the near-infrared spectroscopy (NIR) dataset, nuclear magnetic resonance (¹H NMR) dataset and excitation-emission matrix fluorescence (EEM) dataset, are used to test and verify the performance of the iPLS-GD method. We compare the hybrid iPLS-GD method with the PLS and iPLS methods from the aspects of modeling ability and predictive ability. The results demonstrated that the iPLS-GD method can be used as an effective and promising tool for the determination of target compounds in complex samples in practice.

Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field

The biogeographical ancestry (BGA) of a trace or a person/skeleton refers to the component of ethnicity, constituted of biological and cultural elements, that is biologically determined. Nowadays, many individuals are interested in exploring their genealogy, and the capability to distinguish biogeographic information about population groups and subgroups via DNA analysis plays an essential role in several fields such as in forensics. In fact, for investigative and intelligence purposes, it is beneficial to inference the biogeographical origins of perpetrators of crimes or victims of unsolved cold cases when no reference profile from perpetrators or database hits for comparative purposes are available. Current approaches for biogeographical ancestry estimation using SNPs data are usually based on PCA and Structure software. The present study provides an alternative method that involves multivariate data analysis and machine learning strategies to evaluate BGA discriminating power of unknown samples using different commercial panels. Starting from 1000 Genomes project, Simons Genome Diversity Project and Human Genome Diversity Project datasets involving African, American, Asian, European and Oceania individuals, and moving towards further and more geographically restricted populations, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and machine learning techniques such as XGBoost were employed, and their discriminating power was compared. PLS-DA method provided more robust classifications than XGBoost method, showing that the adopted approach might be an interesting tool for forensic experts to infer BGA information from the DNA profile of unknown individuals, but also highlighting that the commercial forensic panels could be inadequate to discriminate populations at intra-continental level.

Electronic tongue applications for wastewater and soil analysis

Assessment of water and soil quality is critical for the health, economy, and sustainability of any community. The release of a range of life-threatening pollutants from agriculture, industries, and the residential communities themselves into the different water resources and soil requires of analytical methods intended for their detection. Given the challenge that represents coping with the monitoring of such a diverse and large number of compounds (with over 100,000 chemicals registered, yet in continuous increase), holistic solutions such as electronic tongues (ETs) are emerging as a promising tool for a sustainable, simple, and green monitoring of soil and water resources. In this direction, this review aims to present and critically provide an overview of the basic concepts of ETs, followed by some relevant applications recently reported in the literature in environmental analysis, more specifically, the monitoring of water and wastewater, their quality and the detection of water pollutants as well as soil analysis.

Vibrational Spectroscopy Combined with Chemometrics in Authentication of Functional Foods

Many foods have both edible and medical importance and are appreciated as functional foods, preventing diseases. However, due to unscrupulous vendors and imperfect market supervision mechanisms, curative foods are prone to adulteration or some other events that harm the interests of consumers. However, traditional analytical methods are unsuitable and expensive for a broad and complex application. Therefore, people urgently need a fast, efficient, and accurate detection method to protect self-interests. Recently, the study of target samples by vibration spectrum shows strong qualitative and quantitative ability. The model established by platform technology combined with the stoichiometric analysis method can obtain better parameters, which it has good robustness and can detect functional food efficiently, quickly and nondestructive. The review compared and prospect five different vibrational spectroscopic techniques (near-infrared, Fourier transform infrared, Raman, hyperspectral imaging spectroscopy and Terahertz spectroscopy). In order to better solve some of the actual situations faced by certification, we explore and through relevant research and investigation to appropriately highlight the applicability and importance of technology combined with chemometrics in functional food authentication. There are four categories of authentication discussed: functional food authenticated in source, processing method, fraud and ingredient ratio. This paper provides an innovative process for the authentication of functional food, which has a meaningful reference value for future review or scientific research of relevant departments.

Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics

Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.

Application of multivariate data analysis for food quality investigations: An example-based review

These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.

Application of reverse engineering in the field of pharmaceutical tablets using Raman mapping and chemometrics

Raman micro-spectroscopy technique offers a combination of relatively high spatial resolution with identification of components or mixtures of components in different sample areas, e.g. on the surface or the cross-section of a sample. This study is focused on the analysis of the tablets from pharmaceutical development with different technological parameters: (1) the manufacturing technology, (2) the particle size of the input API (active pharmaceutical ingredient) and (3) the quantitative composition of the individual excipients. These three mentioned parameters represent the most frequently solved problems in the field of reverse engineering in pharmacy. The investigation aims to distinguish tablets with the above-described technological parameters with limited subjective steps by Raman microscopy. Furthermore, non-subjective methods of Raman data analysis using advanced statistical analysis have been proposed, namely Principal Component Analysis, Soft Independent Modelling of Class Analogy and Linear Discriminant Analysis. The methods successfully distinguished and identified even very small differences in the analysed tablets within our study and provided objective statistic evaluation of Raman maps. The information on component and particle size distribution including their small differences, which is the critical parameter in the development of the original and generic products, was obtained due to combination of these methods. Even though each of these chemometric methods evaluates the data set from a different perspective, their mutual application on the problem of Raman maps evaluation confirmed and specified results on level that would be unattainable with the use of only one them.

Optical deciphering of multinary chiral compound mixtures through organic reaction based chemometric chirality sensing

The advances of high-throughput experimentation technology and chemometrics have revolutionized the pace of scientific progress and enabled previously inconceivable discoveries, in particular when used in tandem. Here we show that the combination of chirality sensing based on small-molecule optical probes that bind to amines and amino alcohols via dynamic covalent or click chemistries and powerful chemometric tools that achieve orthogonal data fusion and spectral deconvolution yields a streamlined multi-modal sensing protocol that allows analysis of the absolute configuration, enantiomeric composition and concentration of structurally analogous—and therefore particularly challenging—chiral target compounds without laborious and time-consuming physical separation. The practicality, high accuracy, and speed of this approach are demonstrated with complicated quaternary and octonary mixtures of varying chemical and chiral compositions. The advantages over chiral chromatography and other classical methods include operational simplicity, increased speed, reduced waste production, low cost, and compatibility with multiwell plate technology if high-throughput analysis of hundreds of samples is desired.

The stereoselective analysis of mixtures of chiral compounds typically requires time-consuming chromatography. Here, the authors combine reaction based chiroptical sensing and chemometric tools to directly determine the absolute configuration, enantiomeric composition and concentration of convoluted samples without physical separation.

Metabolomics Data Treatment: Basic Directions of the Full Process

The present chapter describes basic aspects of the main steps for data processing on mass spectrometry-based metabolomics platforms, focusing on the main objectives and important considerations of each step. Initially, an overview of metabolomics and the pivotal techniques applied in the field are presented. Important features of data acquisition and preprocessing such as data compression, noise filtering, and baseline correction are revised focusing on practical aspects. Peak detection, deconvolution, and alignment as well as missing values are also discussed. Special attention is given to chemical and mathematical normalization approaches and the role of the quality control (QC) samples. Methods for uni- and multivariate statistical analysis and data pretreatment that could impact them are reviewed, emphasizing the most widely used multivariate methods, i.e., principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least square-discriminant analysis (OPLS-DA), and hierarchical cluster analysis (HCA). Criteria for model validation and softwares used in data processing were also approached. The chapter ends with some concerns about the minimal requirements to report metadata in metabolomics.

Non-destructive raman spectroscopic determination of freshwater mollusk composition, growth, and damage repair

Increased acidification of aquatic habitats due to climate change is damaging mollusks. Non-destructive methods for analysis are necessary to study these endangered species. We analyzed five Unionidae gastropods using Raman spectroscopy. Shells were primarily composed of aragonite, a polymorph of calcium carbonate found in shell microstructure. Lattice mode Raman peaks from vaterite, a thought to be rare polymorph of calcium carbonate, were identified in each mollusk. Vaterite is present in mollusks at instances of shell damage and subsequent repair. We demonstrate that Raman spectroscopy is sensitive to vaterite, and it may not be as rare as previously thought. We also collected Raman spectra across the interior of Lampsillis fasciola. This data was analyzed through multivariate analysis, combining Principal Component Analysis with Linear Discriminant Analysis (PCA-LDA). Results of PCA-LDA correlate with growth of the mollusks, demonstrating that Raman spectroscopy combined with multivariate analysis could be used to monitor shell growth.

Beyond principal components: a critical comparison of factor analysis methods for subspace modelling in chemistry

Multivariate data analysis tools have become an integral part of modern analytical chemistry, and principal component analysis (PCA) is perhaps foremost among these. PCA is central in approaching many problems in data exploration, classification, calibration, modelling, and curve resolution. However, PCA is only one form of a broader group of factor analysis (FA) methods that are rarely employed by chemists. The dominance of PCA in chemistry is primarily a consequence of history and convenience, but this has obscured the potential advantages of other FA tools that are widely used in other fields. The purpose of this article, which is intended for those who are already familiar with the mathematical foundations and applications of PCA, is to develop a framework to relate PCA to other commonly used FA methods from the perspective of chemical applications. Specifically, PCA is compared to maximum likelihood factor analysis (MLFA), principal axis factorization (PAF) and maximum likelihood PCA (MLPCA). Similarities and differences are highlighted with regard to the assumptions and constraints of the models, algorithms employed, and calculation of scores and loadings. Practical aspects such as data dimensionality, preprocessing, rank estimation, improper solutions (Heywood cases), and software implementation are considered. The performance of the four methods is compared using both simulated and experimental data sets. While PCA provides the most reliable estimates when measurement error variance is uniform (homoscedastic noise) and MLPCA works best when the error covariance matrix is explicitly known, MLFA and PAF have the distinct advantage of providing information about measurement uncertainty and adapting to situations of unknown heteroscedastic errors, eliminating the need for scaling. Moreover, MLFA in particular is shown to be tolerant to deviations from model linearity. These results make a strong case for increased application of other FA methods in chemistry.

Comparison of the chemical profile differences of Aster tataricus between raw and processed products by metabolomics coupled with chemometrics methods

Aster tataricus, a traditional Chinese herb, has been used to treat cough and asthma for many years. Its raw and processed products have different pharmacological effects in clinical applications. To explore the chemical profile differences of components in A. tataricus processed with different methods, metabolomics methods based on ultra-high-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry and gas chromatography-mass spectrometry were developed. Chemometrics strategy was applied to filter and screen the candidate compounds. The accuracy of differential markers was validated by back propagation neural network. The established methods showed that raw A. tataricus, honey-processed A. tataricus, vinegar-processed A. tataricus, and steamed A. tataricus were clearly divided into four groups, suggesting that the components were closely related to the processing methods. A total of 64 nonvolatile and 43 volatile compounds were identified in A. tataricus, and 22 nonvolatile and 12 volatile differential constituents were selected to distinguish the raw and processed A. tataricus. This study demonstrated that the metabolomics methods coupled with chemometrics were a comprehensive strategy to analyze the chemical profile differences and provided a reliable reference for quality evaluation of A. tataricus.