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Chemometrics Tools Used in Analytical Chemistry
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Chemometrics Tools Used in Analytical Chemistry

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 52831

Special Issue Editor

Dr. Ewa Sikorska

E-Mail Website
Guest Editor
Department of Technology and Instrumental Analysis, Institute of Quality Science, University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland
Interests: food chemistry and analysis; analytical chemistry; molecular spectroscopy;chemometrics photophysics and photochemistry of biologically important molecules

Special Issue Information

Dear Colleagues,

The development of new instruments and hyphenated techniques as well as new analytical strategies such as profiling and fingerprtinting contribute to obtaining a large amount of data characterizing the systems studied. These methodologies require the use of chemometric tools for data analysis. In modern analytical chemistry, chemometric methods are used to design experiments and to extract analytical information from the multivariate and multiway data acquired during experiments. Unsupervised methods are used for data visualization and exploration. Supervised methods are applied for classification and calibration. In research, chemometric methods enable the modeling properties of chemical systems and discover the structure and relationships of the data. The multivariate models developed using chemometric methods are the basis for the practical application of instrumental techniques in many field,s including food analysis, process analytical technology, environmental control, medical, pharmaceutical, biological, and forensic fields.

This Special Issue aims to cover original research papers and reviews related to the development of new multivariate and multiway methods and to methodological aspects of chemometric research, such as model optimization, preprocessing, variable selection, and data fusion. Application-oriented papers related to using chemometrics in different fields are also very welcome.

Dr. Ewa Sikorska
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Design of experiments (DoE)
  • Multivariate methods
  • Multiway methods
  • Exploratory data analysis
  • Classification and calibration
  • Model optimization
  • Application of chemometrics

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Published Papers (15 papers)

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Research

Jump to: Review

16 pages, 7235 KiB  
Article
Assessment of the Kernel Gram Matrix Representation of Data in Order to Avoid the Alignment of Chromatographic Signals
by Ivana Stanimirova and Michal Daszykowski
Molecules 2021, 26(3), 621; https://doi.org/10.3390/molecules26030621 - 25 Jan 2021
Viewed by 1903
Abstract
This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It [...] Read more.
This article discusses the possibility of exploratory data analysis of samples described by second-order chromatographic data affected by peak shifts. In particular, the potential of the kernel Gram matrix representation as an alternative to the necessary and time-consuming alignment step is evaluated. It was demonstrated through several simulation studies and comparisons that even small peak shifts can be a substantial source of data variance, and they can easily hamper the interpretation of chromatographic data. When peak shifts are small, their negative effect is far more destructive than the impact of relatively large levels of the Gaussian noise, heteroscedastic noise, and signal’s baseline. The Gram principal component analysis approach has proven to be a well-suited tool for exploratory analysis of chromatographic signals collected using the diode-array detector in which sample-to-sample peak shifts were observed. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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15 pages, 1392 KiB  
Article
Untargeted Ultrahigh-Performance Liquid Chromatography-Hybrid Quadrupole-Orbitrap Mass Spectrometry (UHPLC-HRMS) Metabolomics Reveals Propolis Markers of Greek and Chinese Origin
by Maria-Ioanna Stavropoulou, Aikaterini Termentzi, Konstantinos M. Kasiotis, Antigoni Cheilari, Konstantina Stathopoulou, Kyriaki Machera and Nektarios Aligiannis
Molecules 2021, 26(2), 456; https://doi.org/10.3390/molecules26020456 - 16 Jan 2021
Cited by 9 | Viewed by 2993
Abstract
Chemical composition of propolis depends on the plant source and thus on the geographic and climatic characteristics of the site of collection. The aim of this study was to investigate the chemical profile of Greek and Chinese propolis extracts from different regions and [...] Read more.
Chemical composition of propolis depends on the plant source and thus on the geographic and climatic characteristics of the site of collection. The aim of this study was to investigate the chemical profile of Greek and Chinese propolis extracts from different regions and suggest similarities and differences between them. Untargeted ultrahigh-performance liquid chromatography coupled to hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-HRMS) method was developed and 22 and 23 propolis samples from Greece and China, respectively, were analyzed. The experimental data led to the observation that there is considerable variability in terms of quality of the distinctive propolis samples. Partial least squares - discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed and allowed the identification of significant features for sample discrimination, adding relevant information for the identification of class-determining metabolites. Chinese samples overexpressed compounds that are characteristic of the poplar type propolis, whereas Greek samples overexpress the latter and the diterpenes characteristic of the Mediterranean propolis type. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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14 pages, 2246 KiB  
Article
Phytochemical Profiling and Quality Control of Terminalia sericea Burch. ex DC. Using HPTLC Metabolomics
by Nduvho Mulaudzi, Chinedu P. Anokwuru, Sidonie Y. Tankeu, Sandra Combrinck, Weiyang Chen, Ilze Vermaak and Alvaro M Viljoen
Molecules 2021, 26(2), 432; https://doi.org/10.3390/molecules26020432 - 15 Jan 2021
Cited by 6 | Viewed by 2814
Abstract
Terminalia sericea is used throughout Africa for the treatment of a variety of conditions and has been identified as a potential commercial plant. The study was aimed at establishing a high-performance thin layer chromatography (HPTLC) chemical fingerprint for T. sericea root bark as [...] Read more.
Terminalia sericea is used throughout Africa for the treatment of a variety of conditions and has been identified as a potential commercial plant. The study was aimed at establishing a high-performance thin layer chromatography (HPTLC) chemical fingerprint for T. sericea root bark as a reference for quality control and exploring chemical variation within the species using HPTLC metabo3lomics. Forty-two root bark samples were collected from ten populations in South Africa and extracted with dichloromethane: methanol (1:1). An HPTLC method was optimized to resolve the major compounds from other sample components. Dichloromethane: ethyl acetate: methanol: formic acid (90:10:30:1) was used as the developing solvent and the plates were visualized using 10% sulfuric acid in methanol as derivatizing agent. The concentrations of three major bioactive compounds, sericic acid, sericoside and resveratrol-3-O-β-rutinoside, in the extracts were determined using a validated ultra-performance liquid chromatography-photodiode array (UPLC-PDA) detection method. The rTLC software (written in the R-programming language) was used to select the most informative retardation factor (Rf) ranges from the images of the analysed sample extracts. Further chemometric models, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were constructed using the web-based high throughput metabolomic software. The rTLC chemometric models were compared with the models previously obtained from ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS). A characteristic fingerprint containing clear bands for the three bioactive compounds was established. All three bioactive compounds were present in all the samples, although their corresponding band intensities varied. The intensities correlated with the UPLC-PDA results, in that samples containing a high concentration of a particular compound, displayed a more intense band. Chemometric analysis using HCA revealed two chemotypes, and the subsequent construction of a loadings plot indicated that sericic acid and sericoside were responsible for the chemotypic variation; with sericoside concentrated in Chemotype 1, while sericic acid was more abundant in Chemotype 2. A characteristic chemical fingerprint with clearly distinguishable features was established for T. sericea root bark that can be used for species authentication, and to select samples with high concentrations of a particular marker compound(s). Different chemotypes, potentially differing in their therapeutic potency towards a particular target, could be distinguished. The models revealed the three analytes as biomarkers, corresponding to results reported for UPLC-MS profiling and thereby indicating that HPTLC is a suitable technique for the quality control of T. sericea root bark. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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16 pages, 2687 KiB  
Article
WE-ASCA: The Weighted-Effect ASCA for Analyzing Unbalanced Multifactorial Designs—A Raman Spectra-Based Example
by Nairveen Ali, Jeroen Jansen, André van den Doel, Gerjen Herman Tinnevelt and Thomas Bocklitz
Molecules 2021, 26(1), 66; https://doi.org/10.3390/molecules26010066 - 25 Dec 2020
Cited by 6 | Viewed by 2947
Abstract
Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only [...] Read more.
Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only a few methods have been developed for multivariate data. In this work, we present the weighted-effect ASCA, named WE-ASCA, as an enhanced version of ANOVA-simultaneous component analysis (ASCA) to deal with multivariate data in unbalanced multifactorial designs. The core of our work is to use general linear models (GLMs) in decomposing the response matrix into a design matrix and a parameter matrix, while the main improvement in WE-ASCA is to implement the weighted-effect (WE) coding in the design matrix. This WE-coding introduces a unique solution to solve GLMs and satisfies a constrain in which the sum of all level effects of a categorical variable equal to zero. To assess the WE-ASCA performance, two applications were demonstrated using a biomedical Raman spectral data set consisting of mice colorectal tissue. The results revealed that WE-ASCA is ideally suitable for analyzing unbalanced designs. Furthermore, if WE-ASCA is applied as a preprocessing tool, the classification performance and its reproducibility can significantly improve. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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15 pages, 3025 KiB  
Article
Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data
by Reynaldo Villarreal-González, Antonio J. Acosta-Hoyos, Jaime A. Garzon-Ochoa, Nataly J. Galán-Freyle, Paola Amar-Sepúlveda and Leonardo C. Pacheco-Londoño
Molecules 2021, 26(1), 20; https://doi.org/10.3390/molecules26010020 - 23 Dec 2020
Cited by 4 | Viewed by 3147
Abstract
Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR [...] Read more.
Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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15 pages, 3042 KiB  
Article
Development of Advanced Chemometric-Assisted Spectrophotometric Methods for the Determination of Cromolyn Sodium and Its Alkaline Degradation Products
by Noha M. El Zahar, Mariam M. Tadros and Bassam M. Ayoub
Molecules 2020, 25(24), 5953; https://doi.org/10.3390/molecules25245953 - 16 Dec 2020
Cited by 5 | Viewed by 2243
Abstract
Advanced and sensitive spectrophotometric and chemometric analytical methods were successfully established for the stability-indicating assay of cromolyn sodium (CS) and its alkaline degradation products (Deg1 and Deg2). Spectrophotometric mean centering ratio spectra method (MCR) and chemometric methods, including principal component regression (PCR) and [...] Read more.
Advanced and sensitive spectrophotometric and chemometric analytical methods were successfully established for the stability-indicating assay of cromolyn sodium (CS) and its alkaline degradation products (Deg1 and Deg2). Spectrophotometric mean centering ratio spectra method (MCR) and chemometric methods, including principal component regression (PCR) and partial least square (PLS-2) methods, were applied. Peak amplitudes after MCR at 367.8 nm, 373.8 nm and 310.6 nm were used within linear concentration ranges of 2–40 µg mL−1, 5–40 µg mL−1 and 10–100 µg mL−1 for CS, Deg1 and Deg2, respectively. For PCR and PLS-2 models, a calibration set of eighteen mixtures and a validation set of seven mixtures were built for the simultaneous determination of CS, Deg1 and Deg2 in the ranges of 5–13 µg mL−1, 8–16 µg mL−1, and 10–30 µg mL−1, respectively. The authors emphasize the importance of a stability-indicating strategy for the investigation of pharmaceutical products. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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20 pages, 2511 KiB  
Article
Metabolite Characterization and Correlations with Antioxidant and Wound Healing Properties of Oil Palm (Elaeis guineensis Jacq.) Leaflets via 1H-NMR-Based Metabolomics Approach
by Mohamad Shazeli Che Zain, Soo Yee Lee, Nadiah Mad Nasir, Sharida Fakurazi and Khozirah Shaari
Molecules 2020, 25(23), 5636; https://doi.org/10.3390/molecules25235636 - 30 Nov 2020
Cited by 9 | Viewed by 3849
Abstract
Oil palm (Elaeis guineensis Jacq.) leaflets (OPLs) are one of the major agricultural by-products generated from the massive cultivation of Malaysian palm oil. This biomass is also reported to be of potential value based on its health-improving effects. By employing proton nuclear [...] Read more.
Oil palm (Elaeis guineensis Jacq.) leaflets (OPLs) are one of the major agricultural by-products generated from the massive cultivation of Malaysian palm oil. This biomass is also reported to be of potential value based on its health-improving effects. By employing proton nuclear magnetic resonance (1H-NMR) spectroscopy combined with multivariate data analysis (MVDA), the metabolite profile of OPLs was characterized and correlated with their antioxidant and wound healing properties. Principal component analysis (PCA) classified four varieties of extracts, prepared using solvents ranging from polar to medium polarity, into three distinct clusters. Cumulatively, six flavonoids, eight organic acids, four carbohydrates, and an amine were identified from the solvent extracts. The more polar extracts, such as, the ethyl acetate-methanol, absolute methanol, and methanol-water, were richer in phytochemicals. Based on partial least square (PLS) analysis, the constituents in these extracts, such as (+)-catechin, (−)-epicatechin, orientin, isoorientin, vitexin, and isovitexin, were strongly correlated with the measured antioxidant activities, comprising ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and nitric oxide (NO) free radical scavenging activities, as well as with cell proliferation and migration activities. This study has provided crucial evidence on the importance of these natural antioxidant compounds on the wound healing properties of OPL. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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10 pages, 734 KiB  
Article
Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles
by Ioana Feher, Dana Alina Magdas, Cezara Voica, Gabriela Cristea and Costel Sârbu
Molecules 2020, 25(21), 4955; https://doi.org/10.3390/molecules25214955 - 26 Oct 2020
Cited by 5 | Viewed by 1688
Abstract
Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied [...] Read more.
Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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10 pages, 1554 KiB  
Article
N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil
by Larissa Naida Rosa, Thays Raphaela Gonçalves, Sandra T. M. Gomes, Makoto Matsushita, Rhayanna Priscila Gonçalves, Paulo Henrique Março and Patrícia Valderrama
Molecules 2020, 25(19), 4366; https://doi.org/10.3390/molecules25194366 - 23 Sep 2020
Cited by 3 | Viewed by 1995
Abstract
The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have [...] Read more.
The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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15 pages, 2551 KiB  
Article
Determination of Cadmium (II) in Aqueous Solutions by In Situ MID-FTIR-PLS Analysis Using a Polymer Inclusion Membrane-Based Sensor: First Considerations
by René González-Albarrán, Josefina de Gyves and Eduardo Rodríguez de San Miguel
Molecules 2020, 25(15), 3436; https://doi.org/10.3390/molecules25153436 - 29 Jul 2020
Cited by 12 | Viewed by 2718
Abstract
Environmental monitoring is one of the most dynamically developing branches of chemical analysis. In this area, the use of multidimensional techniques and methods is encouraged to allow reliable determinations of metal ions with portable equipment for in-field applications. In this regard, this study [...] Read more.
Environmental monitoring is one of the most dynamically developing branches of chemical analysis. In this area, the use of multidimensional techniques and methods is encouraged to allow reliable determinations of metal ions with portable equipment for in-field applications. In this regard, this study presents, for the first time, the capabilities of a polymer inclusion membrane (PIM) sensor to perform cadmium (II) determination in aqueous solutions by in situ visible (VIS) and Mid- Fourier transform infrared spectroscopy (MID-FTIR) analyses of the polymeric films, using a partial least squares (PLS) chemometric approach. The influence of pH and metal content on cadmium (II) extraction, the characterization of its extraction in terms of the adsorption isotherm, enrichment factor and extraction equilibrium were studied. The PLS chemometric algorithm was applied to the spectral data to establish the relationship between cadmium (II) content in the membrane and the absorption spectra. Furthermore, the developed MID-FTIR method was validated through the determination of the figures of merit (accuracy, linearity, sensitivity, analytical sensitivity, minimum discernible concentration difference, mean selectivity, and limits of detection and quantitation). Results showed reliable calibration curves denoting systems’ potentiality. Comparable results were obtained in the analysis of real samples (tap, bottle, and pier water) between the new MID-FTIR-PLS PIM based-sensor and F-AAS. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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13 pages, 2123 KiB  
Article
Metabolite Profiles of Red and Yellow Watermelon (Citrullus lanatus) Cultivars Using a 1H-NMR Metabolomics Approach
by Fadzil Sulaiman, Amalina Ahmad Azam, Muhammad Safwan Ahamad Bustamam, Sharida Fakurazi, Faridah Abas, Yee Xuan Lee, Atira Adriana Ismail, Siti Munirah Mohd Faudzi and Intan Safinar Ismail
Molecules 2020, 25(14), 3235; https://doi.org/10.3390/molecules25143235 - 15 Jul 2020
Cited by 11 | Viewed by 4072
Abstract
Watermelon, a widely commercialized fruit, is famous for its thirst-quenching property. The broad range of cultivars, which give rise to distinct color and taste, can be attributed to the differences in their chemical profile, especially that of the carotenoids and volatile compounds. In [...] Read more.
Watermelon, a widely commercialized fruit, is famous for its thirst-quenching property. The broad range of cultivars, which give rise to distinct color and taste, can be attributed to the differences in their chemical profile, especially that of the carotenoids and volatile compounds. In order to understand this distribution properly, water extracts of red and yellow watermelon pulps with predominantly polar metabolites were subjected to proton nuclear magnetic resonance (1H-NMR) analysis. Deuterium oxide (D2O) and deuterated chloroform (CDCl3) solvents were used to capture both polar and non-polar metabolites from the same sample. Thirty-six metabolites, of which six are carotenoids, were identified from the extracts. The clustering of the compounds was determined using unsupervised principal component analysis (PCA) and further grouping was achieved using supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The presence of lycopene, β-carotene, lutein, and prolycopene in the red watermelon plays an important role in its differentiation from the yellow cultivar. A marked difference in metabolite distribution was observed between the NMR solvents used as evidenced from the PCA model. OPLS-DA and relative quantification of the metabolites, on the other hand, helped in uncovering the discriminating metabolites of the red and yellow watermelon cultivars from the same solvent system. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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Review

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28 pages, 461 KiB  
Review
Comprehensive Review on Application of FTIR Spectroscopy Coupled with Chemometrics for Authentication Analysis of Fats and Oils in the Food Products
by Abdul Rohman, Mohd Al’Ikhsan B. Ghazali, Anjar Windarsih, Irnawati, Sugeng Riyanto, Farahwahida Mohd Yusof and Shuhaimi Mustafa
Molecules 2020, 25(22), 5485; https://doi.org/10.3390/molecules25225485 - 23 Nov 2020
Cited by 47 | Viewed by 7272
Abstract
Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats [...] Read more.
Currently, the authentication analysis of edible fats and oils is an emerging issue not only by producers but also by food industries, regulators, and consumers. The adulteration of high quality and expensive edible fats and oils as well as food products containing fats and oils with lower ones are typically motivated by economic reasons. Some analytical methods have been used for authentication analysis of food products, but some of them are complex in sampling preparation and involving sophisticated instruments. Therefore, simple and reliable methods are proposed and developed for these authentication purposes. This review highlighted the comprehensive reports on the application of infrared spectroscopy combined with chemometrics for authentication of fats and oils. New findings of this review included (1) FTIR spectroscopy combined with chemometrics, which has been used to authenticate fats and oils; (2) due to as fingerprint analytical tools, FTIR spectra have emerged as the most reported analytical techniques applied for authentication analysis of fats and oils; (3) the use of chemometrics as analytical data treatment is a must to extract the information from FTIR spectra to be understandable data. Next, the combination of FTIR spectroscopy with chemometrics must be proposed, developed, and standardized for authentication and assuring the quality of fats and oils. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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36 pages, 3448 KiB  
Review
Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity
by Marina Creydt and Markus Fischer
Molecules 2020, 25(17), 3972; https://doi.org/10.3390/molecules25173972 - 31 Aug 2020
Cited by 13 | Viewed by 4292
Abstract
Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of [...] Read more.
Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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16 pages, 754 KiB  
Review
The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy
by Daniel Cozzolino
Molecules 2020, 25(16), 3674; https://doi.org/10.3390/molecules25163674 - 12 Aug 2020
Cited by 25 | Viewed by 3064
Abstract
The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a [...] Read more.
The last two decades have witnessed an increasing interest in the use of the so-called rapid analytical methods or high throughput techniques. Most of these applications reported the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman) in a wide range of samples (e.g., food ingredients and natural products). In these applications, the analytical method is integrated with a wide range of multivariate data analysis (MVA) techniques (e.g., pattern recognition, modelling techniques, calibration, etc.) to develop the target application. The availability of modern and inexpensive instrumentation together with the access to easy to use software is determining a steady growth in the number of uses of these technologies. This paper underlines and briefly discusses the three critical pillars—the sample (e.g., sampling, variability, etc.), the spectra and the mathematics (e.g., algorithms, pre-processing, data interpretation, etc.)—that support the development and implementation of vibrational spectroscopy applications. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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58 pages, 4306 KiB  
Review
Comparison of Chemometric Problems in Food Analysis using Non-Linear Methods
by Werickson Fortunato de Carvalho Rocha, Charles Bezerra do Prado and Niksa Blonder
Molecules 2020, 25(13), 3025; https://doi.org/10.3390/molecules25133025 - 2 Jul 2020
Cited by 26 | Viewed by 5319
Abstract
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately [...] Read more.
Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Analytical Chemistry)
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