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Fourier-transform infrared spectroscopy (FTIR) serves as a rapid analytical technique to characterize food specimens chemically. The purpose of this study was to investigate the potential of FTIR combined with multivariate statistics to detect Alpha-glucosidase (Alpha-glu) inhibitory activities of a non-cereal flour-like coconut testa flour (CTF). CTF of five distinct local cultivars was sequentially extracted with hexane, ethyl acetate (EtOAc), and methanol (MeOH) to assay the Alpha-glu inhibitory activity. FTIR spectra of CTF extracts were obtained within the range of 4000–500 cm−1 and the prominent spectral peaks obtained for both hexane and EtOAc extracts were roughly similar but some additional peaks were observed in EtOAc extracts representing phenolic constituents. The major absorbance peaks found in MeOH extracts were primarily indicative of the occurrence of the hydroxyl group associated with carbohydrates and phenolic compounds. The multivariate predictive models developed using partial least squares (PLS) and orthogonal partial least squares (OPLS) regression analyses indicated a strong correlation between Alpha-glu inhibitory activity and spectral data. Models developed for the spectral regions 3700–2800 cm−1 and 1800–500 cm−1 exerted the highest regression coefficients with the lowest root mean square errors. In OPLS regression analysis, the model obtained with third-derivative spectral data was identified as the best, exhibiting the highest regression coefficients and the lowest root mean square errors. Both PLS and OPLS regression analyses indicated a potential correlation of Alpha-glu inhibitory activity with FTIR spectral regions. Notably, OPLS models offered enhanced interpretability of the model parameters. This study suggests that the application of multivariate regression analysis of FTIR spectral data on coconut-based products could help to detect Alpha-glu inhibitory activities. [ABSTRACT FROM AUTHOR]

Fourier-transform infrared spectroscopy (FTIR) serves as a rapid analytical technique to characterize food specimens chemically. The purpose of this study was to investigate the potential of FTIR combined with multivariate statistics to detect Alpha-glucosidase (Alpha-glu) inhibitory activities of a non-cereal flour-like coconut testa flour (CTF). CTF of five distinct local cultivars was sequentially extracted with hexane, ethyl acetate (EtOAc), and methanol (MeOH) to assay the Alpha-glu inhibitory activity. FTIR spectra of CTF extracts were obtained within the range of 4000–500 cm−1 and the prominent spectral peaks obtained for both hexane and EtOAc extracts were roughly similar but some additional peaks were observed in EtOAc extracts representing phenolic constituents. The major absorbance peaks found in MeOH extracts were primarily indicative of the occurrence of the hydroxyl group associated with carbohydrates and phenolic compounds. The multivariate predictive models developed using partial least squares (PLS) and orthogonal partial least squares (OPLS) regression analyses indicated a strong correlation between Alpha-glu inhibitory activity and spectral data. Models developed for the spectral regions 3700–2800 cm−1 and 1800–500 cm−1 exerted the highest regression coefficients with the lowest root mean square errors. In OPLS regression analysis, the model obtained with third-derivative spectral data was identified as the best, exhibiting the highest regression coefficients and the lowest root mean square errors. Both PLS and OPLS regression analyses indicated a potential correlation of Alpha-glu inhibitory activity with FTIR spectral regions. Notably, OPLS models offered enhanced interpretability of the model parameters. This study suggests that the application of multivariate regression analysis of FTIR spectral data on coconut-based products could help to detect Alpha-glu inhibitory activities.

Introduction: The cupping test is a widely used method for quality assessment of Arabica coffee. However, the cupping test is limited by the low number of certified panelists and the low throughput. Therefore, an analytical-based quality assessment may be a promising tool to complement the cupping test. A present, there is no report investigating quality marker candidates, focusing only on "specialty" grade Arabica coffee from Indonesia. Objective: This study identified the potential quality marker(s) in Arabica Specialty coffee at different stages (green beans, roasted beans, and brewed coffee. Methods: The metabolite profiles of ten different Arabica specialty-grade coffees were analyzed with different cup scores using gas chromatography–mass spectrometry (GC/MS). From the ten samples, green coffee beans, roasted coffee beans, and brewed coffee were selected. In addition, an orthogonal projection to latent structure (OPLS) regression analysis was conducted to obtain a potential quality marker based on the variable importance in projection (VIP). The potential quality marker(s) were validated by GC/MS metabolome profiling and OPLS analysis of different sets of samples consisting of 35 Arabica specialty-grade coffee samples. Results: In Arabica coffee samples, the OPLS model of the three stages showed galactinol to have a high VIP score. Galactinol showed a consistent positive correlation with cup scores at all stages of coffee production (green beans, roasted beans, and brewed coffee). The correlation suggests galactinol is a potential quality marker after further validation using different samples. Conclusion: GC/MS combined with OPLS regression analysis suggested galactinol as a quality marker and provide an early screening method for Arabica coffee quality that complements the cupping test performed by certified panelists. [ABSTRACT FROM AUTHOR]

Virgin Olive Oil (VOO) shelf life is determined by the varietal-specific chemical composition and principally by the of phenolic composition. The aim of this study was to investigate the changes in fatty acid profile, phenolic composition, and quality parameters of nine Italian monovarietal VOOs obtained under the same pedoclimatic, agronomic and technological conditions and stored for 12 months at 15 °C in the dark. The varieties with medium-high concentrations of secoiridoids and balanced values between the individual molecules were those with the highest stability. Orthogonal Projections to Latent Structures (OPLS) regression revealed that oleuropein derivatives and phenolic alcohols had the highest antioxidant activity. OPLS discriminant analysis separated well fresh and stored oils. PV, K270, tyrosol, hydroxytyrosol, and oxidated oleacein were the most effective indicators of VOO ageing. Oleacein and oleocanthal decreased after storage, phenolic alcohols, oleacein and ligstroside aglycon increased. [ABSTRACT FROM AUTHOR]

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Background: The oncological and functional role of postoperative radiotherapy (PORT) after open partial laryngeal surgery (OPLS) remains debatable. Methods: A systematic review and a meta-analysis of the literature were conducted according to the PRISMA guidelines. Outcomes of patients receiving OPLS with and without PORT for laryngeal cancer were summarized. Results: In the 10 studies that were included in the meta-analysis, no significant difference emerged in terms of pooled overall survival between OPLS patients who did and who did not receive PORT (− 0.3%, 95% CI − 5.4 to 4.9%, p = 0.922). Only one study showed a significantly higher incidence of complications in the PORT cohort. Conclusions: PORT may apparently be performed after OPLS in face of adverse postoperative features without an increased risk of toxicities affecting the neolarynx. Because of the limitations in the available literature, the oncological and functional effects of PORT in this setting needs to be prospectively assessed to strengthen the evidence of this treatment strategy for laryngeal cancer. [ABSTRACT FROM AUTHOR]

The current study aimed to develop an authentication technique using FTIR (Fourier transform infrared) spectroscopy and chemometrics to detect pork oil (PO) adulteration in snakehead fish oil (SFO). Twenty-one series of adulterated SFO samples were prepared by mixing SFO and PO in binary mixtures with several concentration level of PO ranging from 0 to 100% v/v. FTIR spectra of SFO and PO were very similar but SFO has a unique peak at 1418 cm−1, which is only observed in a very little shoulder in PO. The FTIR spectra were mainly from the vibration of functional groups from fatty acids and triacylglycerols. Chemometrics of principal component analysis (PCA) using 12 principal components successfully differentiated authentic and adulterated SFO samples. Classification of authentic SFO and adulterated SFO with PO was successfully obtained using orthogonal projections to latent structures–discriminant analysis (OPLS-DA) with good of fitness (R2X = 0.986; R2Y = 0.818) and good of predictivity (Q2 = 0.620). The vibrations of 1743 cm−1, 1720 cm−1, 1712 cm−1, and 1708 cm−1 (carbonyl groups); variables of 1161 cm−1, 1153 cm−1, 1137 cm−1, and 914 cm−1 (bending vibration of –CH3, –CH2, and –CH groups), and variables of 1107 cm−1 and 1099 cm−1 (C–O vibration) were found as differentiating wavenumber for samples differentiation. Permutation test and receiver operating characteristics (ROC) analysis confirmed the validity of the OPLS-DA model. The concentration of PO in SFO was successfully predicted using partial least square (PLS) and orthogonal PLS (OPLS) with high accuracy (R2 > 0.990) and high precision (RMSEE and RMSECV < 5.00) in both models. From the S-line plot, variables of 1708 cm−1, 1153 cm−1, 1107 cm−1, and 914 cm−1 had important roles in creating OPLS model. It can be concluded that a combination of FTIR spectroscopy and chemometrics could be used to as a rapid analytical method for halal authentication of SFO. [ABSTRACT FROM AUTHOR]

The study investigated the changes of nucleotides, succinic acid, and free amino acids amounts in yolk and the causes leading to the changes after pickling to uncover the fundamental umami component of preserved egg yolk. The findings demonstrated that while the contents of 5′-adenosine monophosphate (AMP), 5′-cytidine monophosphate (CMP), 5′-guanosine monophosphate (GMP), 5′-uridine monophosphate (UMP), and succinic acid increased after slightly decreasing aspartic acid (Asp) content in preserved egg yolk increased gradually. The contents of 5′-inosine monophosphate (IMP) and other free amino acids were gradually decreased. Comparing the taste activity value (TAV), it was found that the single umami substance, succinic acid, played a key role in inducing the umami taste. In combination with the Spearman correlation analysis, it was shown that the proteinaceous substance, which is the most significant umami component in preserved egg yolk, tended to condense first and subsequently disintegrate in an alkaline environment. The orthogonal partial least squares analysis (OPLS) found that pH was also affected by the changes in proteinaceous substance. These findings offer suggestions for enhancing the pickling procedure and investigating the optimal pickling period for preserved eggs.

• The quality consistency of BZYMP was evaluated using a Comprehensive linear quantitative fingerprint method (CLQFM). • Establishment of three-wavelength fusion fingerprint (TWFP) for comprehensive evaluation of BZYMP. • Establishment of Electrochemical fingerprint. • Constructed OPLS model to predict the antioxidant capacity of BZYMP. • Constructed spectral-effect relationship models for TWFP-ECFP and TWFP- IC 50. Bazhen Yimu Pill (BZYMP) is a concoction of 9 Chinese herbs with pharmacological effects such as vasodilatation, inhibition of platelet aggregation and uterine stimulation. Due to its complex composition and the interaction of multiple chemical components, it presents challenges in quality control. The aim of this study was to construct a fingerprint profile of BAYMP and to conduct a study on the relationship between the fingerprint profile and therapeutic effects. Firstly, 2 fingerprint profiles of BZYMP were established using high performance liquid chromatography (HPLC) and electrochemical analysis. In addition, in order to avoid the incomplete chemical information extracted from a single wavelength, a three-wavelength fusion fingerprint (TWFP) was established and a clear and easy-to-understand electrochemical fingerprint (ECFP) was innovatively established by using electrochemical analysis, and 9 characteristic parameters were recorded. Subsequently, the quality consistency of BZYMP was qualitatively and quantitatively evaluated by the comprehensive linear quantitative fingerprint method (CLQFM) method, while the fingerprint efficacy relationship between the fingerprint peaks of BZYMP and the IC 50 value of the DPPH assay was constructed by the OPLS model, and the relationship between the fingerprint peaks and the electrochemical analyses was explored by using the Pearson correlation coefficient. This study examined BZYMP from two dimensions, quality consistency and efficacy, and provided a new idea for other traditional Chinese medicines and their preparations to realize the dual control of quality and efficacy. [ABSTRACT FROM AUTHOR]

Schistosomiasis is a neglected tropical disease caused by parasitic flatworms (blood fluke) of the genus Schistosoma. Parasites acquire most nutrients for their development and sustainment within the definitive host either by ingestion into the gut or across the body surface. Over the years, the best conditions for long-term maintenance of parasites in vitro have been thoroughly established. In our hands, 1H-NMR spectroscopy represents a powerful tool to characterize the metabolic changes in S. mansoni in response to culturing condition perturbations. In order to compare the metabolic fingerprint of ex vivo and parasites cultured in vitro with or without the supplement of reduced glutathione, we conducted a pilot study applying the 1H-NMR spectroscopy-based metabolomics. We obtained new insight into specific metabolic pathways modulated under these different experimental conditions. [ABSTRACT FROM AUTHOR]

Oregano is one of the most used culinary herb and it is often adulterated with cheaper plants. In this study, GC–MS was used for identification and quantification of metabolites from 104 samples of oregano (Origanum vulgare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggygria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the regression models on the basis of the oregano-to-adulterants mixing ratio. Orthogonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled discrimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils. [ABSTRACT FROM AUTHOR]

Hydroxyl radicals (·OH) produced in subsurface sediments play an important role in biogeochemical cycles. One of the major sources of·OH in sediments is associated with reduced compounds (e.g., iron and organic matter) oxygenation. Moreover, the properties of iron forms and dissolved organic matter (DOM) components varied significantly across redox-fluctuation zones of estuaries. However, the influence of these variations on mechanisms of·OH production in estuaries remains unexplored. Herein, sediments from riparian zones, wetlands, and rice fields in the Yellow River Estuary were collected to systematically explore the diverse mechanisms of·OH generation. Rhythmic continuous·OH production (82–730 μmol/kg) occurred throughout the estuary, demonstrating notable spatial heterogeneity. The amorphous iron form and humic-like DOM components were the key contributors to·OH accumulation in estuary wetlands and freshwater restoration wetlands, respectively. The crystalline iron form and protein-like DOM components influenced the capabilities of iron reduction and continuous·OH production. Moreover, the orthogonal partial least squares models outperformed various multivariate models in screening crucial factors and predicting the spatiotemporal production of·OH. This study provides novel insights into varied mechanisms of·OH generation within distinct redox-fluctuation zones in estuaries and further elucidates elemental behavior and contaminant fate in estuarine environments. Given that estuaries serve as sinks for anthropogenic pollutants, various organic pollutants (e.g., emerging contaminants such as antibiotics) have been widely detected in estuarine environments. The production of·OH in sediments has been proven to affect the fate of contaminants. Therefore, the varied mechanisms of·OH in estuarine environments, dominated by diverse iron forms and DOM components, were explored in this study. MLR and OPLS models exhibited good performance in screening crucial factors and predicting·OH production. Our work highlights that in estuarine subsurface environments, the presence of·OH potentially leads to a natural degradation of pollutants. [Display omitted] • The generation and mechanism of·OH across varied esutarine redox-fluctuation zones were studied. • The production of·OH exhibited spatial heterogeneities from 82 to 730 μmol/kg. • Amorphous and crystalline iron phases were the key contributor and controller to·OH production. • DOM especially humic-like component was the main electron donor and shuttle in early oxygenation. • Predictive model based on OPLS for·OH production showed satisfactory performance. [ABSTRACT FROM AUTHOR]

Accurate electricity consumption (EC) forecasting is vital for ensuring efficient energy supply and underpinning long-term economic stability. Prior research has often overlooked the intricate interplay between the nuanced dynamics of economic growth and EC. In contrast, this study innovatively incorporates the economic cycle into the analytical framework of EC, providing a more comprehensive perspective. Firstly, we construct the MSAR-OPLS two-step model to discern the factors influencing EC across various cycles. We select the most fitting stabilization cycle as the foundation for forecasting. By integrating both dynamic and static scenario analyses, we offer a comprehensive and precise evaluation of future EC trends. This study ensures both the reliability and accuracy of our forecasts. Projections suggest that China's EC will escalate to 11,780 billion kWh by 2030, reflecting an average annual growth rate of 3.96% from 2023 to 2030. Furthermore, through rigorous robustness and sensitivity analyses, the study validates the forecast's accuracy. Beyond emphasizing the role of economic phases in ensuring energy security, this research furnishes policymakers with insights on embedding foresightful elements into strategic economic and energy planning. • Electricity consumption forecasting is a precondition for power planning. • Include economic cycles in electricity consumption forecasting. • Combine dynamic analysis to establish static forecasts for multiple scenarios. • Prevent power shortages or surpluses through influencing factors identification. [ABSTRACT FROM AUTHOR]

This paper deals with the effect of introducing an additional interaction site onto molecular-mechanical models of nitrogen-containing heterocyclic compounds. The introduction of only one additional site next to nitrogen atoms is shown to result in significant improvement of the quality of the models along with negligible slowdown of calculation speed. Concretely, it was proposed to introduce the site inside the aromatic ring at a distance of 0.4 Å from the nitrogen atom center. All the parametrization can be completely automated. The proposed force field allows predicting heats of evaporation of liquids of the compound under investigation with an accuracy of 1 kcal/mol. [ABSTRACT FROM AUTHOR]

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904–922 nm and 1675–1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

The weakened microstructure of archaeological wood (AW) objects from waterlogged environments necessitates consolidation to avoid anisotropic shrinkage upon drying. Polymer impregnation through submergence or spraying treatments is commonly applied, and for larger and thicker objects, the impregnation period can stretch over decades. Thus, for efficient treatment, continuous monitoring of the impregnation status is required. Today, such monitoring is often destructive and expensive, requiring segments for extraction and chromatographic quantification. This study proposes an in situ Raman spectroscopic method for quantification of polyethylene glycol (PEG) in waterlogged AW. A calibration model was built on standards of PEG, cellulose powder, and milled wood lignin using orthogonal partial least squares (OPLS). The OPLS model had a strong linear relationship, and the PEG content in wood of varying degrees of degradation could be determined. However, the accuracy of the model was low with a root mean square error of prediction of 11 wt%. The low accuracy was traced to the heterogeneity in the calibration and validation set samples with regard to the small probing volume of the confocal instrumental setup. [ABSTRACT FROM AUTHOR]

Vanadium peroxidases from marine algae are responsible for the production of ozone‐depleting compounds, volatile halogenated organic compounds (VHOC). Due to the impact the release of these compounds has on the global atmospheric and biogeochemical processes, there is an interest within marine sciences in developing analytical methods for studying the various aspects of the VHOC production, particularly in situ. This study aimed to provide new methods towards the development of in situ methods within marine sciences. We demonstrate the use of design of experiments together with orthogonal partial least squares (OPLS) and transposed orthogonal partial least squares (T‐OPLS) to address the qualitative spectral analysis of an enzyme‐buffer system. The measurements were performed with surface‐enhanced Raman spectroscopy (SERS) on vanadium bromoperoxisase from the red algae Corallina officinalis. The chemometric tools used aimed to provide greater insights into how factors such as time, amount of gold nanoparticles and enzyme concentration influence the spectral responses and whether there was any synergy between those factors. The results acquired in this report aim to support future method development of chemometrics within in situ applications in marine sciences. Herein, we demonstrate the use of design of experiments orthogonal partial least squares (OPLS) and its variations to address the qualitative analysis of a multicomponent system. The insight gained by us open up for possibilities in developing in vivo SERS methods combined with chemometrics. T‐OPLS combined with a designed experimental space allows separating relevant signals from a multicomponent matrix, which otherwise can be missed during spectral interpretation and OPLS modelling. [ABSTRACT FROM AUTHOR]

• NMR associate to multivariate calibration is an efficient method for the characterization of oils. • NMR is able to estimate various crude oil physicochemical properties from a single spectrum. • MPA strategies improved the quality of the multivariate model. This paper proposes a methodology to characterize the following petroleum properties: UOP characterization factor (K), nitrogen content, solubility parameter, sulfur content, maximum and minimum pour point, and kinematic viscosity (at 20 °C, 30 °C, 40 °C and 50 °C), in petroleum within a single 1H NMR (proton nuclear magnetic resonance) spectra. Multivariate calibration tools, such as partial least squares (PLS), orthogonal projections to latent structures (OPLS) (with variable selection), interval PLS (iPLS), synergy interval PLS (siPLS) and model population analysis (MPA), were applied to 138 samples. The models were evaluated by coefficient of determination (R2), root mean square error of prediction (RMSEP), and residuals. In general, model population analysis had better results in the determination of petroleum's physicochemical properties than PLS and OPS. Thus, MPA provided models with better accuracy in the determination of the following properties: UOP characterization factor (0.06), nitrogen content (0.048 wt%), kinematic viscosity at 40 °C (relative error of 21.9%) and pour point (14.58 °C and 19.2 °C, respectively). The sulfur content was estimated with a 0.09 wt% mean error of prediction, using the PLS model, and the solubility parameter with a 1.06 (MPa)1/2 mean error of prediction, using the OPLS model. [ABSTRACT FROM AUTHOR]

In this perspective, we review the theory and methodology of the derivation of force fields (FFs), and their validity, for molecular simulations, from their inception in the second half of the twentieth century to the improved representations at the end of the century. We examine the representations of the physics embodied in various force fields, their accuracy and deficiencies. The early days in the 1950s and 60s saw FFs first introduced to analyze vibrational spectra. The advent of computers was soon followed by the first molecular mechanics machine calculations. From the very first papers it was recognized that the accuracy with which the FFs represented the physics was critical if meaningful calculated structural and thermodynamic properties were to be achieved. We discuss the rigorous methodology formulated by Lifson, and later Allinger to derive molecular FFs, not only obtain optimal parameters but also uncover deficiencies in the representation of the physics and improve the functional form to account for this physics. In this context, the known coupling between valence coordinates and the importance of coupling terms to describe the physics of this coupling is evaluated. Early simplified, truncated FFs introduced to allow simulations of macromolecular systems are reviewed and their subsequent improvement assessed. We examine in some depth: the basis of the reformulation of the H-bond to its current description; the early introduction of QM in FF development methodology to calculate partial charges and rotational barriers; the powerful and abundant information provided by crystal structure and energetic observables to derive and test all aspects of a FF including both nonbond and intramolecular functional forms; the combined use of QM, along with crystallography and lattice energy calculations to derive rotational barriers about ɸ and ψ; the development and results of methodologies to derive "QM FFs" by sampling the QM energy surface, either by calculating energies at hundreds of configurations, or by describing the energy surface by energies, first and second derivatives sampled over the surface; and the use of the latter to probe the validity of the representations of the physics, reveal flaws and assess improved functional forms. Research demonstrating significant effects of the flaws in the use of the improper torsion angle to represent out of plane deformations, and the standard Lorentz-Berthelot combining rules for nonbonded interactions, and the more accurate descriptions presented are also reviewed. Finally, we discuss the thorough studies involved in deriving the 2nd generation all-atom versions of the CHARMm, AMBER and OPLS FFs, and how the extensive set of observables used in these studies allowed, in the spirit of Lifson, a characterization of both the abilities, but more importantly the deficiencies in the diagonal 12-6-1 FFs used. The significant contribution made by the extensive set of observables compiled in these papers as a basis to test improved forms is noted. In the following paper, we discuss the progress in improving the FFs and representations of the physics that have been investigated in the years following the research described above. [ABSTRACT FROM AUTHOR]

In the previous paper, we reviewed the origins of energy based calculations, and the early science of FF development. The initial efforts spanning the period from roughly the early 1970s to the mid to late 1990s saw the development of methodologies and philosophies of the derivation of FFs. The use of Cartesian coordinates, derivation of the H-bond potential, different functional forms including diagonal quadratic expressions, coupled valence FFs, functional form of combination rules, and out of plane angles, were all investigated in this period. The use of conformational energetics, vibrational frequencies, crystal structure and energetics, liquid properties, and ab initio data were all described to one degree or another in deriving and validating both the FF functional forms and force constants. Here we discuss the advances made since in improving the rigor and robustness of these initial FFs. The inability of the simple quadratic diagonal FF to accurately describe biomolecular energetics over a large domain of molecular structure, and intermolecular configurations, was pointed out in numerous studies. Two main approaches have been taken to overcome this problem. The first involves the introduction of error functions, either exploiting torsion terms or introducing explicit 2-D error correction grids. The results and remaining challenges of these functional forms is examined. The second approach has been to improve the representation of the physics of intra and intermolecular interactions. The latter involves including descriptions of polarizability, charge flux aka geometry dependent charges, more accurate representations of spatial electron density such as multipole moments, anisotropic nonbond potentials, nonbond and polarization flux, among others. These effects, though not as extensively studied, likely hold the key to achieving the rigorous reproduction of structural and energetic properties long sought in biomolecular simulations, and are surveyed here. In addition, the quality of training and validation observables are evaluated. The necessity of including an ample set of energetic and crystal observables is emphasized, and the inadequacy of free energy as a criterion for FF reliability discussed. Finally, in light of the results of applications of the two approaches to FF development, we propose a "recipe" of terms describing the physics of inter and intramolecular interactions whose inclusion in FFs would significantly improve our understanding of the energetics and dynamics of biomolecular systems resulting from molecular dynamics and other energy based simulations. [ABSTRACT FROM AUTHOR]

Highlights • Multivariate analysis (MVA) can be used for elucidation of structure information not seen in FTIR by eye. • Water of hydration, changes in backbone conformation and increase of hydroxypropylation and methylation signals was verified. • Hydrophobized Methyl arabinoxylan has a higher water holding capacity of hydration than Hydroxypropyl arabinoxylan. • MVA can be used as a tool for hypothesis generation providing keen empirical observations of structural features. Abstract Hemicelluloses are a relatively unused renewable resource. One reason is their broad structure variety that makes it hard to understand structure-property relations. In this study arbinoxylan, extracted from barley husk, was chemically modified into hydroxypropyl methyl-, hydroxypropyl- and methyl arabinoxylan. The relationship between structure and phase behavior was investigated by using multivariate analysis. The arabinoxylan ethers were characterized using mid-infrared FTIR spectroscopy and from principal components analysis, PCA, structural or physical variations between samples were visualized. With orthogonal projections to latent structures, OPLS, vibrations specific for arabinoxylan hydroxypropyl and methyl substitutions was assigned. Among the observed differences between chemical derivatives was an intensity change in the water vibration. The differences in hydration were related to clouding phase behavior of the arabinoxylan ethers. This study shows that multivariate analysis methods are useful for finding unexpected and/or hidden features in the polysaccharide structure. [ABSTRACT FROM AUTHOR]

The green alga Caulerpa cylindracea is a non-autochthonous and invasive species that is severely affecting the native communities in the Mediterranean Sea. Recent researches show that the native edible fish Diplodus sargus actively feeds on this alga and cellular and physiological alterations have been related to the novel alimentary habits. The complex effects of such a trophic exposure to the invasive pest are still poorly understood. Here we report on the metabolic profiles of plasma from D. sargus individuals exposed to C. cylindracea along the southern Italian coast, using 1H NMR spectroscopy and multivariate analysis (Principal Component Analysis, PCA, Orthogonal Partial Least Square, PLS, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA). Fish were sampled in two seasonal periods from three different locations, each characterized by a different degree of algal abundance. The levels of the algal bisindole alkaloid caulerpin, which is accumulated in the fish tissues, was used as an indicator of the trophic exposure to the seaweed and related to the plasma metabolic profiles. The profiles appeared clearly influenced by the sampling period beside the content of caulerpin, while the analyses also supported a moderate alteration of lipid and choline metabolism related to the Caulerpa-based diet.

Abstract Cognitive aging is highly complex. We applied a data-driven statistical method to investigate aging from a hierarchical, multidimensional, and multivariate approach. Orthogonal partial least squares to latent structures and hierarchical models were applied for the first time in a study of cognitive aging. The association between age and a total of 316 demographic, clinical, cognitive, and neuroimaging measures was simultaneously analyzed in 460 cognitively normal individuals (35–85 years). Age showed a strong association with brain structure, especially with cortical thickness in frontal and parietal association regions. Age also showed a fairly strong association with cognition. Although a strong association of age with executive functions and processing speed was captured as expected, the association of age with visual memory was stronger. Clinical measures were less strongly associated with age. Hierarchical and correlation analyses further showed these associations in a neuroimaging-cognitive-clinical order of importance. We conclude that orthogonal partial least square and hierarchical models are a promising approach to better understand the complexity in cognitive aging. [ABSTRACT FROM AUTHOR]

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics. [ABSTRACT FROM AUTHOR]

Although the protein backbone is the most fundamental part of the structure, the fine‐tuning of side‐chain conformations is important for protein function, for example, in protein‐protein and protein‐ligand interactions, and also in enzyme catalysis. While several benchmarks testing the performance of protein force fields for side chain properties have already been published, they often considered only a few force fields and were not tested against the same experimental observables; hence, they are not directly comparable. In this work, we explore the ability of twelve force fields, which are different flavors of AMBER, CHARMM, OPLS, or GROMOS, to reproduce average rotamer angles and rotamer populations obtained from extensive NMR studies of the 3J and residual dipolar coupling constants for two small proteins: ubiquitin and GB3. Based on a total of 196 μs sampling time, our results reveal that all force fields identify the correct side chain angles, while the AMBER and CHARMM force fields clearly outperform the OPLS and GROMOS force fields in estimating rotamer populations. The three best force fields for representing the protein side chain dynamics are AMBER 14SB, AMBER 99SB*‐ILDN, and CHARMM36. Furthermore, we observe that the side chain ensembles of buried amino acid residues are generally more accurately represented than those of the surface exposed residues. [ABSTRACT FROM AUTHOR]

The effects of the configuration and temperature on the Young’s modulus of poly (methyl methacrylate) (PMMA) have been studied using molecular dynamics simulations. For the DREIDING force field under ambient temperatures, increasing the number of monomers significantly increases the modulus of isotactic and syndiotactic PMMA while the isotactic form has a greater modulus. The effects of temperature on the modulus of isotactic PMMA have been simulated using the DREIDING, AMBER, and OPLS force fields. All these force fields predict the effects of temperature on the modulus from 200 to 350 K that are in close agreement with experimental values, while at higher temperatures the moduli are greater than those measured. The glass transition temperature determined by the force fields, based on the variation of the modulus with temperature, is greater than the experimental values, but when obtained from a plot of the volume as a function of the temperature, there is closer agreement. The Young’s moduli calculated in this study are in closer agreement to the experimental data than those reported by previous simulations. [ABSTRACT FROM AUTHOR]

Acetylcholinesterase (AChE) is considered as one of the most favored drug targets for Alzheimer’s disease. The effects of different force fields (FFs) on ranking affinity of acetylcholinesterase inhibitors were obtained using the fast pulling of ligand (FPL) method in steered-molecular dynamics (SMD) simulations. GROMOS, AMBER, CHARMM, and OPLS-AA FFs were investigated in this work. The pulling work derived with GROMOS FF has the strongest correlation and smallest error compared with experimental binding affinity. Moreover, the CPU consumption in the calculations using GROMOS FF is the lowest, which could allow us to rank affinity of a large number of AChE ligands. [ABSTRACT FROM AUTHOR]

• A soft sensor capable of predicting the trace concentration of chlorine dioxide in water was developed. • FTIR spectra, in combination with a data-based model, was employed to develop soft sensor. • Six distinct artificial neural network models were developed and evaluated based on statistical standards. • The most proficient OPLS-RF model demonstrated LOD and LOQ values of 0.1 and 0.25 ppm for water samples. Chlorine dioxide (ClO 2) is a widely used sterilizer and a disinfectant across a multitude of industries. When using ClO 2 , it is imperative to measure the ClO 2 concentration to abide by the safety regulations. This study presents a novel, soft sensor method based on Fourier transform infrared spectroscopy (FTIR) spectroscopy for measurement of ClO 2 concentration in different water samples varying from milli Q to wastewater. Six distinct artificial neural network models were constructed and evaluated based on three overarching statistical standards to select the optimal model. The OPLS-RF model outperformed all other models with R2, RMSE, and NRMSE values of 0.945, 0.24, and 0.063, respectively. The developed model demonstrated limit of detection and limit of quantification values of 0.1 and 0.25 ppm, respectively, for water. Furthermore, the model also exhibited good reproducibility and precision as measured by the BCMSEP (0.064). The soft sensor-based method presented in the study offers significant advantages in terms of simplicity and speedy detection. In summary, the study presents development of a soft sensor that is capable of predicting the trace content of chlorine dioxide ranging between 0.1 to 5 ppm in a water sample by connecting FTIR with an OPLS-RF model. [Display omitted] [ABSTRACT FROM AUTHOR]

زمینه و هدف: دینامیک مولکولی، روشی برای شبیه‌سازی رفتار ترمودینامیکی مواد در سه فاز جامد، مایع و گاز با استفاده از نیرو، سرعت و مکان ذرات می‌باشد. دربین این عوامل، مهمترین عامل، نیرو است. در شبیه‌سازی دینامیک مولکولی کلاسیک، نیرو از پتانسیل کلاسیک به‌دست‌می‌آید. پتانسیل کلاسیکی، تابعی از مکان اتم‌ها یا هسته‌هاست و به موقعیت الکترون‌ها در اتم‌ها وابسته نیست. هدف از این کار، مطالعه و مقایسه‌ی انرژی محاسبه‌ شده برای چند پروتئین مهم بیولوژیکی بوده ‌است. مواد و روش‌ها: شبیه‌سازی دینامیک مولکولی، روشی مناسب برای مدل‌سازی میکروسکوپی در مقیاس اتمی و مولکولی فراهم‌می‌کند. محاسبات روی یک کامپیوتر شخصی با برنامه‌ی هایپر کم انجام‌شد. ژئومتری بدون هیچ تغییری انجام شد و این امکان فراهم گردید که تمام اتم‌ها، پیوندها و زوایای دی هدرال به‌طور خود به‌خود تغییرکند. یافته‌ها: انرژی نهایی ساختمان سه پروتئین در شبیه‌سازی‌های مونت کارلو، دینامیک مولکولی و دینامیک لانگوین انجام شد. بهینه‌کردن ساختار هندسی و برهم‌کنش انرژی‌های محاسبه‌شده با روش‌های مختلف برای چند پروتئین شامل گیرنده‌ی فاکتور رشد عصبی و آنزیم پروتئینی موثر در یادگیری مقایسه‌شد. نتیجه‌گیری: در شبیه‌سازی دینامیک مولکولی کوانتومی، نیرو از پتانسیل کلاسیکی و معادله‌ی الکترونی شرودینگر محاسبه می‌شود. شبیه‌سازی کامپیوتری با معرفی روش‌های غیرتعادلی در تعیین خواص انتقالی و درنظرگرفتن اثرات مکانیک کوانتومی توسعه‌یافته‌است. انرژی پتانسیل و درجه‌ی حرارت درطی شبیه‌سازی‌ها، تقریباً ثابت هستند که این خود، نشان‌دهنده‌ی پایداری ساختمان این پروتئین‌ها در دماهای ذکر شده‌ می‌باشد.