Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination

This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of α-linolenic and linoleic acid in eight types of edible vegetable oils and their blending. For this purpose, a combination of spectral wavelength selection by wavelet transform (WT) and elimination of uninformative variables (UVE) was proposed to obtain simple partial least square (PLS) models based on a small subset of wavelengths. WT was firstly utilized to compress full NIR spectra which contain 1413 redundant variables, and 42 wavelet approximate coefficients were obtained. UVE was then carried out to further select the informative variables. Finally, 27 and 19 wavelet approximate coefficients were selected by UVE for α-linolenic and linoleic acid, respectively. The selected variables were used as inputs of PLS model. Due to original spectra were compressed, and irrelevant variables were eliminated, more parsimonious and efficient model based on WT-UVE was obtained compared with the conventional PLS model with full spectra data. The coefficient of determination (r²) and root mean square error prediction set (RMSEP) for prediction set were 0.9345 and 0.0123 for α-linolenic acid prediction by WT-UVE-PLS model. The r² and RMSEP were 0.9054, 0.0437 for linoleic acid prediction. The good performance showed a potential application using WT-UVE to select NIR effective variables. WT-UVE can both speed up the calculation and improve the predicted results. The results indicated that it was feasible to fast determine α-linolenic acid and linoleic acid content in edible oils using NIR spectroscopy.     

Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding ¹H and ¹³C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (D_LR), viscosity (V_LR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, ¹³C NMR and ¹H NMR spectra and on 3 sets of merged spectra, i.e., IR + ¹H NMR, IR + ¹³C NMR and IR + ¹H NMR + ¹³C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, ¹³C NMR and ¹H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input ¹³C NMR or ¹H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.     

Chemometrics-Assisted Optimization of Beta-/Gamma-Tocol Separation on a C<Subscript>30</Subscript> Stationary Phase in Reversed-Phase LC

This paper presents a chemometrics-assisted optimization study to improve the separation of tocopherol (-T) and tocotrienol (-TT) homologues on a C₃₀ stationary phase in reversed-phase HPLC. The HPLC settings were optimized using a central composite design and the response surface methodology. Flow rate, column temperature, and mobile phase composition were chosen as independent variables. Peak resolution (R_s), analysis time (t_R), and peak symmetries of the tocopherol isomers were chosen as response variables. Optimum performance in terms of R_s was obtained at a flow rate of 0.31 mL min^?1, a temperature of 8.70 °C, and % B content (methyl tert-butyl ether: methanol: water, 80:18:2, v/v/v) in the mobile phase of 38.12%. The analysis of variance and regression analysis gave adjusted R² values of 0.9841 for R_s, 0.9850 for t_R-(α-T), 0.9853 for t_R-(β-T), and 0.9204 for the peak symmetry of β-T. This confirms the good agreement of experimental data with predicted values. The close eluting peaks of β-/γ-tocol could be baseline separated at the optimized conditions at a minimized analysis time. Empirical second-order polynomial models were derived that gave statistically high significances (P?<?0.0001). Hence, the models can be successfully employed to predict the optimum separation conditions of co-eluting peaks of β-/γ-tocols. The optimized method was successfully applied to determine the individual tocol homologues in various cold pressed edible oils. Total contents ranged from 15 to almost 2600 mg tocol kg^?1 oil.     

Characterization and quantification of secondary metabolite profiles in leaves of red and white clover species by NIR and ATR-IR spectroscopy

Different varieties of two clover species (Trifolium pratense L. and Trifolium repens L.), cultivated in 2008 and 2009 were analysed by near-infrared (NIR) and mid-infrared (MIR) spectroscopy for establishing a fast and reliable quantification protocol for isoflavones and phenolic acids. Based on HPLC–UV/MS reference data, good results were obtained by PLS regression for the prediction of total isoflavone (R² = 0.90) as well as for glycitin content (R² = 0.88). Because of the lower concentration of formononetin and phenolic acids, their prediction quality was generally slightly lower (R² = 0.73 and R² = 0.64, respectively) compared to those of the isoflavones. The applicability of ‘leave one out’ cross validation for such a large data set is proven by comparison to an averaged randomized test-set validation leading to similar results. Additionally, the large sample set (n = 624) was screened by hierarchical cluster analysis allowing a fast evaluation of influences resulting from different cultivation parameters on the isoflavone and phenolic acid content. Climatic changes (cultivation year, date of harvest) seem to have the most impact on the metabolic profile as indicated by higher variability in the referring spectra when both cultivation years were simultaneously regarded. This work offers a new vibrational spectroscopic approach for the qualitative and quantitative determination of isoflavone and phenolic acid profiles, directly performed in the plant material without any laborious sample preparation and time-consuming chromatography. Once validated by HPLC reference, MIR and NIR spectroscopy can be used for the reliable prediction of secondary metabolites in clover as well as for fast screening and pre-evaluation of the diversity of a large sample set, aiming to reduce analytical costs, chemical waste and expenditure of time.     

Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions

In this work, we developed artificial intelligence-based models for prediction and correlation of CO₂ solubility in amino acid solutions for the purpose of CO₂ capture. The models were used to correlate the process parameters to the CO₂ loading in the solvent. Indeed, CO₂ loading/solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R² scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R² Score of 0.998. With an R² of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R² of 0.9.     

Synthesis of BINOL-containing oxacalix[4]arenes

Unprecedented chiral oxacalixarenes were obtained from the reaction of (±)-1,1′-binaphthyl-2,2′-diol (BINOL) with 1,5-difluoro-2,4-dinitrobenzene. Structural elucidation of C_2h-symmetric meso (R,S)-oxacalix[4]arene and D₂-symmetric racemic (as well as enantiopure) (R,R)- and (S,S)-oxacalix[4]arene was carried out by means of 1D and 2D NMR spectroscopy, ESI-MS, X-ray diffractometry, and circular dichroism spectroscopy.     

Determination of the enantiomeric purity and the configuration of β-aminoalcohols using (R)-2-fluorophenylacetic acid (AFPA) and fluorine-19 NMR: application to β-blockers

A method has been developed for determining the enantiomeric purity and the absolute configuration of β-aminoalcohols of type ArOCH₂CH(OH)CH₂NHR (R=iPr, tBu). To determine enantiomeric purity, the amine function was first protected by a benzyl group, then the compound formed was esterified using the acid chloride of (R)-2-fluorophenylacetic acid (AFPA). The ¹⁹F NMR analysis of the derivative obtained revealed the presence of two distinctly separate signals (∼2.5 ppm), the one for the RS–SR pair being the most deshielded. The configuration was determined directly on the aminoalcohol by using the acid. In stoichiometric conditions, when R=iPr, the amide function was obtained very preponderantly. The ¹⁹F NMR spectrum of the amide presented four distinct signals when derivatization was carried out by means of a reaction between the (±)-β-aminoalcohol and the (R)-AFPA. The extreme signals, which were over 3.5 ppm apart, did not belong to the same diastereomer. With R=tBu essentially the ester function was obtained. The first studies revealed the presence of two signals, though not as clearly separated as in the previous cases. Each experiment was simple to perform, and purification was not necessary. Mosher's acid gave unsatisfactory results in each case.     

Partial least squares modeling of combined infrared, H NMR and C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding ¹H and ¹³C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (D_LR), viscosity (V_LR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, ¹³C NMR and ¹H NMR spectra and on 3 sets of merged spectra, i.e., IR + ¹H NMR, IR + ¹³C NMR and IR + ¹H NMR + ¹³C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, ¹³C NMR and ¹H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input ¹³C NMR or ¹H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.     

Application of potentiometric stripping analysis with constant inverse current for determining soluble lead in human teeth

A method for the determination of soluble lead in human teeth by potentiometric stripping analysis with constant inverse current in the analytic step (PSA-i_R), is described. The metal ions were concentrated as their amalgams on the glassy carbon surface of a working electrode that was previously coated with a thin mercury film and then stripped by a suitable oxidant. This paper examined effect of various factors on the PSA-i_R results including the electrolysis potential, the solution stirring rate, and the constant inverse current. Quantitative analysis was carried out by both standard addition and calibration curve methods; a good linearity was obtained in the concentration range from 5 to 25 μg/dm³. A detection limit of 0.64 μg/dm³ was obtained, with a 5.21% coefficient of variation. Results obtained for teeth were not significantly different from these obtained by flameless atomic absorption spectrophotometry (FAAS).     

Potential of visible-near infrared spectroscopy combined with chemometrics for analysis of some constituents of coffee and banana residues

Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B + C). The precision of NIR methodology was better (p < 0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R² < 0.80), the models for all the analytes exhibited R² > 0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies.     

Prediction of humic acid content and respiration activity of biogenic waste by means of Fourier transform infrared (FTIR) spectra and partial least squares regression (PLS-R) models

Fourier transform infrared (FTIR) spectroscopy has been proven to be an appropriate analytical method for the qualitative assessment of compost stability. This study focuses on quantitative determination of two time-consuming parameters: humic acid (HA) contents and respiration activity. Reactivity/stability and humification were quantified by respiration activities (oxygen uptake) and humic acid contents. These features are also reflected by a specific infrared spectroscopic pattern. Based on this relationship partial least squares regression (PLS-R) models for the prediction of respiration activities and humic acid contents were calculated. Characteristic wavenumber regions that are assigned to the biological/chemical parameter were selected for multivariate data analysis. The coefficient of determination (R²) obtained for the humic acid prediction model from infrared spectra was 87% with a root mean square error of cross-validation (RMSECV) of 2.6% organic dry matter (ODM). The prediction model for respiration activity resulted in a R² of 94% and a RMSECV for oxygen uptake of 2.9 mg g⁻¹ dry matter (DM).     

Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content

The utility of near infrared spectroscopy as a non-invasive technique for the assessment of internal eating quality parameters of stonefruit (peaches, nectarines and plums) was assessed. Calibration model performance for the attributes of total soluble solids (TSS) was encouraging (typical R² > 0.88, RMSECV 0.53-0.88%TSS, SDR_CV 2.9-3.7). Model performance was acceptable using a combined multi-variety peach-nectarine data set, but it was advantageous to maintain a separate multi-variety plum model. Model robustness to temperature was achieved by including into the calibration set samples scanned at a range of temperatures, with less than 5% of total population required to be treated in this way. Similarly, where models incorporated the range of TSS seen in the validation population, prediction performance was good. Model performance was stable over several seasons in terms of R² (typical R² > 0.8), with bias corrected SEP varying in proportion to population S.D. Prediction bias for new populations could be corrected by model updating or direct bias adjustment.     

Carbon monoxide isotope enrichment and separation by pressure swing adsorption

Simulations of three different 3-bed 3-step pressure swing adsorption (PSA) cycles were carried out to study the enrichment and recovery of ¹⁴CO from an isotopic mixture of ¹⁴CO, ¹³CO and ¹²CO using NaX zeolite. Each PSA cycle included feed pressurization/feed (FP/P), heavy reflux (HR) and countercurrent depressurization (CnD) steps; they differed only in the way the CnD step was carried out: PSA Cycle I was carried out under total reflux (i.e., with no ¹⁴CO heavy product production); PSA Cycle II was carried out with discontinuous ¹⁴CO heavy product production; and PSA Cycle III was carried out with continuous ¹⁴CO heavy product production. The effects of the CnD step valve coefficient (c _v ), heavy reflux ratio (R _R ), and cycle time (t _cyc ) on the PSA process performance were determined in terms of the ¹⁴CO enrichment, ¹⁴CO recovery and CO feed throughput. The results showed that there was essentially no limit to the extent of the ¹⁴CO enrichment, despite the inherently low ¹⁴CO/¹²CO (1.05) and ¹⁴CO/¹³CO (1.12) separation factors for these isotopes on NaX zeolite. Under total reflux an optimum c _v was found for the CnD step and ¹⁴CO enrichments as high as 152 were obtained. Using the optimum c _v under finite reflux, a ¹⁴CO enrichment approaching 20 and a ¹⁴CO recovery approaching 100 % were easily achieved with discontinuous (PSA Cycle II) or continuous (PSA Cycle III) ¹⁴CO heavy product production. There was essentially no difference in the performance of PSA Cycles II and III, a counterintuitive result. The ¹⁴CO enrichment and the ¹⁴CO recovery both increased with decreasing CO feed throughputs and higher R _R , which were always very close to unity.     

Main fatty acid classes in vegetable oils by SB-ATR-Fourier transform infrared (FTIR) spectroscopy

The prospect of using single bounce attenuated total reflectance (SB-ATR)-Fourier transform infrared (FTIR) spectroscopy as a rapid quantitative tool to determine the main fatty acid groups present in different edible oils was investigated. Partial least squares (PLS) calibrations were developed using SB-ATR-FTIR spectra which were associated with fatty acid groups (saturated, trans, mono- and polyunsaturated) using quantitative data obtained by gas chromatography (GC). Good calibrations were obtained for all main four fat groups (saturated, mono, trans and polyunsaturated) with excellent precision. The coefficient of determination (R²), root mean square error of prediction (RMSEP) and bias for validation set were obtained as 0.999, 2.43 and 0.998 for saturated; 0.999, 1.850 and 0.003 for mono; 0.999, 0.625 and −0.001 for trans while for poly the values were 0.999, 1.170 and 0.003, respectively. The results of 13 validation samples for total saturated, mono, trans and polyunsaturated fats by FI-IR were found in the range of 8.16-55.16, 37.62-74.75, 0.20-18.16 and 1.36-62.35%, respectively. The present study shows that it may well be possible to expand the utility of SB-ATR-FTIR spectroscopy not only to provide isolated trans data, but also serve as a simple, rapid and quantitative means of categorizing the main groups present in the edible oils. The information obtained would be useful for meeting the new lipid nutritional labeling requirements.     

Pharmacophore elucidation and 3D-QSAR analysis of a new class of highly potent inhibitors of acid ceramidase based on maximum common substructure and field fit alignment methods

Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) studies have been carried on a series of 2,4-dioxopyrimidine-1-carboxamides as acid ceramidase inhibitors. Two alignment rules for the compounds were defined using maximum common substructure and field fit. The best orientation was then searched by all-orientation search strategy, to minimize the effect of the initial orientation of the structures. The Kennard Stone algorithm was used to divide the entire set into training (25 compounds) and test (7 compounds) sets. Pharmacophore model identification was also performed using DISCOtech algorithm and refinement was carried out using GASP, to highlight important structural features that could be responsible for the inhibitory activity. All constructed models showed appropriate statistical parameters in terms of q ² and r _pred ² . Based upon the information obtained from CoMFA, CoMSIA, and developed pharmacophore pattern, some key features that may be used to design new inhibitors for acid ceramidase have been identified.     

Pt(II) stereoisomeric complexes with serine

There have been synthesized Pt(II) stereoisomeric complexes with hydroxy-α-amino acid serine (SerH = NH₂CH(CH₂OH)COOH is α-amino-β-hydroxypropionic acid): trans-[Pt(S-SerH)₂Cl₂], trans-[Pt(R-SerH)(S-SerH)Cl₂] with monodentately (through NH₂ group ) bound SerH and cis-, trans-[Pt(R-Ser)(S-Ser)], trans-[Pt(S-Ser)₂] with bidentately bound (through groups NH₂ and COO) ligands (R, S is the absolute configuration of asymmetric carbon atom). The successive phases in the synthesis of Pt(II) stereoisomeric complexes with serine were studied by ¹⁹⁵Pt NMR spectroscopy. To identificate the compounds synthesized the method of elemental analysis, IR and NMR (¹⁹⁵Pt, ¹³C, ¹H) spectroscopy were used. For trans-[Pt(R-Ser)(S-Ser)] the X-ray diffraction data were obtained.     

Direct measurement of proton dissociation in thhe excited state of protonated 1-aminopyrene with picosecond pulses

Direct measurement of proton dissociation in the excited singlet state of protonated I-aminopyrene in a mixture of H₂O (or D₂O) and acetonitrile (1:1) has been carried out by means of picosecond time-resolved spectroscopy. The proton dissociation retes k¹₁ and k^D+₁ at about 300 K were determined tobe 3.7 (±0.6) * 10⁹ s^-1, respectively. These rates are in very good agreement with those obtained by nanosecond time-resolved spectroscopy with fluorometry. The excited singlet state ;K^*₂ value of l-aminopyrene was also determined by dynamic analyses.     

Tetragonal distortions and vibronic coupling in hexaflouro nickel(II) and copper (II) complexes

Two parameters, axial (R_a) and equatorial (R_e) metal-ligand distances, describe the tetragonal distortions of the NiF^4?₆ and CuF^4?₆ complexes under study. The experimental values of R_a plotted vs R_e exhibit a smooth curve type dependence valid for MF₆ chromophore of solid state compounds. In the model study it becomes explained by analyzing the shape of the adiabatic potential surface (APS) of the form of E_T(R_a, R_e). Around the energy minima the map of numerical values (obtained by the quantum-chemical CNDOUHF and INDOUHF methods) was transformed to an analytic form from which the quantitative characteristics of APS were obtained in a new, simple way. The set of equatorial-axial parameters as well as the set of vibronic parameters were evaluated and discussed in more detail.