Chemometrics tools used in analytical chemistry: An overview

Characterization of engine lubricants by fluorescence spectroscopy and chemometrics

In this study, principal component analysis (PCA) and parallel factor analysis (PARAFAC) combined with excitation-emission matrix fluorescence (EEMF) were used to determine the most efficient excitation wavelengths of engine lubricants; identify their fluorophores; classify them and look for correlations between their fluorescence and their physical parameters. EEMF spectra were obtained for the different samples in the range of 260 to 600 nm, and 300 to 700 nm for excitation and emission wavelengths respectively. PCA and PARAFAC showed that the efficient excitation wavelengths for engine lubricants are 300, 350, 400, 450 and 470 nm. These five wavelengths represented the maxima of the PARAFAC recovered excitation profiles, of which two were attributed to fluorene and pyrene. The relative proportions of the PARAFAC retrieved components were used to classify engine lubricants with a satisfactory percentage of classification of 70%. Finally, a good correlation was obtained between some physical parameters (particularly the viscosity) of engine lubricants and their fluorescence.

Inline Raman Spectroscopy and Indirect Hard Modeling for Concentration Monitoring of Dissociated Acid Species

We propose an approach for monitoring the concentration of dissociated carboxylic acid species in dilute aqueous solution. The dissociated acid species are quantified employing inline Raman spectroscopy in combination with indirect hard modeling (IHM) and multivariate curve resolution (MCR). We introduce two different titration-based hard model (HM) calibration procedures for a single mono- or polyprotic acid in water with well-known (method A) or unknown (method B) acid dissociation constants pK_a. In both methods, spectra of only one acid species in water are prepared for each acid species. These spectra are used for the construction of HMs. For method A, the HMs are calibrated with calculated ideal dissociation equilibria. For method B, we estimate pK_a values by fitting ideal acid dissociation equilibria to acid peak areas that are obtained from a spectral HM. The HM in turn is constructed on the basis of MCR data. Thus, method B on the basis of IHM is independent of a priori known pK_a values, but instead provides them as part of the calibration procedure. As a detailed example, we analyze itaconic acid in aqueous solution. For all acid species and water, we obtain low HM errors of < 2.87 × 10^-4mol mol^-1 in the cases of both methods A and B. With only four calibration samples, IHM yields more accurate results than partial least squares regression. Furthermore, we apply our approach to formic, acetic, and citric acid in water, thereby verifying its generalizability as a process analytical technology for quantitative monitoring of processes containing carboxylic acids.

Inorganic Elements in Mytilus galloprovincialis Shells: Geographic Traceability by Multivariate Analysis of ICP-MS Data

The international seafood trade is based on food safety, quality, sustainability, and traceability. Mussels are bio-accumulative sessile organisms that need regular control to guarantee their safe consumption. However, no well-established and validated methods exist to trace mussel origin, even if several attempts have been made over the years. Recently, an inorganic multi-elemental fingerprint coupled to multivariate statistics has increasingly been applied in food quality control. The mussel shell can be an excellent reservoir of foreign inorganic chemical species, allowing recording long-term environmental changes. The present work investigates the multi-elemental composition of mussel shells, including Al, Cu, Cr, Zn, Mn, Cd, Co, U, Ba, Ni, Pb, Mg, Sr, and Ca, determined by inductively-coupled plasma mass-spectrometry in Mytilus galloprovincialis collected along the Central Adriatic Coast (Marche Region, Italy) at 25 different sampling sites (18 farms and 7 natural banks) located in seven areas. The experimental data, coupled with chemometric approaches (principal components analysis and linear discriminant analysis), were used to create a statistical model able to discriminate samples as a function of their production site. The LDA model is suitable for achieving a correct assignment of >90% of individuals sampled to their respective harvesting locations and for being applied to counteract fraud.

Glassy Carbon Electrode Modified with C/Au Nanostructured Materials for Simultaneous Determination of Hydroquinone and Catechol in Water Matrices

The simultaneous determination of hydroquinone and catechol was conducted in aqueous and real samples by means of differential pulse voltammetry (DPV) using a glassy carbon electrode modified with Gold Nanoparticles (AuNP) and functionalized multiwalled carbon nanotubes by drop coating. A good response was obtained in the simultaneous determination of both isomers through standard addition to samples prepared with analytical grade water and multivariate calibration by partial least squares (PLS) in winery wastewater fortified with HQ and CT from 4.0 to 150.00 µM. A sensitivity of 0.154 µA µM−1 and 0.107 µA µM−1, and detection limits of 4.3 and 3.9 µM were found for hydroquinone and catechol, respectively. We verified the reliability of the developed method by simultaneously screening analytes in spiked tap water and industrial wastewater, achieving recoveries over 80%. In addition, this paper demonstrates the applicability of chemometric tools for the simultaneous quantification of both isomers in real matrices, obtaining prediction errors of lower than 10% in fortified wastewater.

Applications of QbD-based Software’s in Analytical Research and Development

Background:

Quality by design-based software’s in analytical research and development normally encompasses multiple objectives. For decades, this task has been attempted through trial and error, supplemented with the previous experience, knowledge, and wisdom of analytical researchers.

Objective:

The study analyzes the current QbD-assisted software’s, such as design-experts, minitab, fusion product development, etc., and its broad implementations in an analytical research and development.

Methods:

The traditional approach may fails to meet the intended purpose by trial and error procedure during analytical research and development. However, modern scientific technology is equipped with highly advanced features associated with the software of the QbD paradigm. The impact and interactions between the critical process variables and critical method attributes such as resolution, tailing, etc. can be well understood by the screening, optimization, and robustness studies based on the principles of experimental design.

Results:

The design of experiments assimilate statistical multi-variate analysis instead of one factor at a time approach. This also provides a prominent, most reliable quality output, which is also essential for getting highly robust method as well as to obtain homogenous product development.

Conclusion:

The present review, critically discussed about the various QbD based multivariate software and their applications in drug development and analytical research.

Evaluation of VEGF mediated pro-angiogenic and hemostatic effects and chemical marker investigation for Typhae Pollen and its processed product

ETHNOPHARMACOLOGICAL RELEVANCE

Typhae Pollen (TP) is a well-known Traditional Chinese Medicine (TCM) to remove blood stasis. Carbonized Typhae Pollen (CTP), a processed product of TP after being stir-fried, has been widely applied to clinical practice with its capability of hemostasis. However, the underlying mechanism of TP and CTP are still not fully elucidated and discrimination against TP and CTP remains a challenge.

AIM OF STUDY

The aim of this study is to investigate whether TP could remove blood stasis by promoting angiogenesis and the process of carbonizing it could enhance hemostatic effect. Meanwhile, some chemical markers for quality control of CTP had better to be found.

MATERIAL AND METHODS

The changes of constituents between TP and CTP were analyzed by UPLC-QTOF-MS/MS. We investigated pro-angiogenic and hemostatic effects of TP and CTP in two zebrafish models: VRI-induced ISV insufficiency model and Ator-induced cerebral hemorrhage model. Subsequently, quantitative real-time PCR (qRT-PCR) was applied to investigate the mechanism of pharmacological effects. Finally, chemometric method was applied to find chemical markers.

RESULTS

A total of 19 compounds were identified in qualitative analysis. The loss rate of each compound was calculated and compared. Two compounds (huaicarbon A/B) could only be detected in CTP and the content of flavonoid glycosides in CTP was significantly decreased compared with TP. The average content of the three identified flavonoid aglycones (quercetin, isorhamnetin and kaempferol) was increased about 30 percent in CTP. TP promoted pro-angiogenesis by up-regulating the expression of VEGFA, flt1 and kdr. After heating process, the pro-angiogenic activity was reduced and hemostatic activity was enhanced in CTP. Then qRT-PCR analysis found that CTP could significantly up-regulate the expression of VEGFA and vWF. In the discovery of markers, 6 chemical markers for discrimination of TP and CTP were obtained by chemometric method.

CONCLUSION

Our research indicated that the pro-angiogenic activity of TP was involved in VEGF signaling pathway. After processing, hemostatic activity of CTP has been enhanced by up-regulating the expression of VEGFA and vWF. A chemical marker database was established to provide a scientific evidence for quality control, mechanism and the clinical application of TP and CTP.

PARAFAC study of L-cys@CdTe QDs interaction to BSA, cytochrome c and trypsin: An approach through electrostatic and covalent bonds

Utilizing fluorescence spectroscopy, non-covalent and covalent interactions of L-cys@CdTe quantum dots to bovine serum albumin (BSA), cytochrome c and trypsin were investigated. L-cys@CdTe QDs with the emission maximum at 530 nm and an average diameter of 2.6 nm were synthesized in the aqueous medium. Formaldehyde, N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride (EDC) with N-hydroxysuccinimide (NHS), and glutaraldehyde was applied as cross-linkers. In the case of both electrostatic and covalent strategies PARAFAC, as a powerful multi-way chemometrics technique, was utilized to analyze fluorescence excitation-emission (EEM) spectra. For non-covalent and covalent bonding, two and three significant components composed the PARAFAC models. Resolved EEM shows that in the presence of formaldehyde, a new component with an emission peak similar to BSA was obtained. Using EDC-NHS cross-linker, the fluorescence peak of the newly formed component was in a distinct wavelength with similar emission intensity, compared to L-cys@CdTe QDs and BSA. Employing glutaraldehyde, a distinguished component was easily detected at emission wavelengths higher than that of L-cys@CdTe QDs and proteins. It was concluded that the choice of cross-linker is a critical step to create different emission spectra when dealing with nano-bio-conjugations. This study shows that glutaraldehyde cross-linker leads to increase sensitivity, selectivity, and accuracy of protein analysis.

A data-driven approach to generate pseudo-reaction sequences for the thermal conversion of Athabasca bitumen

We use self-modeling multivariate curve resolution to identify pseudo-components and chemical transformations in thermal conversion of Athabasca bitumen.

Sampling unit for efficient signal detection and application to liquid chromatography-Raman spectroscopy

A new sampling unit design enhances the signal intensity and is available to combine Raman spectrometer with liquid chromatography.

A Rapid UV/Vis Spectrophotometric Method for the Water Quality Monitoring at On-Farm Root Vegetable Pack Houses

Our research aim was to apply UV/Vis spectrophotometric techniques for the rapid monitoring of the quality of water sourced from on-farm root vegetable washing processes. To achieve this goal, the quality assessment of the washing water and wastewater at different stages of the technological processes was performed using physicochemical, biological, and UV/Vis absorbance measurements as well as statistical methods, such as principal component analysis (PCA) and partial least squares (PLS) regression. Limit values of UV/Vis absorbance at specific wavelengths were predicted in order to adapt them for routine testing and water quality monitoring at the farm packhouses. Results of the lab analyses showed, that the main problems of the water quality were caused by suspended solids (470–3400 mg L−1), organic substances (BOD5 215–2718 mg L−1; COD 540–3229 mg L−1), nitrogen (3–52 mg L−1), phosphorus (1–6 mg L−1), and pathogenic microorganisms (TVC > 300 cfu mL−1, E. coli 5.5 × 103–1.0 × 104 cfu mL−1, intestinal enterococci 2.8 × 102–1.5 × 104 cfu mL−1, coliform bacteria 1.6 × 103–2.0 × 104 cfu mL−1). Suspended solids exceeded the limit values by 10–50 times, organic matter by 10–25 times, dissolved organic carbon by 3–5 times, nitrogen by 3–7 times, total phosphorus by 3–12 times, and microorganisms by 3–10 times. UV/Vis limit values calculated were as follows: A210 nm—3.997–4.009 cm−1, A 240 nm—5.193–5.235 cm−1, A254 nm—4.042–4.047 cm−1, A320 nm—7.387–7.406 cm−1, and A 660 nm—3.937–3.946 cm−1. UV/Vis measurements at A320 nm are proposed for the routine water quality monitoring.

Online Liquid Chromatography-Raman Spectroscopy Using the Vertical Flow Method

Liquid chromatography and Raman spectroscopy (LC-Raman system) were combined and developed with the aid of the vertical flow method that enhances the Raman signal intensity. The LC-Raman system enabled the online acquisition of the nonresonance Raman spectrum of LC eluates. We employed singular value decomposition (SVD) and subsequent reconstruction of the components for the analysis of two-dimensional (temporal and spectral) data. The obtained components were consistent with the Raman spectra and elution patterns of the samples, indicating the appropriateness of the SVD-based procedure. The rise and fall times of the elution band of the temporal component were considered as the instrumental function. D₂O mixed with H₂O exhibited increased full width at half maximum of the elution band of up to 30% in comparison to the calculated value because of diffusion. Band broadening was less significant in the case in which an immiscible solute (pentane) was mixed with H₂O. The limits of detection and quantitation were 1.2 ± 0.1, 2.1 ± 0.1, and 2.7 ± 0.1 mM and 4.1 ± 0.1, 6.9 ± 0.1, and 9.1 ± 0.2 mM for the ortho-, meta-, and para-isomers of methoxyphenol, respectively. The nonresonance Raman experiment provides the molecular specificity to LC on the basis of the inherent properties of eluates.

Lifting wavelet transform for Vis-NIR spectral data optimization to predict wood density

Visible and near infrared (Vis-NIR) spectroscopy is a mature analytical tool for qualitative and quantitative analysis in various sectors. However, in the face of "curse of dimensionality" due to thousands of wavelengths for a Vis-NIR spectrum of a sample, the complexity of computation and memory will be increased. Additionally, variable optimization technique can be used to improve prediction accuracy through removing some irrelevant information or noise. Wood density is a critical parameter of wood quality because it determines other important traits. Accurate estimation of wood density is becoming increasingly important for forest management and end uses of wood. In this study, the performance of two-dimensional (2D) correlation spectroscopy between wavelengths of various spectral transformations, i.e., reflectance spectra (R), reciprocal (1/R), and logarithm spectra (log (1/R)), were analyzed before optimizing spectral variable. The spectra of optimal transformation were decomposed using biorthogonal wavelet family from 3rd to 8th decomposition level based on lifting wavelet transform (LWT). The optimal wavelet coefficients of LWT were selected based on the performance of calibration set using partial least squares (PLS). Two frequent variable selection methods including uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were also compared. The results showed that the dimensionality of spectral matrix was reduced from 2048 to 16 and the best density prediction results of Siberian elm (Ulmus pumila L.) were obtained (R_p²R = 0.899, RMSEP = 0.016) based on LWT.

A rapid analytical method for the quantitative determination of the sugar in acarbose fermentation by infrared spectroscopy and chemometrics

In this article, a rapid analytical method for the quantitative determination of the glucose and maltose in the industrial acarbose fermentation was established by the combination application of infrared spectroscopy and chemometrics. The spectra of the 398 acarbose samples were collected by a portable infrared fast analyzer and the concentration of the glucose and maltose in the acarbose fermentation solution were determinate by high performance liquid chromatography (HPLC) as the referent database. Four spectral pretreatment methods, first derivative (FD), second derivative (SD), Savitzky-Golay (SG) convolution smoothing and mean center (MC) were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(21, 3)+MC method. The effects of different principal component numbers (PCs) on the parameters were also optimized. Two models of PLS and MLR, were used to predict the concentration of the glucose and maltose. The FD+SG(21, 3)+MC method was chosen as best method, with 12 PCs for glucose and 11 for maltose as optimized parameters. The PLS model was significantly better than the MLR model. Furthermore, both the predicted values and the reference values of glucose and maltose models showed superior linear relationship within the calibration range. The absolute errors of the predicted values and their corresponding reference values of glucose and maltose in the PLS model were within ±0.14 and ±0.35 confidence intervals, respectively. The prediction correct rate was 98.3%, which indicated that the prediction results of model were excellent.

Effects of Heat Treatment on Indomethacin-Cimetidine Mixture; Investigation of Drug-Drug Interaction Using Singular Value Decomposition in FTIR Spectroscopy

This study investigated the effect of phase transformation on indomethacin (IMC), cimetidine (CIM), and their 1:1 eutectic mixture, using FTIR spectra and multivariate analysis. The IMC form γ, CIM form A, and IMC-CIM 1:1 mixture molar ratio samples were heated on a hot plate at 303-453 K for 5 min and cooled till room temperature. The prepared samples were investigated by powder X-ray diffraction analysis and infrared spectroscopy with KBr powder. The PXRD patterns suggested the bulk IMC form γ, bulk CIM form A, and 1:1 physical mixture samples phase transformed into low-crystallinity IMC form γ, amorphous CIM, and eutectic mixture, respectively. FTIR spectra combined with singular value decomposition (SVD) suggested by absorbance peak shifts of several vibration modes that the polymorphic transformations of the samples affect their molecular interactions. The estimated melting points of heated samples were evaluated by sigmoid fitting based on the SVD results. The principal components of SVD show that the process of phase transformation of IMC-CIM 1:1 mixture samples affect the IR vibration of the CO stretch of IMC and the C-C stretch and C-H bending of CIM. These results suggest that molecular interaction is an important factor in the eutectic condition of IMC-CIM 1:1 mixture. This observation enables to evaluate for in co-amorphous formulations or stability of drug combinations in a poly pill.

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil

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.

The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

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.

A Metabolomics Coupled With Chemometrics Strategy to Filter Combinatorial Discriminatory Quality Markers of Crude and Salt-Fired Eucommiae Cortex

Eucommiae Cortex is commonly used for treating various diseases in a form of the crude and salt-fired products. Generally, it is empirical to distinguish the difference between two types of Eucommiae Cortex. The metabolomics coupled with chemometrics strategy was proposed to filter the combinatorial discriminatory quality markers for precise distinction and further quality control of the crude and salt-fired Eucommiae Cortex. The metabolomics data of multiple batches of Eucommiae Cortex samples was obtained by ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). Orthogonal partial least-squares discriminant analysis was utilized to filter candidate markers for characterizing the obvious difference of the crude and salt-fired Eucommiae Cortex. The accuracy of combinatorial markers was validated by random forest and partial least squares regression. Finally, eleven combinatorial discriminatory quality markers from 67 identified compounds were rapidly screened, identified, and determined for distinguishing the difference between crude and salt-fired Eucommiae Cortex. It was demonstrated that UHPLC-MS based metabolomics with chemometrics was a powerful strategy to screen the combinatorial discriminatory quality markers for distinguishing the crude and salt-fired Eucommiae Cortex and to provide the reference for precise quality control of Eucommiae Cortex.

Quantitative Analysis of Infrared Spectra of Binary Alcohol + Cyclohexane Solutions with Quantum Chemical Calculations

Hydrogen bonding has profound effects on the behavior of molecules. Fourier-transform infrared spectroscopy is the technique most commonly used to qualitatively identify hydrogen-bonding moieties present in a chemical sample. However, quantitative analysis of infrared (IR) spectra is nontrivial for the hydroxyl stretching region where hydrogen bonding is most prominently expressed in organic alcohols and water. Specifically, the breadth and extreme overlap of the O-H stretching bands, and the order of magnitude variability of their IR attenuation coefficients complicates the analysis. In the present work, sequential molecular dynamics simulations and quantum mechanical calculations are used to develop a function to relate the integrated IR attenuation coefficient to the vibrational frequencies of hydroxyl bands across the O-H stretching region. This relationship is then used as a guide to develop an attenuation coefficient scaling function to quantitatively determine concentrations of alcohols in a hydrocarbon solution from experimental IR spectra by integration across the entire hydroxyl frequency range.

Optical screening for presence of banned Sudan III and Sudan IV dyes in edible palm oils

Due to the proven carcinogenicity of Sudan III and IV dyes, they are considered global public health issues. They are banned in all forms as food colourants. We propose the monitoring of simple and easy-to-measure optical properties of palm oils, such as the refractive indices and spectrophotometric properties, as efficient indicators to detect adulteration. Coupling these results with principal component analysis, excess refractive index, and integration of transmittance introduces a novel detection tool for the authentication of edible palm oil. This opens a new opportunity for accurate handheld devices to detect adulteration and provide control in the field. This work assessed in total of 49 samples, some collected from different parts of Ghana and others, in-house adulterated samples. The Ghana Food and Drugs Authority, who performed a complex and expensive chemical analysis of the samples, confirmed our results with good agreement.

Potential Risk of Higenamine Misuse in Sports: Evaluation of Lotus Plumule Extract Products and a Human Study

Since 2017, higenamine has been added to the World Anti-Doping Agency (WADA) prohibited list as a β2-agonist prohibited at all times for sportspersons. According to WADA's report, positive cases of higenamine misuse have been increasing yearly. However, higenamine occurs naturally in the Chinese herb lotus plumule-the green embryo of lotus (Nelumbo nucifera Gaertn) seeds-commercially available as concentrated powder on the Asian market. This study evaluated the major phytochemical components of lotus plumule products using an appropriate extraction method, followed by a human study in which the products were orally administered in multiple doses to investigate the risk of doping violations. Comparing various extraction methods revealed that optimized microwave-assisted extraction exhibited the highest extraction efficiency (extraction time, 26 min; power, 1046 W; and temperature, 120 °C). Subsequently, the alkaloids in lotus plumule products were quantitatively confirmed and compared. Human study participants (n = 6) consumed 0.8 g of lotus plumule (equivalent to 679.6 μg of higenamine) three times daily for three consecutive days. All participants' urinary higenamine concentrations exceeded the WADA reporting cut-off of 10.0 ng/mL. Accordingly, lotus plumule consumption may engender adverse analytical findings regarding higenamine. Athletes should avoid consuming lotus plumule-containing products during in- and out-of-competition periods.

Differential Constituents in Roots, Stems and Leaves of Polygonum multiflorum Thunb. Screened by UPLC/ESI-Q-TOF-MS and Multivariate Statistical Analysis

The differential constituents in leaves, stems and roots of Polygonum multiflorum Thunb. were analyzed by ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-Q-TOF-MS) and by multivariate statistical analysis. The established extraction and analysis method showed relative standard deviations (RSDs) for intra-day precision of less than 3.40%, for repeatability of less than 4.06% and for stability of less than 5.10%. Principal component analysis and orthogonal projections to latent structures discriminant analysis of the UPLC/ESI-Q-TOF-MS data showed good ability to classify the leaves, stems and roots of P. multiflorum Thunb. The differential constituents, such as stilbenes, polygoacetophenoside, flavonoids and anthraquinones, accounting for variations between the leaves, stems and roots, were filtered through the variable importance in projection values and were further identified by elemental composition analysis, mass fragmentation data and retention times of available standards. Differences between the chemical compositions in the leaves, stems and roots of P. multiflorum Thunb. were closely related to their various therapeutic effects. This UPLC/ESI-Q-TOF-MS-based analytical strategy could be further utilized to evaluate the overall quality of traditional Chinese medicines and their differences of chemical constituents in different parts of the plant and/or in the plants of different geographical locations.

Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products

Chemometrics offers an important complementary tool to enhance the searching and isolation of bioactive natural products from natural sources.

Analysis of endocannabinoids in plasma samples by biocompatible solid-phase microextraction devices coupled to mass spectrometry

Anandamide (AEA) and 2-arachidonoyl glycerol (2-AG) represent two of the most important endocannabinoids (ECs) investigated in neurobiology as therapeutic targets for several mental disorders. However, the determination of these ECs in biological matrices remains a challenging task because of the low concentrations, low stability and high protein-bound (LogP ∼ 6). This work describes innovative analytical methods based on biocompatible SPME (Bio-SPME), SPME-UHPLC-MS/MS and Bio-SPME-Nano-ESI-MS/MS, to determine AEA and 2-AG in human plasma samples. The direct coupling of Bio-SPME with nano-ESI-MS/MS can be considered an alternative tool for faster analysis. Different Bio-SPME fibers based on silica and polymeric coating (i.e. C₁₈, C₃₀, and HLB) were evaluated. Different desorption solvents based on combinations of methanol, acetonitrile, and isopropanol were also evaluated for efficient elution with minimum carry-over. Given the high protein binding analytes and the fact that SPME extracts the free-concentration of the analytes, the plasma samples were modified with additives such as guanidine hydrochloride (Gu-HCl), trifluoroacetic acid, and acetonitrile. This study was carried out by experimental design to achieve complete protein denaturation and the release of target analytes. The maximum extraction efficiency was obtained under the following conditions: HLB coated fibers (10 mm length, 20 μm coating thickness), matrix modified (300 μL of plasma) with 50 μL of Gu-HCL 1 mol L^-1, 75 μL of ACN and 75 μL of water, and desorption with methanol/isopropanol solution (50:50, v/v). Both methods were validated based on current international guidelines and can be applied for monitoring of concentrations of endocannabinoids in plasma samples. SPME-UHPLC-MS/MS method presented lower LOQ values than SPME-nanoESI-MS/MS. The additional separation (chromatographic column) favored the detectability of LC-MS/MS method. However, the SPME-nano-ESI-MS/MS decrease the total analysis time, due to significant reductions in desorption and detection times.

Differentiation of avocados according to their botanical variety using liquid chromatographic fingerprinting and multivariate classification tree

BACKGROUND

The oil content, composition and marketing threshold value of an avocado depends on the cultivar hence, identifying the cultivar of the avocado fruit is desirable. However, analytical methods have not been reported with this aim.

RESULTS

A multivariate classification tree method was proposed to discriminate three commercial botanical varieties of avocado: Hass, Fuerte and Bacon, using high-performance liquid chromatography coupled to a charged aerosol detector (HPLC-CAD). Prior to the chromatographic analysis the avocados were lyophilized and then the oil fraction was extracted using a pressurized liquid extraction system. Normal and reverse phase liquid chromatography were applied in order to obtain the chromatographic fingerprint for each sample. Soft independent modelling of class analogies (SIMCA) and partial least-squares discriminant analysis (PLS-DA) were applied. Classification quality metrics were determined to evaluate the performance of the classification. Several strategies to develop the classification models were employed. Finally, the useful application of 'classification trees' methodology, which has been scarcely applied in the field of analytical food control, was evaluated to perform a multiclass classification.

CONCLUSION

Discrimination of the three botanical varieties was achieved. The best classification was obtained when the PLS-DA is applied on the normal-phase chromatographic fingerprints. Classification trees are showed to be useful tools that provide complementary information to single concatenated models showing different results from the same prediction sample set. © 2019 Society of Chemical Industry.

Disposable Colorimetric Paper-Based Probe for the Detection of Amine-Containing Gases in Aquatic Sediments

Amine compounds are considered highly important in environmental pollution, industrial, and medicinal fields. The objective of this work was to develop a disposable, highly accurate, highly selective, and low-cost paper-based probe through the combination of color change of seven pH indicators for the detection of amine compounds in the gaseous state. The probe was designed with seven rings which were printed using the wax-printing technique and colored with different pH indicators. The colors of the probe were analyzed using red, green, and blue (RGB) values extracted from the images obtained with a homemade smartphone application. The chemometric tools, principal component analysis, and hierarchical cluster analysis methods were adapted to further classify amine gases. The colorimetric probe showed an excellent capability for detecting the amines with high accuracy, prompt response, and high selectivity. These dye arrays have been proven to detect ethanolamine (NH₂CH₂CH₂OH), dimethylamine ((CH₃)₂NH), and trimethylamine ((CH₃)₃N) gases at parts per million scale.

Analysis of the retention of tetracyclines on reversed-phase columns: Chemometrics, design of experiments and quantitative structure-property relationship (QSPR) study for interpretation and optimization

In this study, design of experiments was applied for the analysis of 6 reversed phase U-HPLC columns used for the separation of four tetracyclines (TCs): tetracycline, doxycycline, chlortetracycline and oxytetracycline in different elution conditions. In a first part, a fractional factorial design (2^4-1) was used to study the influence of four chromatographic parameters: column temperature, pH, flow rate and composition of the mobile phase (i.e. nature of the solvent used as the organic modifier), on the quality of the separation, which was evaluated in terms of peak width and resolution between two pairs of TCs. This experimental design revealed that the nature of the solvent: acetonitrile (ACN) or methanol (MeOH), and the mobile phase flow rate were the two main factors actually having the most influence on the quality of the separation. Moreover, these two factors presented an antagonistic influence according to the response considered: peak width or peak resolution. In order to understand this behavior, a Doehlert design was performed in the second part. It consisted in modeling the evolution of responses as a function of the two main factors: nature of the composition of the mobile phase (mix of ACN and MeOH, from 100% ACN to 100% MeOH) and mobile phase flow rate (from 0.3 to 0.8 mL min^-1). For all the reversed phase columns studied, an inversion of the elution order of TCs and an increase of the retention factors was observed according to the composition of the organic mixture at the end of the gradient. To understand the modification of the interactions implied in the various retention modes related to the selectivity of the organic solvents used, a quantitative structure-property relationship (QSPR) study was achieved. In this final study, the molecular descriptors of each TCs were connected to its retention factor.

Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry

Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.