Chemometric Data Treatment Research Articles

Comparison of effects from gamma, e-beam and X-ray radiation on multilayer polymer films used in biopharmaceutical devices by chemometric treatment of spectroscopic data

The present study investigated the effects of gamma-ray, electron beam, and X-ray radiation on polymers in EVA/EVOH/EVA multilayer films. Spectroscopic methods, including FTIR and UV–Vis, were employed in conjunction with chemometric treatments such as PCA, SIMPLISMA treatments, and PLS models. The feasibility of dose prediction on multilayer film, given a known dose, is attainable across various irradiation technologies. Both gamma ray and X-ray exhibit comparable effects on the multilayer film, and it is feasible to anticipate the dose released on the film by utilizing UV–Vis spectra or infrared spectra for gamma ray or X-ray of a predetermined dose.

A Non-Invasive In Situ Spectroscopic Analysis of Cinnabar Minerals to Assist Provenance Studies of Archaeological Pigments

This study presents a non-invasive in situ methodology based on the use of portable elemental (energy dispersive X-ray fluorescence spectroscopy, EDXRF) and molecular (Raman spectroscopy) spectroscopic-based instrumentation as a tool to obtain preliminary information to assist subsequent provenance studies of archaeological cinnabar pigments in the laboratory. In this work, six cinnabar mineral ores, extracted from the Almadén mining district and an original raw pigment coming from the Archaeological Park of Pompeii have been analyzed. As the detection capacities and spectral resolution of the portable instruments are usually poorer than the equivalent benchtop equipment, a comparative study of the in-situ and laboratory results was conducted. Afterward, chemometric data treatment was performed considering both the molecular and elemental information. According to the elemental results, it was not possible to find a strong concordance between the cinnabar ores and the pigment from Pompeii, suggesting the need for additional methodologies in the laboratory (isotope ratio analysis) to complete a proper provenance study. However, this approach was useful to classify the ores according to their mineralogical differences. Therefore, this methodology could be proposed as a useful tool to conduct a representative sampling of the cinnabar mineral ores to be considered in a provenance study of archaeological cinnabar pigments.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI−) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI− biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Use of near-infrared spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits

The reflectance NIR spectroscopy and chemometric data treatment on mature intact lemons, Limone di Sorrento PGI (cv Ovale di Sorrento) and Limone Costa D’Amalfi PGI (cv Sfusato Amalfitano) from Campania region, collected in 2018 and 2019, were used to predict properties, and discriminate cultivar and geographical provenance. By PCA, lemon NIR spectra grouped for production years due to the year variation of lemon properties attributable to annual climatic differences, homogeneous in all sites. This agrees with lemon chemical and physical differences by production year. Consequently, the relationship of NIR spectra with lemon quality properties by MLR and the cultivar and provenances discrimination by LDA were affected by year climatic difference; therefore, better model reliability was for single production year. NIR detectability of lemon properties did not appear beyond lemon thick peels, therefore the measured properties of lemon juices could derive from measurable properties of peel correlating with pulp properties.

Variable selection in the chemometric treatment of food data: A tutorial review

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Trends in Gunshot Residue Detection by Electrochemical Methods for Forensic Purpose

Gunshot Residue (GSR) has been a subject of interest for the forensic fraternity. Numerous analytical contributions towards the GSR analysis have been reported. Sensitivity, portability, cost-effectiveness, speed, etc. are such factors of electrochemical methods that have attracted the researchers across the globe to test the applicability of these as a potential analytical tool for forensic evaluation of GSR. With the development of scientific technology, efforts have been made towards the hand-held device for the on-field analysis of GSR. Recently, chemometric treatment of data generated from the electrochemical analysis of GSR has offered more effective approach. It makes the analysis more conclusive and minimizes the chances of false-positive detection. It will be very fruitful to anticipate the analytical potential of electrochemical tools for GSR analysis. This article reviews the research progress towards the development of electrochemical sensor for GSR detection reported during 2013–2020 along with challenges and future perspectives.

NMR Approach for Monitoring Caranha Fish Meat Alterations due to the Freezing-Thawing Cycles

The fish meat shelf-life is highly influenced by temperature, making it necessary to use the cold chain to ensure freshness. Once this chain is stopped, unwanted reactions are initiated, and nutritional and texture changes in the meat may occur, compromising its consumption. Although these changes are important, it is not always possible for the consumer to differentiate between fresh and re-frozen fish due to the similarity when exposed to ice. In this context, the present study describes a metabolomic approach used to assess the influence on fish meat of the break in the cold chain by freezing-thawing cycles. Samples of caranha (Piaractus brachypomus) were used, and the metabolic profiles of frozen and frozen-thawed fishes were evaluated by nuclear magnetic resonance (NMR). The metabolic profile changes were identified through the chemometric treatment of NMR data and quantified using the Electronic Reference to Access In Vivo Concentrations (ERETIC 2) method. The results indicated the freezing-thawing cycles favored the increase of endogenous and exogenous enzymatic activities, expressed by variations in inosine, hypoxanthine, lactate, taurine, creatine, and trimethylamine amounts. This increase in metabolite concentrations, in particular inosine, hypoxanthine, and trimethylamine, was attributed as the main cause for the losses on fish meat quality and nutritional flavor, when compared with fresh fish.

Direct analysis of particulate matter (PM10) for the determination of Be, Cd and Pb using high resolution-continuum source electrothermal atomic absorption spectrometry: Assessment of the potential correlation between analyte content and meteorological parameters

Atmospheric particulate matter (APM) monitoring is a fundamental tool for air pollution management and control. However, the limited amount of an APM sample that is typically available upon collection using specific devices may be a limiting factor to promote reliable chemical analysis. Direct solid analysis is thus a cost-effective alternative to be applied for this purpose, since it simplifies the handling of samples, combining faster analysis to maximum detectability with minimum sample consumption. This study presents the development of methods for the determination of beryllium, cadmium and lead in inhalable particulate matter (PM10) using direct solid analysis and high-resolution continuum source electrothermal atomic absorption spectrometry. The temperature program and the use of chemical modifiers were optimized for each individual analyte. Zirconium was used as a permanent modifier for the determination of Be, whereas Pd and Mg were co-injected as chemical modifiers for the determination of Cd and Pb. The potential interferences and calibration strategies were also evaluated. Under optimized conditions, calibration against aqueous standards was proven feasible and detection limits of 0.001, 0.001 and 0.004 ng m−3 for Be, Cd and Pb, respectively, were achieved. The methods were applied to the determination of the referred analytes in 28 samples of PM10 collected at an urban site in the city of Florianópolis, in the State of Santa Catarina, Brazil. Chemometric treatment of data considering analyte content in addition to meteorological parameters (accumulated rainfall, sunlight incidence and wind speed) showed a detectable correlation between these parameters. Samples could be statistically differentiated based on separate loadings for Cd and Pb, which was opposite to Be, for instance. Hence, there is the possibility to correlate these parameters in order to establish a relationship between the origin of the elements in the PM10 samples and the impact of meteorological conditions on their distribution in solid atmospheric particles.

A chemometric approach to compare Portuguese native hops with worldwide commercial varieties

AbstractA diversity of native hops can be found in Portugal, but little is known concerning their volatile and sensory profiles. Nowadays, the exponential growth of the craft beer sector and the preference for more flavoured beers promote the research of unexplored wild hops that have the advantage of being well adapted to the Portuguese edaphoclimatic conditions. Therefore, the goal of this study was to characterize the volatile profile of 75 native Portuguese hops and compare with 34 commercial varieties by means of headspace solid‐phase microextraction gas chromatography/mass spectrometry (HS‐SPME‐GC/MS), in order to select those that present similarities with commercialized hops and confirm by check‐all‐that‐apply (CATA) analysis if they present similar organoleptic characteristics. Due to the complexity of hop volatile profile and the great number of samples analysed, robust chemometric treatment of chromatographic and sensorial data was required to make reliable conclusions. Twelve Portuguese hops present a volatile profile and sensory characteristics quite similar to some commercial varieties, because 11 Portuguese hops were grouped with the European varieties Challenger, Hallertauer Magnum and Perle, both in volatile profile and sensory analysis and one clustered with American registered varieties.

Chemometric Analysis of Low-field 1H NMR Spectra for Unveiling Adulteration of Slimming Dietary Supplements by Pharmaceutical Compounds.

The recent introduction of compact or low-field (LF) NMR spectrometers that use permanent magnets, giving rise to proton (1H) NMR frequencies between 40 and 80 MHz, have opened up new areas of application. The two main limitations of the technique are its insensitivity and poor spectral resolution. However, this study demonstrates that the chemometric treatment of LF 1H NMR spectral data is suitable for unveiling medicines as adulterants of slimming dietary supplements (DS). To this aim, 66 DS were analyzed with LF 1H NMR after quick and easy sample preparation. A first PLS-DA model built with the LF 1H NMR spectra from forty DS belonging to two classes of weight-loss DS (non-adulterated, and sibutramine or phenolphthalein-adulterated) led to the classification of 13 newly purchased test samples as natural, adulterated or borderline. This classification was further refined when the model was made from the same 40 DS now considered as representing three classes of DS (non-adulterated, sibutramine-adulterated, and phenolphthalein-adulterated). The adulterant (sibutramine or phenolphthalein) was correctly predicted as confirmed by the examination of the 1H NMR spectra. A limitation of the chemometric approach is discussed with the example of two atypical weight-loss DS containing fluoxetine or raspberry ketone.

Deep Learning Techniques to Improve the Performance of Olive Oil Classification.

The olive oil assessment involves the use of a standardized sensory analysis according to the “panel test” method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for olive oil classification. It is an essential task in an attempt to get the most robust model over time and, both to avoid fraud in the price and to know whether it is suitable for consumption or not. The aim of this paper is to combine chemical techniques and Deep Learning approaches to automatically classify olive oil samples from two different harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples, which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The data from the two harvests are built from the selection of specific olive oil markers from the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the best results we have configured the parameters of our model according to the nature of the data. The results obtained show that a deep learning approach applied to data obtained from chemical instrumental techniques is a good method when classifying oil samples in their corresponding categories, with higher success rates than those obtained in previous works.

Characterization of heteroatom distributions in the polar fraction of North Sea oils using high-resolution mass spectrometry

Resins are a sub-fraction of crude oil consisting of heteroatom-containing polar compounds. These species are of key importance in oil recovery, oil fluid thermodynamic properties, refining processes and for the environmental impact of petroleum production. Due to the inherent complexity of crude oil, little is known of the absolute molecular structure of these compounds. Here we show how a combination of separation science and mass spectrometry reveal the compositional space of resins isolated from Danish North Sea crudes. By employing spectral stitching mass spectrometry, we resolve over 15,000 unique peaks, an increase of 50% compared to broadband acquisition. The results indicate that the dominant component of North Sea resins are basic nitrogencontaining compounds with small to medium sized aromatic cores. Using chemometric data treatment it becomes evident that each field displays unique profiles, and that these fractions can be used for oil-oil and oil-source correlations. Further, a more detailed knowledge of their composition will allow us to gain a better understanding of the interactions between resins, hydrocarbons and reservoir rock material. Improved understanding of rock-fluid and fluid-fluid interactions is key for development and understanding of improved recovery mechanisms for tight chalk reservoirs.

Chemometric Assessment and Best-Fit Function Modelling of the Toxic Potential of Selected Food Packaging Extracts.

Food packaging materials constitute an ever more threatening environmental pollutant. This study examined options to specifically assess the ecotoxicity of packaged wastes, such as cans, subjected to various experimental treatments (in terms of extraction media, time of exposure, and temperature) that imitate several basic conditions of the process of food production. The extracts were studied for their ecotoxicity with bioluminescent Vibrio fischeri bacteria. The first objective of this study was to find patterns of similarity between different experimental conditions; we used multivariate statistical methods, such as hierarchical cluster analysis, to interpret the impact of experimental conditions on the ecotoxicity signals of the package extracts. Our second objective was to apply best-fit function modelling for additional data interpretation, taking into account, that ecotoxicity for various temperature conditions is time- and temperature dependent. We mathematically confirmed that chemometric data treatment allows for better understanding how different experimental conditions imitating the real use of food packaging. We also demonstrate that the level of ecotoxicity depends on different extraction media, time of exposure, and temperature regime.

HS-GC-IMS and chemometric data treatment for food authenticity assessment: Olive oil mapping and classification through two different devices as an example

There is a lack of official analytical methods to classify olive oil samples according to its organoleptic quality. For that, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was applied and two devices with different technical configurations were compared; a commercial isothermal HS-GC-IMS with a 5 cm drift tube and a new HS-GC-IMS prototype, which works at ramped temperature and presents a 10 cm drift tube. Both devices were compared in terms of resolution, precision and classification rates (CRs) using a 30 m non-polar capillary column. Our results suggested that the resolution in the chromatographic separation was improved using the device in which a ramped temperature can be performed in a shorter analysis time and the resolution in the ion mobility separation was also better. The repeatability values (RSD) for the standards quality control were lower than 10% in both cases. Moreover, as revealed in the olive oil 2D-maps, a higher number of markers were detected by the isothermal HS-GC-IMS (91) than that for the ramped temperature device (76), probably explained by the different sensitivity of both devices. These markers were used to build chemometric models based on orthogonal partial least squares-discriminant analysis. The olive oil samples in the validation set could be classified into extra virgin, virgin and lampante, defective and non-defective, as well as edible and non-edible with CRs ranging from 83 to 100%. In order to improve the resolution, a 60 m non-polar column was used in the ramped temperature HS-GC-IMS and enabled an increase in the visualized markers to 128, but it required a longer analysis time. The repeatability values were also lower than 10% and the CRs were improved (94–100%). Finally, 21, 22 and 25 olive oil aroma compounds were identified using the isothermal (30 m column) and the ramped temperature HS-GC-IMS with 30 and 60 m columns, respectively. Although some of these markers may give clues about the olive oil category, the chemometrics processing approach offers an objective tool for a robust classification and it is easily transferable to the industry.

Chemometric treatment of simple physical and chemical data for the discrimination of unifloral honeys

In this work, nonspecific physico-chemical parameters were determined in 160 honey samples belonging to the four main botanical categories present in Sardinia Island, Italy (strawberry tree, thistle, asphodel and eucalyptus) in order to develop a discriminant method for determining the botanical origin of honey. All the possible combinations of the seven physico-chemical parameters (pH, free acidity, electrical conductivity, color, total phenolic compounds, FRAP activity, and DPPH activity) measured in the honey samples were evaluated by Linear Discriminant Analysis (LDA). LDA models led to the prediction of each botanical origin with a very low level of misclassification (typically less than 5%). Since very high levels of correct prediction in cross validation (98.3%) and external validation (100%) were obtained considering only four parameters (i.e. pH, acidity, conductivity and DPPH), these results might allow a fast and easy control of the botanical origin of honeys.

Development and Validation of Chemometric Spectrophotometric Methods for Simultaneous Determination of Simvastatin and Nicotinic Acid in Binary Combinations.

The development and introduction of combined therapy represent a challenge for analysis due to severe overlapping of their UV spectra in case of spectroscopy or the requirement of a long tedious and high cost separation technique in case of chromatography. Quality control laboratories have to develop and validate suitable analytical procedures in order to assay such multi component preparations. New spectrophotometric methods for the simultaneous determination of simvastatin (SIM) and nicotinic acid (NIA) in binary combinations were developed. These methods are based on chemometric treatment of data, the applied chemometric techniques are multivariate methods including classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS). In these techniques, the concentration data matrix were prepared by using the synthetic mixtures containing SIM and NIA dissolved in ethanol. The absorbance data matrix corresponding to the concentration data matrix was obtained by measuring the absorbance at 12 wavelengths in the range 216 - 240 nm at 2 nm intervals in the zero-order. The spectrophotometric procedures do not require any separation step. The accuracy, precision and the linearity ranges of the methods have been determined and validated by analyzing synthetic mixtures containing the studied drugs. Chemometric spectrophotometric methods have been developed in the present study for the simultaneous determination of simvastatin and nicotinic acid in their synthetic binary mixtures and in their mixtures with possible excipients present in tablet dosage form. The validation was performed successfully. The developed methods have been shown to be accurate, linear, precise, and so simple. The developed methods can be used routinely for the determination dosage form.

Development of a SBSE-TD method coupled to GC–MS and chemometrics for the differentiation of variety and processing conditions in peach juices

Peach juices of distinct varieties, namely yellow- and red-fleshed, and commercial and freshly blended were analyzed. The method used was based on Stir Bar Sorptive Extraction (SBSE) involving a polydimethylsiloxane-coated stir bar with thermal desorption (TD), followed by gas chromatography coupled to mass spectrometry (GC–MS) analysis. The resulting analytical data included 41 compounds belonging to several chemical classes, such as aldehydes, alcohols, lactones, terpenoids, fatty aldehydes, fatty acids and hydrocarbons. Furthermore, chemometric data treatment using unsupervised analysis (PCA) proved useful to classify peach juices on the basis of variety. Stepwise Linear Discriminant Analysis (SLDA) showed that a reduced number of variables (14 compounds), including lactones (6-pentyl-α-pyrone, γ-decalactone, γ-dodecalactone, and δ-dodecalactone), fatty acids (hexadecanoic acid), fatty aldehydes (tetracosanal and octacosanal), hydrocarbons (C23, C26, C27, C29, and C33), and alcohols (phytol and α-tocopherol), were necessary to classify the juice samples according to variety and processing conditions.

UV–Vis spectroscopy with chemometric data treatment: an option for on-line control in nuclear industry

Chemometrics can be very useful for the classical field of UV–Vis determination of metals in aqueous solutions. A conventional approach consisting of using selective bands in a univariate mode is often not applicable to the real-world samples from e.g. hydrometallurgical processes, because of overlapping signals, light scattering on foreign particles, gas bubble formation, etc. And this is where chemometrics can do a good job. This paper overviews certain contributions to the field of multivariate data processing of UV–Vis spectra for seemingly simple case of metal detection in aqueous solutions. Special attention is given to applications in nuclear technology field.

Optimization and classification of natural products by chemometric method. Analysis of gas chromatographic data of essential oils of Indonesia Myristica fragans Houtt.

Essential oils (EO) of nutmeg (Myristica fragan Houtt) have a different chemical composition according to their origin. So it is important that laboratories of quality control have a fast and effective method of analysis to prevent frauds and adulteration. The analytical method generally employed [1], is time consuming and two pairs of compounds were not resolved (sabinene/β-pinene, p-cimene/limonene). In order to optimize the GC separation, while reducing the analysis time, we have identified the influential analytical factors among 11 by using a screening of factors: 5 factors are influent. After, we studied the interactions between these 5 factors by using a fractional factorial experiment matrix 2 5 – 1 in 16 experiments [2] and by considering the interactions effect of first order b ij. This optimization allowed to reduce analysis time (20 min instead of 55 min) and to improve the separation between the unresolved compounds (Resolution > 1.5). This method was applied to differentiate EO from ripe nutmeg and those from maces: a chemometric treatment (PCA) of GC data was carried out. The repartition of samples in a 3D score plot (PC1; PC2; PC4 explained variance 81%) show two groups: the maces and the ripe nutmeg. The study of loading associated to these compounds showed that the EO of maces are different on the basis of their content of phenylpropenic compounds (eugenol, myristicin, safrol...) while the EO of ripe nutmegs is characterized by the content of terpenic compounds (α and γ-terpinene, sabinene, α and β-pinene...). However, an overlap of the different classes exists. So the supervised classification method SIMCA [3] was used. Four samples of each origin were used to build the disjoint PCA and three from the same origin to test the models: all the samples are well classified. This study allowed to reduce analysis time and to improve the separation between all the compounds, and to differentiate EO from their origin: this method can be used as quality control.

Developing universal models for the prediction of physical quality in citrus fruits analysed on-tree using portable NIRS sensors

The citrus sector seeks rapid, economical, environmentally-friendly and non-destructive technologies for monitoring external and internal changes in physical quality taking place in fruit during on-tree growth, thus allowing fruit quality to be evaluated at any stage of fruit development. The use of portable near-infrared spectroscopy (NIRS) sensors based on micro-electro-mechanical system (MEMS) technology, in conjunction with chemometric data treatment models, has already been studied for quality-control purposes in two citrus species: oranges and mandarins. The critical challenge is to develop robust and accurate universal models based on hundreds of highly heterogeneous citrus samples in order to design quality prediction models applicable to all fruits belonging to the genus Citrus, rather than models that can only be applied successfully to a single citrus species. This study evaluated and compared the performance of Modified Partial Least Squares (MPLS) and LOCAL regression algorithms for the prediction of major physical-quality parameters in all citrus fruits. Results showed that, while models developed using both linear (MPLS) and non-linear regression techniques (LOCAL) yielded promising results for the on-tree quality evaluation of citrus fruits, the LOCAL algorithm additionally increased the predictive capacity of models constructed for all the main parameters tested. These findings confirm that NIRS technology, used in conjunction with large databases and local regression strategies, increases the robustness of models for the on-tree prediction of citrus fruit quality; this will undoubtedly be of benefit to the citrus industry.