Chemometric Techniques Research Articles

Probing Differential Metabolome Responses among Wheat Genotypes to Heat Stress Using Fourier Transform Infrared-Based Chemical Fingerprinting

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress.

The use of infrared spectroscopy and chemometrics to investigate deterioration in vegetable tanned leather: potential applications in heritage science

Vegetable tanned leather presents a unique challenge to conservators and curators of heritage collections, as little is known about how its physical and chemical properties change upon deterioration. Developing a better understanding of deterioration processes would be incredibly valuable in informing the conservation, storage, and restoration of leather objects. Fourier Transform infrared spectroscopy (FTIR) used with attenuated total reflectance (ATR) is increasingly applied in the heritage sector due to its relative ease of application and potential to be non-destructive. However, whilst FTIR has been applied successfully to the understanding of deterioration in other protein-based materials such as parchment, its application to the analysis of leather has been limited, largely due to the highly complex spectra obtained. Here, we have developed multivariate statistical methods for the analysis of FTIR data obtained from a time-series of leather samples artificially degraded at different pH values. Principal component analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and k-means clustering, when used together, are demonstrated as powerful tools in identifying early subtle differences in the FTIR spectra as leather degrades, identifying differences occurring over time and between different environmental conditions. We show that k-means clustering of time series data was able to highlight some areas of the spectrum that might be indicative of degradation, which more common chemometric techniques could not. The methods we describe here have the potential to widen the application of FTIR as a fast, non-destructive and reliable tool for assessing the condition of archaeological and historical leather objects, ultimately leading to better informed conservation, storage and restoration of these objects.

Physicochemical properties and detection of glucose syrup adulterated Kelulut ( Heterotrigona itama ) honey using Near‐Infrared spectroscopy

In this study, the physicochemical properties and the precision of adulterated honey detection by NIRS were assessed. A total of 11 adulteration ratio samples were produced with glucose syrup. The ash and moisture content of the honey samples were found to be in accordance with the Malaysian Standard, including all adulterated ones. The pH value and HMF content complied with the standard except for the pH of 100% adulterated honey and HMF content of 30–100% adulteration. NIR spectra were scanned at 900–1700 nm, 1848 spectra were recorded, and the data were pre-processed to reduce the unwanted spectral interference. Chemometric techniques and machine learning were used as prediction tools for adulteration levels. This includes principal component analysis (PCA), k-nearest neighbors (kNN) and random forest. The accuracy of the prediction samples was above 90%. Hence, this was satisfactory for rapid detection of Kelulut honey adulteration. Novelty impact statement Kelulut honey is becoming increasingly popular and in-demand due to its health advantages. Kelulut honey adulteration is more likely as a result of this. The NIRS can detect Kelulut honey adulteration more quickly than any other method with greater efficiency and reliability. Honey adulteration may be detected rapidly and non-destructively using NIRS and chemometrics. The results demonstrate that employing this technology as a screening method in routine laboratories might be useful for quality control assessments.

Utilizing chemometric techniques to evaluate water quality spatial and temporal variation. A case study: Bahr El-Baqar drain — Egypt

This study employed factor analysis (FA)/Principal component analysis (PCA), and hierarchical cluster analysis (HCA), as chemometric techniques in Bahr El-Baqar drain, Egypt to nominate and assess the most representative parameters of the present water quality status, and to assort the sampling sites according to the similarity of the selected parameters, in summer and winter seasons. The water quality data divided the drain into two datasets (before and after El-Salam canal) according to calculated values of the means percentage difference (%) and paired t-test. FA/PCA, selected four factors as indicators of water quality, explaining the cumulative variance ranged (76.0 - 81.1)%. The HCA grouped the sampling sites before the El-Salam canal into four, and five clusters during summer, and winter, respectively while the sampling sites after the El-Salam canal were grouped into three, and four clusters during summer, and winter, respectively showing that the sampling sites are influenced by the seasonal variation. It was revealed that the major sources of variability in Bahr El-Baqar drain are foremost from an anthropogenic origin and thereafter governed by biogenic and organic activities followed by agricultural activities. Finally, this study emphasizes chemometric techniques as a powerful and useful tool to understanding the water quality spatial and temporal variations and their causes. This can support cost, effort, and time reduction, thereby reinforcing regular water quality monitoring plans and may assist in the establishment of guidelines and regulatory plans. • FA/PCA, HCA, and Pearson’s correlation were adopted as chemometric techniques. • Two datasets were created according to the means % difference and paired t-test. • 4 factors explained water quality indicators with cumulative var. (76.0 - 81.1)%. • Sources of variability are anthropogenic/biogenic, organic and agricultural. • Chemometrics evaluated water quality spatial and temporal variations powerfully.

Identification of human hair wigs and animal hair wigs by the method of near infrared spectroscopy modeling

The near infrared spectrogram of human hair wigs and animal hair wigs was measured with a Fourier near infrared spectrometer. In addition, a qualitative model for the identification of human hair wigs and animal hair wigs with their near infrared spectrogram has been established. The effectiveness of spectral preprocessing with chemometric techniques including standard normal variable, second derivative and Norris smoothing has been analyzed through principal component analysis. The results showed that the accuracy of the discriminant model could reach 100%, indicating that the near infrared spectroscopy method is highly applicable and effective for the identification of human hair wigs and animal hair wigs.

UV spectroscopy as a quantitative monitoring tool in a dairy side-stream fractionation process

In this work, we investigate the feasibility of multi-wavelength ultra-violet spectroscopy for the quantification of β-lactoglobulin and α-lactalbumin in ultrafiltration permeates from protein fractionation processes. Spectra from solutions of pure proteins were compared and distinctive characteristics for the two proteins were identified. Subsequently, two different calibration approaches were tested to overcome the “cage of covariance” that is inherent in protein fractionation and up-concentration processes. Selection of wavelength regions allowed for the prediction of the β-lactoglobulin and α-lactalbumin concentration with high precision and accuracy, reaching a root mean square error of cross-validation of 0.26 w/w% (concentration range 0–10 w/w%∗∗Concentration values of α-la and β-lg in this work have been arbitrarily, independently and linearly scaled away from the true value for confidentiality reasons.) protein for α-lactalbumin and 0.11 w/w% (0–10 w/w%) protein for β-lactoglobulin. This proves the potential of the methods developed for implementation as rapid monitoring of protein composition in permeates from ultrafiltration processes. The developed Partial Least Squares (PLS) regression models were used to predict protein composition in a continuous mode during two lab-scale filtration experiments. The results obtained show that UV spectroscopy can be used, along with tailored chemometrics techniques, for monitoring protein composition in protein fractionation processes both at-line and potentially in-line.

Potential of NIRS Technology for the Determination of Cannabinoid Content in Industrial Hemp (Cannabis sativa L.)

Industrial hemp (Cannabis sativa L.) is a plant native to Asia, and is considered to be a primary source of food, textile fiber, and medicines. It is characterized by containing minimal concentrations of delta-9 tetrahydrocannabidol (THC), which is the main psychoactive chemical component, and cannabidiol (CBD), a non-psychoactive substance. In most European countries, the maximum concentration legally allowed for cultivation is 0.2% of THC, and it is currently under debate whether to increase this level to 0.3%. Moreover, in many countries its production is being regularized and legalized, increasing the need for a rapid analysis method. The present work evaluated the cannabinoid content in hemp (Cannabis sativa L.) using near infrared spectroscopy (NIRS) technology in combination with chemometric techniques. For this, several samples of the Kompolti variety were analyzed. Samples were dried and ground, and the content of total THC (%) and total CBD (%) was determined by high performance liquid chromatography (HPLC) with a diode array detector as reference measurements, and then the spectra were collected by NIRS. Principal component analysis and partial least square regression models were developed. Good coefficients of determination of cross-validation of 0.77 for THC and CBD, and a ratio of prediction to deviation >2 for total THC and CBD, were achieved. The results obtained show that NIRS technology has potential for the quantitative determination of cannabinoids. Therefore, this analytical method would allow a simpler, more robust, precise, and sustainable estimation than the current HPLC approach.

Spectroscopic–chemometric modeling of 80 humic acids confirms the structural pattern identity of humified organic matter despite different formation environments

The structure of humic substances (HSs) and the humification process are critical topics for understanding the dynamics of carbon on the planet. This study aimed to assess the structural patterns of 80 humic acid (HA) samples isolated from different soils, namely, Histosols, Ferralsols, Cambisols, Mollisols, Planosols and vermicompost, by spectroscopic characterization using solid-state 13C nuclear magnetic resonance cross-polarization/magic angle spinning combined with chemometric techniques. All 80 HAs had a similar structural pattern, regardless of their source of origin, but they had different relative quantities of organic C species. The different structural amounts of the various organic C fractions generated different properties in each of the HAs. This explains why there were similarities in the HS functions but why the intensities of these functions varied among the samples from the different soil types and environments, confirming that HSs are a group of compounds with a structural identity distinct from the molecules that give rise to them. There appears to be no single definition for the humification process; therefore, for the soils from each source of origin, a specific humification process occurs that depends on the characteristics of the local environment. Humification can be understood as a process that is similar to a chemical reaction, where the key factor that determines the formation of the products is the structural characteristics of the reactants (organic substrates deposited in the soil). The degree to which the reaction progresses is governed by the reaction conditions (chemical, physical, and biological properties of the soil). The structural patterns for HSs obtained in this study justify the existence of HSs structured as self-assembled, hydrophilic and hydrophobic domains that, under certain conditions, can undergo transformations, altering the balance of organic carbon in the environment.

A Rapid Routine Methodology Based on Chemometrics to Evaluate the Toxicity of Commercial Infant Milks Due to Hazardous Elements

The toxicity and the health risk assessment associated to the presence of some hazardous elements (HEs) in dried (infant formula and powdered) milks due to manufacturing and packaging process, raw materials used, environmental conditions, etc. need to be determined. With this aim, a new methodology based on the combination of health risk quotients and non-supervised (as cluster analysis (CA) and principal component analysis (PCA)) chemometric techniques is proposed in this study. The methodology was exemplified using the concentration of 27 elements, some of them HEs, measured in 12 powdered milk samples produced for children and adults in Brazil and Colombia. The concentration values were obtained by inductively coupled plasma-mass spectrometry (ICP-MS) after acid microwave digestion. Elemental concentrations vary depending upon the type of milk (initiation, growing-up, follow-on milks and adult milks). However, hazard quotients (HQ) and carcinogenic risk (CR) values showed no risk associated to the presence of HEs on milks. The methodology designed made possible to conclude that adults’ milks are more characteristic of elements naturally present in milk. Children milks present major presence of trace and minor elements. Between infant milks, sample H, designed for babies between 12 and 36 months, was identified as of poor quality. Moreover, it was possible to deduce that while the fortification process applied to children powdered milks is a probable metal and metalloid source, together with the manufacturing, the skimming process is not a contamination source for milks.Graphical abstract

Benchmarking Spectroscopic Techniques Combined with Machine Learning to Study Oak Barrels for Wine Ageing.

Due to its physical, chemical, and structural properties, oakwood is widely used in the production of barrels for wine ageing. When in contact with the wine, oak continuously releases aromatic compounds such as lignin, tannin, and cellulose to the liquid. Due to the release process, oak loses its characteristic aromatic compounds in time; hence, the flavour that it gives to the enclosed wine decreases for repeated wine refills and a barrel replacement is required. Currently, the estimation of the maximum number of refills is empirical and its underestimation or overestimation can impose unnecessary costs and impair the quality of the wine. Therefore, there is a clear need to quantify the presence of the aforementioned aromatic compounds in an oak barrel prior to a refill. This work constitutes a study to examine noninvasive optical biosensing techniques for the characterization of an oak barrel used in wine ageing, towards the development of a model to unveil its lifespan without inducing structural damage. Spectroscopic diagnostic techniques, such as reflectance, fluorescence, and Raman scattering measurements are employed to assess the change in the chemical composition of the oakwood barrel (tannin and lignin presence) and its dependence on repeated refills. To our knowledge, this is the first time that we present a benchmarking study of oak barrel ageing characteristics through spectroscopic methods for the wine industry. The spectroscopic data are processed using standard chemometric techniques, such as Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. Results of a study of fresh, one-time-used, and two-times-used oak barrel samples demonstrate that reflectance spectroscopy can be a valuable tool for the characterization of oak barrels. Moreover, reflectance spectroscopy has demonstrated the most accurate classification performance. The highest accuracy has been obtained by a Partial Least Squares Discriminant Analysis model that has been able to classify all the oakwood samples from the barrels with >99% accuracy. These preliminary results pave a way for the application of cost-effective and non-invasive biosensing techniques based on reflectance spectroscopy for oak barrels assessment.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

Characterization of the Santa Maria del Fiore cupola construction tools using X-ray fluorescence

<p class="Abstract">This paper presents the characterization of different tools employed in the construction of the Santa Maria del Fiore cathedral in Florence; they are part of the Opera di Santa Maria del Fiore collection and are currently exhibited in the Museo dell’Opera del Duomo. The analysed objects are turnbuckles, pulleys, three-legged lewises, and pincers; indeed, despite their uniqueness and their importance from the historical point of view, this study is the first one that investigates their alloys composition. Actually, this information can be of great interest for curators to find the best conservation strategies and to have new insights on the production techniques typical of the Renaissance. The study was performed using X-Ray Fluorescence (XRF) in order to identify the materials constituting the objects. Then, XRF spectra were analysed using chemometric techniques, namely Principal Components Analysis (PCA), in order to investigate possible similarities among different alloys and thus provide new indications to help collocating these tools in a specific historical period.</p>

Counterfeit fifty Ringgit Malaysian banknotes authentication using novel graph-based chemometrics method

Counterfeiting, in particular, the forgery of banknotes is a serious crime problem and has become a great challenge to the global economies. The forensic science experts have been utilizing chemical technique such as infrared spectroscopy to analyze genuine and counterfeit banknotes. Nevertheless, chemometrics techniques are required to further discriminate the banknotes. In this paper, an advanced fuzzy graph chemometrics method is used to discriminate genuine and counterfeit fifty Ringgit Malaysian (RM50) banknotes. The development of the technique, namely chemometrics fuzzy autocatalytic set (c-FACS) is presented in this paper, together with the results and its comparison to principal component analysis (PCA) method. The results from the c-FACS analysis showed distinct patterns and features of the counterfeit banknotes in the c-FACS plot. Furthermore, the new method is faster than PCA in authentication analysis of counterfeit banknotes. Hence, the c-FACS provides better performance in terms of computing time as compared to PCA, and has the potential in assisting the investigation involving counterfeit banknotes.

Chemometric classification of Brazilian artisanal cheeses from different regions according to major and trace elements by ICP-OES

This work aims to determine the mineral composition of Brazilian artisanal cheese (BAC) and classify the cheese types using chemometric techniques. Samples of BAC were analyzed and divided according to their region: Northeast cheeses (Coalho and Manteiga); Midwest cheeses (Caipira); Southeast cheeses (Araxá, Campo das Vertentes, Cerrado, Serra da Canastra and Serro) and South cheeses (Colonial and Serrano). Major (Ca, K, Mg, Na) and trace elements (Cu, Mn, Zn) content were determined by inductively coupled plasma optical emission spectrometer (ICP-OES). Artificial neural network (ANN), K-nearest neighbor (KNN), Random Forest (RF), Support vector machines with the radial kernel (SVM), and Learning Vector Quantization (LVQ) were used as supervised statistical methods to differentiate and classify the cheeses according to the type, and producing region. Coalho cheese showed the highest Ca, Cu, Mn, and Zn content with 91.09 mg g−1, 63.51 μg g−1, 3.29 mg g−1, and 137.51 μg g−1, respectively. Canastra cheese showed the highest K and Na content with 11.68 mg g−1 and 96.98 mg g−1, respectively. Cerrado cheese showed the highest Mg content (7.56 mg g−1). These differences in mineral content are explained by the type of cheese (fresh, ripened), producing region (climate, animal feeding, raw milk), and the technological process of cheese making (salts, metallic instruments). BAC is an essential source of minerals beneficial to health, and its authenticity is paramount for the consumer. RF and SVM classifiers were able to classify the cheese type dataset using the accuracy and Kappa coefficient as the figures of merit (Accuracy= 0.8347 and Kappa= 0.8105, Accuracy=0.8323 and Kappa=0.8078, respectively). For the classification of the production region, all algorithms presented excellent figures of merit. The classification performance can be characterized as outstanding for both cases. Such techniques guarantee the authenticity of Brazilian artisanal cheeses and the Protected Designation of Origin of the cheeses from each region, enhancing the added value of the cheese.

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.

Complementarity of LA-ICP-MS and petrography in the analysis of Neolithic pottery from the Scheldt River valley, Belgium

This paper presents the results of laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS) analysis of the clay fraction in 70 Late Mesolithic and Early/Middle Neolithic pottery sherds from the Scheldt basin and 10 sampled sediments. The elemental concentration results were visualized using multivariate chemometric techniques and compared to the results of petrographic analysis of the same ceramics in order to investigate the complementarity of both approaches. In addition, the results for the pottery and sediments were compared for clay sourcing purposes. Overall, the elemental analysis was able to confirm part of the observations from the petrographic analysis. However, a large part of the ceramics chemical clustering was driven by heterogeneity in the elemental composition that does not stem from differences in the clay source used. Furthermore, no conclusions could be drawn on the use of the sampled sediments for pottery production. Therefore, it is concluded that LA-ICP-MS analysis of the clay fraction in pottery can complement petrography, but petrographic analysis remains indispensable for clay sourcing of pottery from northern Belgium.

Optimization of Phenolic Compound Extraction from Brewers’ Spent Grain Using Ultrasound Technologies Coupled with Response Surface Methodology

Brewers’ spent grain (BSG) is the main solid by-product from the brewery industry, rich in valuable nutrients and bioactive compounds. The aim of this study was to valorize this by-product, recovering phenolic compounds from BSG using ultrasound-assisted extraction (UAE) and chemometric techniques, such as the response surface methodology (RSM). Therefore, UAE process parameters (temperature and time) and solvent composition (ethanol aqueous mixtures) were optimized using a three-level Box–Behnken design, in order to carry out the maximum yield in phenols. Then, the extract obtained under optimal conditions was characterized for the total phenolic content and antioxidant capacity (2,20-azino-bis(3-ethylbenothiazoline-6-sulphonic acid, ABTS, and 2,2-diphenyl-1-picrylhydrazyl, DPPH), and individual phenolic compounds were identified using HPLC-DAD. The results show the highest level of total soluble phenolic content (4.1 ± 0.1 mg GAE/g d.w.) at 80 °C, 50 min and 65:35% ethanol:water, with a high goodness of fit between experimental and predicted values (R2 = 0.987), and a high antioxidant potential (DPPH: 0.42 ± 0.01 mg TE eq/g d.w.; ABTS: 5.82 ± 0.04 mg TE eq/g d.w.). A comparison between the classic extraction techniques and the UAE with the same solvent showed an increase of 156% in the phenol yield. The characterization of phenolic profile revealed that ferulic acid (1.5 ± 0.2 mg/L), vanillic acid (0.78 ± 0.18 mg/L) and p-coumaric acid (0.12 ± 0.03 mg/L) were the prevalent ones. UAE coupled with RSM was a useful tool to inexpensively and quickly recover bioactive phenolic compounds from BSG, which can be used in the food, pharmaceutical or cosmetic industries.

Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques

This study aimed to evaluate the applicability of electrochemical impedance spectroscopy to identify raw bovine milk adulteration with urea. Three batches of raw milk adulterated with urea were studied. Hierarchical clustering indicated that the samples could be split in three groups corresponding to low adulteration (less than 7 wt%), medium adulteration (between 8 and 16 wt%) and high adulteration (over than 16 wt%). A linear discriminant analysis was performed resulting in 90% of accuracy in classifying between groups. Besides, a partial least squares model containing three directions provided good accuracy in quantitatively predicting the urea mass fraction added to raw bovine milk. Finally, calculations using an approximated electric circuit model suggested the formation of urea aggregates that hinder charge transportation within the milk thus diminishing the solution conductivity. Results indicate that electrochemical impedance spectroscopy can be a useful, low cost and rapid tool to identify milk adulteration with urea.

Modelling the Photodegradation of Marine Microplastics by Means of Infrared Spectrometry and Chemometric Techniques

This work investigated the structural and chemical changes of plastics undergoing accelerated ageing upon irradiation that simulated the ultraviolet component of solar radiation for a five-year period. The plastics selected were polyethylene (PE) and polypropylene (PP) microplastic debris sampled from a sandy beach as well as pure pellets and fragments of objects made of the same polymers. We recorded Fourier Transform Infrared (FTIR) spectra at prescribed intervals during the irradiation procedure. The spectra were used to study the evolution of the absorption peaks usually associated with the environmental ageing of polyolefins, namely the peaks of hydroxyl and carbonyl stretching, the peaks relating to the presence of double bonds, and those associated with the crystallinity of PE and the tacticity of PP. The results showed that none of the usual degradation indexes followed a clear trend with increasing exposure and that the evolution of absorption peaks was not consistent among different fragments. We used the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) method to process the whole information contained in the FTIR spectra in response to the chemical changes occurring during photochemical ageing. The results showed that the FTIR spectra contained sufficient information to cluster samples according to the irradiation received. Variable Importance of the Projection (VIP) analyses showed that the information for discriminating among different exposures was mainly contained in the absorption peaks corresponding to the hydroxyl and carbonyl stretching absorptions. The chemometric models had large determination coefficients, despite the large number of variables involved and could be applied to assess the environmental fate of plastics under environmental stressors.

Infrared spectroscopy for document dating - Advantages, challenges and limitations

Infrared spectroscopy has been increasingly employed for document dating in forensics. In this paper, we evaluate the advantages and disadvantages of methodologies based on this technique, by studying and comparing the artificial and natural aging of paper. Chemometric techniques were used to extract the relevant information. Principal component analysis (PCA) enabled observation of changes in the paper spectral profiles over time, indicating the influence of different storage conditions (temperature and exposure to UV radiation) on the aging process. This made it possible to observe what different storage conditions resulted in different degradation processes. Regression models based on partial least squares (PLS) were used to select variables such as the genetic algorithm (GA) and the generalized least squares weighting (GLSW) filter. The model that best described the dataset was the one using GLSW for both naturally and artificial aging, with α equal to 0.032 for artificial aging and α equal to 0.112 for natural aging. In this paper we evaluate the complexity of infrared spectroscopy and chemometric approaches, showing that since the degradation of paper depends on several factors, these methodologies must be carefully evaluated before being used for forensic purposes.