Application of chemometric methods to XRF-data – A tutorial review

Development of a rapid X-ray fluorescence method for protein determination in soybean grains

X-ray fluorescence (XRF) is a well-established technique for elemental determination. This study evaluates the ability of XRF to quantify soybean protein content based on elemental composition, particularly sulfur emission. Univariate linear regression, multiple linear regression, and partial least squares regression (PLS) were compared. Two scenarios were considered: scenario A used 108 soybean samples for calibration and 54 for validation; scenario B expanded the protein content range of scenario A, including 32 new samples of soybean mixed with concentrates. PLS showed the best performance in validation, with R2 of 0.73 and 0.89 in scenarios A and B, respectively. The results indicate that protein quantification by XRF has relative prediction errors below 3.1 %. The developed methods provide an alternative for monitoring soybean protein content, suitable for screening applications such as integrating XRF sensors on soybean harvesters.

A novel particle size distribution correction method based on image processing and deep learning for coal quality analysis using NIRS-XRF

The combined application of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) has achieved remarkable results in coal quality analysis by leveraging NIRS's sensitivity to organic compounds and XRF's reliability for inorganic composition. However, variations in particle size distribution negatively affect the diffuse reflectance of NIRS and the fluorescence signal intensities of XRF, leading to decreased accuracy and repeatability in predictions. To address this issue, this study innovatively proposes a particle size correction method that integrates image processing and deep learning. The method first captures micro-images of the coal sample surface using a microscope camera and employs the Segment Anything Model (SAM) for binarization to represent particle size distribution. Subsequently, a Spatial Transformer Network (STN) is applied for geometric correction, followed by feature extraction using a Convolutional Neural Network (CNN) to establish a correlation model between particle size distribution and ash measurement errors. In experiments involving 56 coal samples, including 48 at 0.2 mm for the standard ash prediction model and 8 within a 0∼1 mm range for correction, the results showed significant improvements: standard deviation (SD), mean absolute error (MAE), and root mean square error of prediction (RMSEP) decreased from 0.321%, 0.317%, and 0.335% to 0.229%, 0.225%, and 0.257%, respectively. Using the accuracy of the 0.2 mm particle size validation set as a reference, compared to before correction, the errors in these metrics were reduced by 64.06%, 50%, and 60.80%, respectively. This study demonstrates that integrating deep learning and image analysis significantly enhances the repeatability and accuracy of NIRS-XRF measurements, effectively mitigating sub-millimeter particle size effects on spectral detection results and improving model adaptability. This method, through automated particle size distribution analysis and real-time result correction, holds promise for providing essential technical support for the development of online quality detection technologies for conveyor belt materials.

Shades of red: A chemical exploration of pigments and dyes in 19th century postage stamps by a multi-analytical methodology

A total of 57 European, Canadian and North American postage stamps, all in red shades, were analyzed with the main goal of unraveling which pigments or dyes were used to produce the red color in the period dated from 1841 to 1899. Both non-destructive techniques, including X-Ray Fluorescence (XRF), Fiber Optics Reflectance Spectra (FORS), and Steady State Fluorescence Spectroscopy, and destructive methods such as High-Performance Liquid Chromatography coupled with Diode-Array Detection (HPLC-DAD) and Electrospray Ionization High-Resolution Mass Spectrometry (ESI-HRMS), were utilized for a comprehensive analysis. The examined red shades were identified as originating from either a single pigment or dye, or a combination of both. XRF analysis detected red lead/litharge in 14 postage stamps, vermilion in 8 and iron oxide in 4. The mapping results obtained by this technique were shown to be very important in the determination of inorganic pigments. Most specimens contained a natural organic dye, with carminic acid being the most prevalent, appearing in 30 samples. In contrast, alizarin was identified in only 3 of the examined postage stamps. A synthetic dye, eosin Y, first synthesised by Heinrich Caro in 1871, was detected in 11 stamps and suggested by FORS and steady-state fluorescence in 6 others printed from 1879 onwards. HPLC-HRMS provided more detailed information on the natural colorant. In 19 samples both organic and inorganic dyes or pigments were found to coexist. It has been shown that spectroscopic techniques, when used with an appropriate database, can play a role in suggesting the presence of certain compounds that are subsequently detected by other techniques.

Where Did Vessels Come from? A Study of Pottery Provenance from the Site of Velika Humska Čuka, Serbia

The archaeological materials from the Velika Humska Čuka site on the northern fringe of the Niš Basin in southeastern Serbia were analyzed to reveal the provenance of ceramics and other artifacts. This study focused on the elemental analysis of 61 samples, including local clay pits, potsherds, and whole vessels. Samples were chosen based on stylistic and typological characteristics to distinguish local and "foreign" pottery. Elemental analysis was conducted using energy-dispersive X-ray fluorescence (EDXRF) spectrometry, complemented by principal component analysis (PCA) for data interpretation. Results indicated that the majority of pottery samples, over 80%, were produced using local clay from deposits near the site. However, approximately 20% of the analyzed vessels were made using clay from deposits near the Bubanj site, 8 km south of Velika Humska Čuka. A vessel on a hollow high foot combining stylistic elements of the Bubanj-Hum I group and Early Eneolithic Pannonian groups was made of clay not sourced from any identified local deposits, suggesting its non-local origin. While the predominance of local materials suggests self-sufficient production, the use of non-local clays and stylistic influences highlights long-distance connections and exchanges. The study emphasizes the importance of Velika Humska Čuka in understanding the development of ceramic traditions and the cultural dynamics of the Early Eneolithic in the Central Balkans.

Investigating gunshot wounds in charred bone with XRF spectroscopy: a technical note

The analysis of traces of injuries can be difficult in cases of charred human remains since the alteration and fragmentation are high. The aim of this study is to explore the use of X-Ray Fluorescence (XRF) technique as a screening tool for detecting and analyzing gunshot residues (GSR) on cremated and highly fragmented materials, as it is a technique that allows for fast qualitative investigations without altering the sample or requiring sample preparation. The study was carried out on two steps: firstly, on completed skeletonized bones to verify if GSR survive to burning; secondly, we considered a more realistic situation, in which soft tissues were present before the shooting. To this aim, nine adult bovine ribs, four retaining soft tissue, five completely skeletonized, were subjected to a shooting test using two types of 9 mm projectiles (jacketed and unjacketed bullets). The ribs were then burnt until complete calcination in an electric furnace. The entry wound of each rib was analyzed using XRF, revealing traces of GSR. The XRF analysis showed that all samples, except for one, contain Pb and/or Sb near the lesion. Furthermore, the samples hit by unjacketed bullets had a more significant presence of Pb in macroscopic yellow areas, which persisted when moving away from the gunshot. These findings could pave the way for the use of XRF technology, mostly when a fast and immediate scan must be done on osteologic materials by a conservative method.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.

Performance improvement of underwater LIBS qualitative and quantitative analysis by irradiating with long nanosecond pulses

Long nanosecond pulses have been proven to be efficient at enhancing underwater LIBS emission. However, the quantitative analytical capability of underwater long-pulse LIBS has yet to be further revealed. In this work, we investigated the spectral characteristics by irradiating with a laser pulse of 120 ns duration. The alkali and alkaline earth metals Li, K and Ca and the transition element Mn were selected for analysis. It is shown that obvious self-reversal structures were observed in the spectra at high concentrations, making the calibration curves saturated. Correction was performed using the approximate Voigt function fitting method, which significantly improves the linearity of the calibration curves. In addition to the target metal elements, atomic lines of the matrix elements H and O in water were also observed, which can serve as promising internal standards for quantitative analysis. A comparison of the quantification performance with and without the internal standards demonstrates that the use of the internal standards is conducive to improving the robustness of the calibration approaches with higher determination coefficients. More importantly, the underwater LIBS signal stability is improved by more than 3 times, and the prediction error for validation samples is reduced by 2-4 times. The present results suggest that long ns pulses are favorable to significantly improving the qualitative and quantitative performance of underwater single-pulse LIBS, enabling long-pulse LIBS to have great potential to be applied to underwater in situ chemical analysis.

In situ microwave-assisted preparation of NS-codoped carbon dots stabilized silver nanoparticles as an off-on fluorescent probe for trace Hg2+ detection

An off-on fluorescent probe (NS-CDs-AgNPs) was synthesized based on a one-pot microwave process by utilizing N, S co-doping carbon dots (NS-CDs) and silver nitrate as precursors. The significant peak of NS-CDs-AgNPs at 393 nm in ultraviolet spectrum indicated silver nanoparticle (AgNPs) were successfully synthesized. A faint blue fluorescence emission (442 nm) was displayed when excited NS-CDs-AgNPs at 371 nm. A remarkable fluorescence recovery was observed upon adding of trance Hg2+, whereas the other heavy metal ions did not elicit this response. The reason for this phenomenon was revealed in this work that a spontaneous redox reaction occurred between NS-CDs-AgNPs and Hg2+, which leaded to the formation of NS-CDs-Agn-2NPsHg complexes. On the basis of this mechanism, a new off-on fluorescent analytical method was constructed for Hg2+ detection with linear range of 10-400 nM (R2 = 0.9941), and the detection limit (LOD) of 5.16 nM. Additionally, satisfactory recovery (90.28%-106.13%) and the relative standard deviation (RSD) (RSD＜5.21%) were obtained in water sample detection. More importantly, the NS-CDs-AgNPs exhibited lower cytotoxicity and better biocompatibility, indicating a huge potential in cell imaging and clinical medicine.

Quantitative Composition and Mesoscale Ion Distribution in p-Type Organic Mixed Ionic-Electronic Conductors

Understanding the ionic composition and distribution in organic mixed ionic-electronic conductors (OMIECs) is crucial for understanding their structure-property relationships. Despite this, direct measurements of OMIEC ionic composition and distribution are not common. In this work, we investigated the ionic composition and mesoscopic structure of three typical p-type OMIEC materials: an ethylene glycol-treated crosslinked OMIEC with a large excess fixed anionic charge (EG/GOPS-PEDOT:PSS), an acid-treated OMIEC with a tunable fixed anionic charge (crys-PEDOT:PSS), and a single-component OMIEC without any fixed anionic charge (pg2T-TT). A combination of X-ray fluorescence (XRF) and X-ray photoelectron spectroscopies, gravimetry, coulometry, and grazing incidence small-angle X-ray scattering (GISAXS) techniques was employed to characterize these OMIECs following electrolyte exposure and electrochemical cycling. In particular, XRF provided quantitative ion-to-monomer compositions for these OMIECs from passive ion uptake following aqueous electrolyte exposure and potential-driven ion uptake/expulsion following electrochemical doping and dedoping. Single-ion (cation) transport in EG/GOPS-PEDOT:PSS due to Donnan exclusion was directly confirmed, while significant fixed anion concentrations in crys-PEDOT:PSS doping and dedoping were shown to occur through mixed anion and cation transport. Controlling the fixed anionic (PSS^-) charge density in crys-PEDOT:PSS mapped the strength of Donnan exclusion in OMIEC systems following a Donnan-Gibbs model. Anion transport dominated pg2T-TT doping and dedoping, but a surprising degree of anionic charge trapping (∼10²⁰ cm^-3) was observed. GISAXS revealed minimal ion segregation both between PEDOT- and PSS-rich domains in EG/GOPS-PEDOT:PSS and between amorphous and semicrystalline domains in pg2T-TT but showed significant ion segregation in crys-PEDOT:PSS at length scales of tens of nm, ascribed to inter-nanofibril void space. These results bring new clarity to the ionic composition and distribution of OMIECs which are crucial for accurately connecting the structure and properties of these materials.

Ultra-repeatability measurement of calorific value of coal by NIRS-XRF

Calorific value is an important indicator to evaluate the comprehensive quality of coal, and its real-time and rapid analysis is of great significance for optimizing the coal blending process and improving boiler combustion efficiency. Traditional assays are time-consuming, and prompt gamma neutron activation analysis (PGNAA) and laser-induced breakdown spectroscopy (LIBS) have certain limitations. In this paper, a novel technique for ultra-repeatability measurement of coal calorific value by combining near-infrared spectroscopy (NIRS) and X-ray fluorescence (XRF) is proposed. In this NIRS-XRF technology, the former can stably measure organic components such as C-H and N-H that are positively correlated with the calorific value, while the latter can stably measure inorganic elements such as Na, Al, Si, Ca, Fe, and Mn that are negatively correlated with the calorific value. The combination of the two can greatly improve the measurement repeatability of coal calorific value. In the quantitative analysis algorithm, a holistic-segmented prediction model based on partial least squares (PLS) is proposed, that is, the holistic model is used to roughly predict the calorific value and determine the segment accordingly, and then the corresponding segmented model is used to accurately predict the calorific value. The experimental results show that the root mean square error of prediction (RMSEP), the average relative error (ARE), and the standard deviation (SD) of this method for predicting the calorific value of coal are 0.71 MJ kg^-1, 1.18% and 0.07 MJ kg^-1 respectively. The measurement repeatability meets the requirements of the Chinese national standard. This calorific value measurement technology based on NIRS-XRF is safe, fast, and stable, providing a new way to optimize and control the utilization process of coal in coal washing plants, power plants, coking, and other industries.

Elemental and spectral chemometric analyses of Octopus vulgaris beaks as reliable markers of capture location

The high demand and economic relevance of cephalopods make them prone to food fraud, including related to harvest location. Therefore, there is a growing need to develop tools to unequivocally confirm their capture location. Cephalopod beaks are nonedible, making this material ideal for traceability studies as it can also be removed without a loss of commodity economic value. Within this context, common octopus (Octopus vulgaris) specimens were captured in five fishing areas along the Portuguese coast. Untargeted multi-elemental total X-ray fluorescence analysis of the octopus beaks revealed a high abundance of Ca, Cl, K, Na, S, and P, concomitant with the keratin and calcium phosphate nature of the material. We tested a suite of discrimination models on both elemental and spectral data, where the elements contributing most to discriminate capture location were typically associated with diet (As), human-related pressures (Zn, Se, and Mn), or geological features (P, S, Mn, and Zn). Among the six different chemometrics approaches used to classify individuals to their capture location according to their beaks' element concentration, classification trees attained a classification accuracy of 76.7%, whilst reducing the number of explanatory variables for sample classification and highlighting variable importance for group discrimination. However, using X-ray spectral features of the octopus beaks further improved classification accuracy, with the highest classification of 87.3% found with partial least-squares discriminant analysis. Ultimately, element and spectral analyses of nonedible structures such as octopus beaks can provide an important, complementary, and easily accessible means to support seafood provenance and traceability, whilst integrating anthropogenic and/or geological gradients.

Improved chemometric approach for XRF data treatment: application to the reverse glass paintings from the Lipari collection

The Aeolian cultural heritage preserves hundreds of testimonies of the past that have passed through six millennia of history. Among these, the Archeological Park of the Aeolian Islands with the Museum Luigi Bernabò Brea (Italy) preserves a valuable set of artworks, which are related to a little-known 'popular' figurative heritage. It is an assemblage of small glass foils decorated using the technique of reverse painting, datable to between the end of the 17^th century and the end of the 18^th century, and actually under investigation by historians. Here, an X-ray fluorescence (XRF) spectroscopy study (performed with portable equipment) is combined with a multivariate approach that allows us to define the best way to process the data to detect compositional differences and similarities among the glass supports. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were applied both on normalized spectra and on normalized peak areas in order to establish the chemometric approach with the highest grouping ability. Results showed that the analysis of the normalized area provides the most reliable grouping based on the different elemental compositions, without problems coming from the background or peak-shape distortions. The obtained results can be used by researchers involved in the analysis of XRF data as a guideline to perform chemometrics. Furthermore, regarding the reverse glass, they can be divided into different typologies based on composition differences, providing a further discrimination criterion for historians involved in the study of the collection to determine the provenance and dating of the items.

Combination of Total-Reflection X-Ray Fluorescence Method and Chemometric Techniques for Provenance Study of Archaeological Ceramics

The provenance study of archaeological materials is an important step in understanding the cultural and economic life of ancient human communities. One of the most popular approaches in provenance studies is to obtain the chemical composition of material and process it with chemometric methods. In this paper, we describe a combination of the total-reflection X-ray fluorescence (TXRF) method and chemometric techniques (PCA, k-means cluster analysis, and SVM) to study Neolithic ceramic samples from eastern Siberia (Baikal region). A database of ceramic samples was created and included 10 elements/indicators for classification by geographical origin and ornamentation type. This study shows that PCA cannot be used as the primary method for provenance purposes, but can show some patterns in the data. SVM and k-means cluster analysis classified most of the ceramic samples by archaeological site and type with high accuracy. The application of chemometric techniques also showed the similarity of some samples found at sites located close to each other. A database created and processed by SVM or k-means cluster analysis methods can be supplemented with new samples and automatically classified.

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products

The halal status of meat products is an important factor being considered by many parties, especially Muslims. Analytical methods that have good specificity for the authentication of halal meat products are important as quality assurance to consumers. Metabolomic and lipidomic are two useful strategies in distinguishing halal and non-halal meat. Metabolomic and lipidomic analysis produce a large amount of data, thus chemometrics are needed to interpret and simplify the analytical data to ease understanding. This review explored the published literature indexed in PubMed, Scopus, and Google Scholar on the application of chemometrics as a tool in handling the large amount of data generated from metabolomic and lipidomic studies specifically in the halal authentication of meat products. The type of chemometric methods used is described and the efficiency of time in distinguishing the halal and non-halal meat products using chemometrics methods such as PCA, HCA, PLS-DA, and OPLS-DA is discussed.

TXRF spectral information enhanced by multivariate analysis: A new strategy for food fingerprint

The increased costumers' request of safe and high-quality food products makes food traceability a priority for frauds identification and quality certification. Elemental profiling is one of the strategies used for food traceability, and TXRF spectroscopy is widely used in food analysis even if its potentialities have not been fully investigated. In this work, a new method for food traceability using directly TXRF spectra coupled with multivariate analyses, was tested. Twenty-four different beans' genotypes (Phaseolus vulgaris L.) grown onto two different sites have been studied. After the development of the method for beans' analysis, TXRF spectra were collected and processed with PCA combined with SNV and GLSW filter obtaining a perfect clustering of the seeds according to their geographical origin. Finally, using PLS-DA, beans were correctly classified demonstrating that TXRF spectra can be successfully used as fingerprint for food/seed traceability and that elemental quantification procedure is not necessary to this aim.

Harnessing the Full Power of Chemometric-Based Analysis of Total Reflection X-ray Fluorescence Spectral Data to Boost the Identification of Seafood Provenance and Fishing Areas

Provenance and traceability are crucial aspects of seafood safety, supporting managers and regulators, and allowing consumers to have clear information about the origin of the seafood products they consume. In the present study, we developed an innovative spectral approach based on total reflection X-ray fluorescence (TXRF) spectroscopy to identify the provenance of seafood and present a case study for five economically relevant marine species harvested in different areas of the Atlantic Portuguese coast: three bony fish-Merluccius merluccius, Scomber colias, and Sparus aurata; one elasmobranch-Raja clavata; one cephalopod-Octopus vulgaris. Applying a first-order Savitzky-Golay transformation to the TXRF spectra reduced the potential matrix physical effects on the light scattering of the X-ray beam while maintaining the spectral differences inherent to the chemical composition of the samples. Furthermore, a variable importance in projection partial least-squares discriminant analysis (VIP-PLS-DA), with k - 1 components (where k is the number of geographical origins of each seafood species), produced robust high-quality models of classification of samples according to their geographical origin, with several clusters well-evidenced in the dispersion plots of all species. Four of the five species displayed models with an overall classification above 80.0%, whereas the lowest classification accuracy for S. aurata was 74.2%. Notably, about 10% of the spectral features that significantly contribute to class differentiation are shared among all species. The results obtained suggest that TXRF spectra can be used for traceability purposes in seafood species (from bony and cartilaginous fishes to cephalopods) and that the presented chemometric approach has an added value for coupling with classic TXRF spectral peak deconvolution and elemental quantification, allowing characterization of the geographical origin of samples, providing a highly accurate and informative dataset in terms of food safety.

Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

In order to extract useful information from X-ray fluorescence (XRF) spectra and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combining a deep belief network (DBN) with a tree-based model was proposed. The DBN was first introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectra. Owing to the strong regression ability of the tree-based model, it can offset the deficiency of DBN in prediction ability so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R² of As and Pb reached 0.9884 and 0.9358, the mean square error of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results.

A Green Approach Based on Micro-X-ray Fluorescence for Arsenic, Micro- and Macronutrients Detection in Pteris vittata

In this study, benchtop micro-X-ray fluorescence spectrometry (µXRF) was evaluated as a green and cost-effective multielemental analytical technique for P. vittata. Here, we compare the arsenic (As) content values obtained from the same samples by µXRF and inductively coupled plasma-optical emissions spectrometry (ICP–OES). To obtain samples with different As concentrations, fronds at different growth time points were collected from P. vittata plants grown on two natural As-rich soils with either high or moderate As (750 and 58 mg/kg). Dried samples were evaluated using multielement-µXRF analysis and processed by PCA. The same samples were then analysed for multielement concentrations by ICP–OES. We show that As concentrations detected by ICP–OES, ranging from 0 to 3300 mg/kg, were comparable to those obtained by µXRF. Similar reliability was obtained for micro- and macronutrient concentrations. A positive correlation between As and potassium (K) contents and a negative correlation between As and iron (Fe), calcium (Ca) and manganese (Mn) contents were found at both high and moderate As. In conclusion, we demonstrate that this methodological approach based on μXRF analysis is suitable for monitoring the As and element contents in dried plant tissues without any chemical treatment of samples and that changes in most nutrient concentrations can be strictly related to the As content in plant tissue.

Expansion Properties of Cemented Foam Backfill Utilizing Coal Gangue and Fly Ash

The cemented backfill (CB) utilizing coal gangue (CG) and fly ash (FA) is widely applied in coal mines. However, the bleeding and shrinkage of CB leads to insufficient contact with surrounding rock, which is not beneficial for controlling roof subsidence and even stope stability. Herein, a cemented foam backfill (CFB) formulation is demonstrated, employing hydrogen dioxide (H2O2) as a chemical foaming agent. The cement and FA show noticeable inhibiting effects on volume expansion due to the network formed by their hydrates. Moderately lower cement, FA, and solid concentration are beneficial to improve volume increment and prolong expanding duration. A foaming coefficient (k) is proposed in theory to evaluate the foaming efficiency. The kem values, determined by volume evolution experiments of CFB slurries, provide a calculation basis for the needed dosage of H2O2 solution targeting specific volume increment. CFB specimens with expanding ratios of 21%~103% and densities of 994~592 kg/cm3 were prepared, with an actual foaming coefficient of 52.40 cm3/g and uniaxial compressive strength (UCS) of 0.32~0.55 MPa. The mass of H2O2 solution was 1.9%~11.3% of cement and 0.29%~1.67% of total solid materials by weight. The UCS decline compared to CB was attributed to rich pores observed by CT and carbonation indicated by X-ray diffraction (XRD).

Prediction Model of Aluminized Coating Thicknesses Based on Monte Carlo Simulation by X-ray Fluorescence

An aluminized coating can improve the high-temperature oxidation resistance of turbine blades, but the inter-diffusion of elements renders the coating’s thickness difficult to achieve in non-destructive testing. As a typical method for coating thickness inspection, X-ray fluorescence mainly includes the fundamental parameter method and the empirical coefficient method. The fundamental parameter method has low accuracy for such complex coatings, while it is difficult to provide sufficient reference samples for the empirical coefficient method. To achieve accurate non-destructive testing of aluminized coating thickness, we analyzed the coating system of aluminized blades, simulated the spectra of reference samples using the open-source software XMI-MSIM, established the mapping between elemental spectral intensity and coating thickness based on partial least squares and back-propagation neural networks, and validated the model with actual samples. The experimental results show that the model’s prediction error based on the back-propagation neural network is 4.45% for the Al-rich layer and 16.89% for the Al-poor layer. Therefore, the model is more suitable for predicting aluminized coating thickness. Furthermore, the Monte Carlo simulation method can provide a new way of thinking for materials that have difficulty in fabricating reference samples.

Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy

The current study proposes a novel analytical method for calculating the breakdown voltage (BV) of transformer oil samples considered as a significant method to assess the safe operation of power industry. Transformer oil samples can be analyzed using the Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate calibration methods. The partial least squares regression (PLSR) back propagation-artificial neural network (BP-ANN) methods and a genetic algorithm (GA) for variable selection are used to predict and assess breakdown voltage in transformer oil samples from various Iranian transformer oils. As a result, the root mean square error (RMSE) and correlation coefficient for the training and test sets of oil samples are also calculated. In the GA-PLS-R method, the squared correlation coefficient (R²pred) and root mean square prediction error (RMSEP) are 0.9437 and 2.6835, respectively. GA-BP-ANN, on the other hand, had a lower RMSEP value (0.2874) and a higher R²pred function (0.9891). Considering the complexity of transformer oil samples, the performance of GA-BP-ANN has resulted in an efficient approach for predicting breakdown voltage; consequently, it can be effectively used as a new method for quantitative breakdown voltage analysis of samples to evaluate the health of transformer oil. .

Lactoperoxidase potential in diagnosing subclinical mastitis in cows via image processing

This report describes how image processing harnessed to multivariate analysis techniques can be used as a bio-analytical tool for mastitis screening in cows using milk samples collected from 48 animals (32 from Jersey, 7 from Gir, and 9 from Guzerat cow breeds), totalizing a dataset of 144 sequential images was collected and analyzed. In this context, this methodology was developed based on the lactoperoxidase activity to assess mastitis using recorded images of a cuvette during a simple experiment and subsequent image treatments with an R statistics platform. The color of the sample changed from white to brown upon its exposure to reagents, which is a consequence of lactoperoxidase enzymatic reaction. Data analysis was performed to extract the channels from the RGB (Red-Green-Blue) color system, where the resulting dataset was evaluated with Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Second-Order Regression (SO). Interesting results in terms of enzymatic activity correlation (R² = 0.96 and R² = 0.98 by MLR and SO, respectively) and of somatic cell count (R² = 0.97 and R² = 0.99 by MLR and SO, respectively), important mastitis indicators, were obtained using this simple method. Additionally, potential advantages can be accessed such as quality control of the dairy chain, easier bovine mastitis prognosis, lower cost, analytical frequency, and could serve as an evaluative parameter to verify the health of the mammary gland.

ED-XRF: a promising method for accurate and rapid quantification of metals in a bacterial matrix

ABSTRACTThe remediation of metal-polluted water using bacterial biofilms is a promising technology. In order to help its development, the present study aims to evaluate the feasibility to utilize XRF spectrometry for accurate and rapid measurement of metal concentrations in bacterial biofilms used in treatment plants. For that purpose, an ED-XRF spectrometer was used to measure Cd, Cu, Fe, Mn, Ni and Zn concentrations within a matrix of marine bacteria Pseudomonas fluorescens BA3SM1 and its metabolites. Contaminated and control cultures of the strain BA3SM1 were dried and crushed, then analysed by ED-XRF. The LOD value of the analysed metals was between 2.08 and 10.5 µg g^-1. Metal concentrations were also measured by ICP-AES or ICP-MS to support ED-XRF results. The two techniques showed a good linear correlation with a slope of at least 0.949 and R² of at least 0.985. These results confirm the possibility to measure metal contents by ED-XRF in bacterial matrices.

Applied Analytical Methods for Detecting Heavy Metals in Medicinal Plants

For thousands of years, medicinal plants (MPs) have been one of the main sources of drugs worldwide. However, recently, heavy metal pollution has seriously affected the quality and safety of MPs. Consuming MPs polluted by heavy metals such as Pb, Hg, and Cu significantly threaten the health of consumers. To manage this situation, the levels of heavy metals in MPs must be controlled. In recent years, this field has attracted significant attention, but few researchers have systematically summarized various analytical methods. Therefore, it is necessary to investigate methods that can accurately and effectively detect the amount of heavy metals in MPs. Herein, some important analytical methods used to detect heavy metals in MPs and their applications have been introduced and summarized in detail. These include atomic absorption spectrometry, atomic fluorescence spectrometry, inductively coupled plasma mass spectrometry, inductively coupled plasma atomic emission spectrometry, X-ray fluorescence spectrometry, neutron activation analysis, and anodic stripping voltammetry. The characteristics of these methods were subsequently compared and analyzed. In addition, high-performance liquid chromatography, ultraviolet spectrophotometry, and disposable electrochemical sensors have also been used for heavy metal detection in MPs. To elucidate the systematic and comprehensive information, these methods have also been briefly introduced in this review.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.

More than XRF Mapping: STEAM (Statistically Tailored Elemental Angle Mapper) a Pioneering Analysis Protocol for Pigment Studies

Among the possible variants of X-Ray Fluorescence (XRF), applications exploiting scanning Macro-XRF (MA-XRF) are lately widespread as they allow the visualization of the element distribution maintaining a non-destructive approach. The surface is scanned with a focused or collimated X-ray beam of millimeters or less: analyzing the emitted fluorescence radiation, also elements present below the surface contribute to the elemental distribution image obtained, due to the penetrative nature of X-rays. The importance of this method in the investigation of historical paintings is so obvious—as the elemental distribution obtained can reveal hidden sub-surface layers, including changes made by the artist, or restorations, without any damage to the object—that recently specific international conferences have been held. The present paper summarizes the advantages and limitations of using MA-XRF considering it as an imaging technique, in synergy with other hyperspectral methods, or combining it with spot investigations. The most recent applications in the cultural Heritage field are taken into account, demonstrating how obtained 2D-XRF maps can be of great help in the diagnostic applied on Cultural Heritage materials. Moreover, a pioneering analysis protocol based on the Spectral Angle Mapper (SAM) algorithm is presented, unifying the MA-XRF standard approach with punctual XRF, exploiting information from the mapped area as a database to extend the comprehension to data outside the scanned region, and working independently from the acquisition set-up. Experimental application on some reference pigment layers and a painting by Giotto are presented as validation of the proposed method.

Insights into leaded gasoline registered in mud depocenters derived from multivariate statistical tool: southeastern Brazilian coast

Lead has been widely used since antiquity, but its uses drastically increased during the Industrial Revolution. The global emission of Pb into the environment was mainly due to tetraethyl lead added to gasoline as an antiknock additive. Because of its toxicity and neurological effects, the compound was phased out in the 1980s. Isotopic signatures are widely applied to differentiate sources of Pb; however, this is an expensive and sophisticated analysis compared to elemental analysis. Thus, this study aims to gain insight into leaded gasoline registered in mud depocenters from the southeastern Brazilian coast using multivariate statistical tools on elemental analysis data of trace elements. Seven multiple cores were collected on board the Research Vessel Alpha Crucis. Al, As, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, Sc, Sr, V and Zn were analyzed by acid digestion and quantified by ICP-OES. Levels and enrichment factors of Pb resulted in homogeneous columns, indicating that small variations in concentrations can be attributed to grain size differences, not presenting contaminated levels. From statistical results, the highest contribution on the first component was represented by a lithogenic source with the leaching of continental rocks. Lead content was notable in its high loadings in other components, which suggests atmospheric deposition. An increase in these components in subsurface samples from vertical profiles between 1935 and 1996 could represent a fingerprint of the consumption of leaded gasoline in Brazil between 1923 and 1989. Thus, statistical analysis of elemental data enabled to infer possible sources and pathways of Pb to the environment, without isotopic analysis.

Organ Metallome Processed with Chemometric Methods Enable the Determination of Elements that May Serve as Markers of Exposure to Iron Oxide Nanoparticles in Male Rats

The systemic influence of iron oxide nanoparticles on the elemental homeostasis of key organs was examined in male rats. In tissues taken at different intervals from nanoparticles injection, the dynamics of elemental changes was analyzed. The organ metallome was studied using total reflection X-ray fluorescence. The obtained data were processed with advanced cluster and discriminant analyses-to classify the tissues according to their organs of origin and to distinguish accurately the nanoparticle-treated and normal rats. Additionally, in the case of liver and heart, it was possible to determine the elements of highest significance for different treatments, which may serve as markers of exposure to iron oxide nanoparticles.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Thickness determination of metal multilayers by ED-XRF multivariate analysis using Monte Carlo simulated standards

We present the thickness measurement of multilayer samples by X-ray fluorescence (XRF) using calibration curves obtained from simulated spectra through Monte Carlo (MC) algorithm. The XRF is a widespread technique for the analysis of single and multilayer films but the accuracy of quantitative analysis must be increased. Moreover, the use certified standards is not easy to implement due to the high variability of combination and/or concentration in layered samples. The results of this work were compared with fundamental parameter (FP) method and focussed ion beam scanning electron microscopy (FIB-SEM) analysis. The results show good quantitative values even without the use of any standard with known thickness. In addition to having built the calibration curves with a simple univariate approach, also multivariate data analysis was performed to consider multiple variables simultaneously. From the comparison of the obtained results, it can be inferred that the univariate analysis worked well in the case of single layer samples and in the determination of the upper layer in multilayer samples but only multivariate analysis, taking into account the matrix effect of each layer, provided maximum accuracy on each layer of multilayer samples.

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.

Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named "meteor-wrongs". In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.

Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO2 and CaCO3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w⁻¹ followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R² ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs

Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.

Low-Input Crops as Lignocellulosic Feedstock for Second-Generation Biorefineries and the Potential of Chemometrics in Biomass Quality Control

Lignocellulose feedstock (LCF) provides a sustainable source of components to produce bioenergy, biofuel, and novel biomaterials. Besides hard and soft wood, so-called low-input plants such as Miscanthus are interesting crops to be investigated as potential feedstock for the second generation biorefinery. The status quo regarding the availability and composition of different plants, including grasses and fast-growing trees (i.e., Miscanthus, Paulownia), is reviewed here. The second focus of this review is the potential of multivariate data processing to be used for biomass analysis and quality control. Experimental data obtained by spectroscopic methods, such as nuclear magnetic resonance (NMR) and Fourier-transform infrared spectroscopy (FTIR), can be processed using computational techniques to characterize the 3D structure and energetic properties of the feedstock building blocks, including complex linkages. Here, we provide a brief summary of recently reported experimental data for structural analysis of LCF biomasses, and give our perspectives on the role of chemometrics in understanding and elucidating on LCF composition and lignin 3D structure.