Mid-infrared Spectroscopy Research Articles

Mid-infrared spectroscopy with a broadly tunable thin-film lithium niobate optical parametric oscillator

Mid-infrared spectroscopy, an important technique for sensing molecules, has encountered barriers from sources either limited in tuning range or excessively bulky for widespread use. We present a compact, efficient, and broadly tunable optical parametric oscillator surmounting these challenges. Leveraging dispersion-engineered thin-film lithium niobate-on-sapphire photonics and a singly resonant cavity allows broad, controlled tuning over an octave from 1.5–3.3 µm. The device generates >25mW of mid-infrared light at 3.2 µm with 15% conversion efficiency. The ability to precisely control the device’s mid-infrared emission enables spectroscopy of methane and ammonia, demonstrating our approach’s relevance for sensing. Our work signifies an important advance in nonlinear photonics miniaturization, bringing practical field applications of high-speed, broadband mid-infrared spectroscopy closer to reality.

Measurements of absolute line strength of the ν1 fundamental transitions of OH radical and rate coefficient of the reaction OH + H2O2 with mid-infrared two-color time-resolved dual-comb spectroscopy

Absolute line strengths of several transitions in the ν1 fundamental band of the hydroxyl radical (OH) have been measured by simultaneous determination of hydrogen peroxide (H2O2) and OH upon laser photolysis of H2O2. Based on the well-known quantum yield for the generation of OH radicals in the 248-nm photolysis of H2O2, the line strength of the OH radicals can be accurately derived by adopting the line strength of the well-characterized transitions of H2O2 and analyzing the difference absorbance time traces of H2O2 and OH obtained upon laser photolysis. Employing a synchronized two-color dual-comb spectrometer, we measured high-resolution time-resolved absorption spectra of H2O2 near 7.9 µm and the OH radical near 2.9 µm, simultaneously, under varied conditions. In addition to the studies of the line strengths of the selected H2O2 and OH transitions, the kinetics of the reaction between OH and H2O2 were investigated. A pressure-independent rate coefficient kOH+H2O2 was determined to be [1.97 (+0.10/−0.15)] × 10−12 cm3 molecule−1 s−1 at 296 K and compared with other experimental results. By carefully analyzing both high-resolution spectra and temporal absorbance profiles of H2O2 and OH, the uncertainty of the obtained OH line strengths can be achieved down to <10% in this work. Moreover, the proposed two-color time-resolved dual-comb spectroscopy provides a new approach for directly determining the line strengths of transient free radicals and holds promise for investigations on their self-reaction kinetics as well as radical–radical reactions.

Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation

Accurate monitoring of soil organic carbon (SOC) is critical for sustainable management of soil for improving its quality, function, and carbon sequestration. As a nondestructive, efficient, and low-cost technique, mid-infrared (MIR) spectroscopy has shown a great potential in rapid estimation of SOC, despite limited studies of the global scale. The objective of this work was to use a globally distributed topsoil MIR spectral library with 33,039 samples to predict SOC using different modeling methods. Effects of nine fractional-order derivatives (FODs) on the predicted accuracy of SOC were evaluated using four regression algorithms (i.e., ratio index-based linear regression, RI-LR; partial least squares regression, PLSR; Cubist; convolutional neural network, CNN). Square-root transformation to SOC data was performed to minimize the skewness and non-linearity. Results indicated FOD to capture the subtle spectral details related to SOC, leading to improved predictions that may not be possible by the raw absorbance and common integer-order derivatives. Concerning the RI-LR models, the optimal validation result for SOC was obtained by 0.75-order derivative, with the ratio of performance to inter-quartile distance (RPIQ) of 1.85. Regarding the full-spectrum modeling for SOC, the CNN outperformed PLSR and Cubist models, irrespective of raw absorbance or eight FODs; the best-performing CNN model was achieved by 1.25-order derivative (validation RPIQ = 6.33). It can be concluded that accurate estimation of SOC using large and diverse MIR spectral library at the global scale combined with deep-learning CNN model is feasible. This global-scale database is extremely valuable for us to deal with the shortage of soil data and to monitor the soils at different geographical scales.

Highly efficient octave-spanning long-wavelength infrared generation with a 74% quantum efficiency in a χ(2) waveguide

The realization of compact and efficient broadband mid-infrared (MIR) lasers has enormous impacts in promoting MIR spectroscopy for various important applications. A number of well-designed waveguide platforms have been demonstrated for MIR supercontinuum and frequency comb generations based on cubic nonlinearities, but unfortunately third-order nonlinear response is inherently weak. Here, we propose and demonstrate for the first time a χ(2) micrometer waveguide platform based on birefringence phase matching for long-wavelength infrared (LWIR) laser generation with a high quantum efficiency. In a ZnGeP2-based waveguide platform, an octave-spanning spectrum covering 5–11 μm is generated through optical parametric generation (OPG). A quantum conversion efficiency of 74% as a new record in LWIR single-pass parametric processes is achieved. The threshold energy is measured as ~616 pJ, reduced by more than 1-order of magnitude as compared to those of MIR OPGs in bulk media. Our prototype micro-waveguide platform could be extended to other χ(2) birefringence crystals and trigger new frontiers of MIR integrated nonlinear photonics.

Chemical and Spectroscopic Characteristics of Humic Acids Under No-Tillage and Forest Systems

ABSTRACT This study aims to evaluate the no-tillage system (NT) with different implantation times and vegetation cover on changes in the chemical and structural compositions of soil humified organic matter. Five areas with different NT management histories were studied; for 6 (NT6), 14 (NT14), 22 (NT22) years with succession of soybean/corn crops; for 12 years and 4 more years with corn and Brachiaria (NT+B), pasture for 39 years and native forest. Soil samples were collected at the depths of 0.0–0.05 and 0.05–0.10 m. The changes in the quality of humic acid (HA) were evaluated by several chemical techniques (total organic carbon (TOC), fulvic acid fraction (FA), humic acid fraction (HA) and humin fraction (HU), ionizable functional groups, elemental composition) and spectroscopic techniques (UV–Vis absorption, fluorescence, and Fourier transform mid-infrared photoacoustic spectroscopy, called FTIR-PAS). On the surface, the TOC, carbon of the HA and HU fractions, total acidity, phenolic acidity, Carbon, Nitrogen, Sulfur, Hydrogen and Hydrogen/Carbon ratio were higher in the areas of forest, pasture and NT22. On the subsurface, the responses are the same as on the surface. However, the NT22 system presents higher FA, HA, carboxylic acidity and humification degree (HD). UV–Vis absorption spectroscopy, fluorescence spectroscopy and FTIR-PAS can discriminate soils under different managements and under natural conditions. Agronomic practices have strongly affected the soil organic matter, in which the NT promotes the formation of HA with characteristics closer to those of the forest, as the implantation time increases, such as 22-year-old NT and pasture. This indicates that care must be taken in managing the no-till system to maximize its potential to promote stabilization of organic matter, thereby contributing to carbon sequestration.

73. Mid-infrared spectroscopy can identify changes in plasma profiles associated with dietary interventions: Untargeted omic screening

73. Mid-infrared spectroscopy can identify changes in plasma profiles associated with dietary interventions: Untargeted omic screening

Mapping biochemical and nutritional changes in durum wheat due to spoilage during storage

Synchrotron X-ray imaging and spectroscopy techniques were used for studying changes during post-harvest storage of food grains. Three varieties (AAC Spitfire, CDC Defy, and AAC Stronghold) of the Canada Western Amber Durum (CWAD) wheat class were stored for five weeks at 17 % moisture content (wb). Control (dry) and stored moistened seeds were analyzed for biochemical and nutritional changes using synchrotron bulk X-ray fluorescence spectroscopy (SR-XRF), X-ray fluorescence imaging (SR-XFI), and mid-infrared (mid-IR) spectroscopy at the Canadian Light Source (CLS), Saskatoon, SK. All varieties of durum wheat were spoiled at the end of five week, and AAC Spitfire and CDC Defy varieties were most affected in nutritional composition and their distribution than AAC Stronghold. Variable response to changes in biochemical and nutrition were found in all three spoiled varieties of the same durum wheat class.

Near to Mid-infrared Spectroscopy of (65803) Didymos as Observed by JWST: Characterization Observations Supporting the Double Asteroid Redirection Test

The Didymos binary asteroid was the target of the Double Asteroid Redirection Test (DART) mission, which intentionally impacted Dimorphos, the smaller member of the binary system. We used the Near-Infrared Spectrograph and Mid-Infrared Instrument instruments on JWST to measure the 0.6–5 and 5–20 μm spectra of Didymos approximately two months after the DART impact. These observations confirm that Didymos belongs to the S asteroid class and is most consistent with LL chondrite composition, as was previously determined from its 0.6–2.5 μm reflectance spectrum. Measurements at wavelengths >2.5 μm show Didymos to have thermal properties typical for an S-complex asteroid of its size and to be lacking absorptions deeper than ∼2% due to OH or H2O. Didymos’ mid-infrared emissivity spectrum is within the range of what has been measured on S-complex asteroids observed with the Spitzer Space Telescope and is most consistent with emission from small (<25 μm) surface particles. We conclude that the observed reflectance and physical properties make the Didymos system a good proxy for the type of ordinary chondrite asteroids that cross near-Earth space, and a good representative of likely future impactors.

Halfway to the Peak: Spatially Resolved Star Formation and Kinematics in a z = 0.54 Dusty Galaxy with JWST/MIRI

We present JWST Mid-InfraRed Instrument/Medium-Resolution Spectrometer (MIRI/MRS) observations of an infrared luminous disk galaxy, FLS1, at z ∼ 0.54. With a lookback time of 5 Gyr, FLS1 is chronologically at the midpoint between the peak epoch of star formation and the present day. The MRS data provide maps of the atomic fine structure lines [Ar ii]6.99, [Ar iii]8.99, [Ne ii]12.81, and [Ne iii]15.55 μm, polycyclic aromatic hydrocarbon (PAH) features at 3.3, 6.2, and 11.3 μm, and the warm molecular gas indicators H2S(5) and H2S(3); all these emission features are spatially resolved. We find that the PAH emission is more extended along the northern side of the galaxy when compared to the well-studied star formation tracer [Ne ii]. The H2 rotational lines, which are shock indicators, are strongest and most extended on the southern side of the galaxy. [Ar ii] is the second brightest fine structure line detected in FLS1 and we show that it is a useful kinematic probe that can be detected with JWST out to z ∼ 3. Velocity maps of [Ar ii] show a rotating disk with signs of turbulence. Our results provide an example of how spatially resolved mid-infrared spectroscopy can allow us to better understand the star formation and interstellar medium conditions in a galaxy halfway back to the peak epoch of galaxy evolution.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Multi-parameter Soil Property Prediction Incorporating Mid-infrared Spectroscopy and Dropout Sequential Artificial Neural Network

Multi-parameter Soil Property Prediction Incorporating Mid-infrared Spectroscopy and Dropout Sequential Artificial Neural Network

Study of Microstructural, Nutritional, and Biochemical Changes in Hulled and Hulless Barley during Storage Using X-ray and Infrared Techniques.

Four varieties of barley (Esma, AC Metacalf, Tradition, and AB Cattlelac), representing four Canadian barley classes, were stored at 17% moisture content (mc) for 8 week. Stored barely was characterized using synchrotron X-ray phase contrast microcomputed tomography, synchrotron X-ray fluorescence imaging, and mid-infrared spectroscopy at the Canadian Light Source, Saskatoon. The deterioration was observed in all the selected varieties of barley at the end of 8 week of storage. Changes due to spoilage over time were observed in the grain microstructure and its nutrient distribution and composition. This study underscores the critical importance of the initial condition of barley grain microstructure in determining its storage life, particularly under unfavorable conditions. The hulled barley varieties showed more deterioration in microstructure than the hulless varieties of barley, where a direct correlation between microstructural changes and alterations in nutritional content was found. All selected barley classes showed changes in the distribution of nutrients (Ca, Fe, K, Mn, Cu, and Zn), but the two-row AC Metcalf variety exhibited more substantial variations in their nutrient distribution (Zn and Mn) than the other three varieties during storage. The two-row class barley varieties showed more changes in biochemical components (protein, lipids, and carbohydrates) than the six-row class varieties.

Heavy-element production in a compact object merger observed by JWST.

The mergers of binary compact objects such as neutron stars and black holes are of central interest to several areas of astrophysics, including as the progenitors of gamma-ray bursts (GRBs)1, sources of high-frequency gravitational waves (GWs)2 and likely production sites for heavy-element nucleosynthesis by means of rapid neutron capture (the r-process)3. Here we present observations of the exceptionally bright GRB 230307A. We show that GRB 230307A belongs to the class of long-duration GRBs associated with compact object mergers4-6 and contains a kilonova similar to AT2017gfo, associated with the GW merger GW170817 (refs. 7-12). We obtained James Webb Space Telescope (JWST) mid-infrared imaging and spectroscopy 29 and 61 days after the burst. The spectroscopy shows an emission line at 2.15 microns, which we interpret as tellurium (atomic mass A = 130) and a very red source, emitting most of its light in the mid-infrared owing to the production of lanthanides. These observations demonstrate that nucleosynthesis in GRBs can create r-process elements across a broad atomic mass range and play a central role in heavy-element nucleosynthesis across the Universe.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods.

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Critical evaluation of analytical methodologies for the discrimination of hemp and drug-type cannabis

Classification of cannabis samples as hemp- or drug-type is an ever-increasing problem worldwide. Colorimetric tests and infrared spectroscopy were selected and critically compared among the available techniques. Cannabis samples with different THC and CBD concentration were analyzed by paper-based analytical devices (µPAD) using the 4-aminophenol colorimetric reaction, attenuated total reflectance mid infrared spectroscopy (ATR-MIR), and diffuse reflectance near infrared spectroscopy (DR-NIR). For the µPAD analysis, cannabis samples were extracted with methanol and later mixed with reagents to develop the colored complex. On the other hand, MIR and NIR spectra were obtained directly on raw samples, and partial least square discriminant analysis (PLS-DA) was used for cannabis classification. All the selected methodologies successfully discriminate between hemp-type and drug-type samples, being suitable to be used in-field. Additionally, assayed methodologies together a reference gas chromatography mass spectrometry (GC–MS) were evaluated and compared from a Green Analytical point of view using the AGREE criteria.

Three pesticide-pesticide eutectic systems of boscalid: Mechanochemical preparation, characterization and mechanism analysis

Preparation of eutectic mixture is considered to be an efficient means to improve physical and chemical properties in many fields. In this work, three pesticide-pesticide eutectic systems of boscalid with diniconazole, uniconazole and 3,6-dichloropicolinic acid were prepared by mechanochemical method. The formation of eutectic mixtures was identified by differential scanning calorimetry and powder X-ray diffraction characterizations. From the constructed solid–liquid phase diagrams, it is discovered that these eutectic systems could form a eutectic when the molar fraction of boscalid is 0.500. In addition, all eutectic temperatures are more than 29 K lower than the higher melting points of the constituents in the systems, which eliminates the issue of the poor thermal stability of constituents and enables the treatment of them using melting-based operations. Ideal model and COSMO-RS theory also calculate these solid–liquid phase diagrams relatively accurately, and the calculated activity coefficient values prove that these systems do not deviate significantly from the ideal state. The distinct non-ideality at the eutectic composition of the three systems also suggests that the interactions of the three coformers and the boscalid differ. The formation of eutectic mixtures was further confirmed by other solid state characterizations including mid-infrared spectroscopy, Raman spectroscopy and etc. The accelerated stability test indicates the good stability of the three eutectic mixtures. Furthermore, the water solubility study suggests the boscalid in the eutectic systems has a higher solubility than raw boscalid. By using COSMO-RS modelling, the most typical homo/hetero-molecular complexes of these pesticides were obtained and analyzed. Based on the trend of affinity characterized by the Gibbs free energy of the generation of molecular complexes, it could be inferred that eutectic point could be viewed as the state for which affinity of hetero-molecular complexes is greatest. The affinity of hetero-molecular complexes also presents the interaction between boscalid and 3,6-dichloropicolinic acid is greater than the other two pesticides at the eutectic point. Furthermore, the calculated solvation free energies document the intermolecular interaction plays an important role in stabilizing the three eutectic systems. These eutectic systems solve the problem that the three coformers are unsuitable for melting-based procedures and improve the solubility, making them potential options for future pesticide formulation development.

Quantitative analysis of adulterated pear juice based on attenuated total reflectance mid-infrared spectroscopy and bagging PLS

The methods of pear juice adulteration have evolved from simple adding water to blending based on characteristic spectrum of juice, which greatly increases the difficulty of identification. Herein, a rapid and convenient analytical method based on attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy and bagging partial least square (PLS) was developed. A total of 63 samples consisted of three kind of adulteration were designed to simulate actual counterfeiting of pear juice. All samples are roughly evenly divided into the calibration and test sets. Three kinds of PLS models including full-spectrum PLS, local PLS with pre feature selection by the ReliefF algorithm and bagging PLS were trained and compared. On the independent test set, the bagging PLS model achieved the best performance, followed by the local PLS model. Specifically, the bagging PLS model with 40 members achieved a root-mean squared error of prediction (RMSEP) of 3.24 %, a 20 % decrease compared to the full spectrum PLS model. Moreover, the error of bagging PLS model on the calibration and test sets is closer, and there is no over-fitting phenomenon, indicating that its robustness is better, with good fitting and generalization. In today's world where computing resources are not a problem, using simple model ensemble is highly desirable.

Metal-organic frameworks combined with mid-infrared spectroscopy for the trace analysis of phosphates in water

Detecting traces of phosphates in water is critical for monitoring water quality in aquatic ecosystems. Mid-infrared techniques are effective for label-free and quick measurements in at-line or in-line applications, but sensitivity is limited due to high water absorption. To combat this, preconcentration schemes combined with evanescent field spectroscopy can be used to enrich the analyte in the probed volume and thereby increasing sensitivity. Metal-organic frameworks (MOFs) are versatile materials with defined porosity and tuneable chemistry, which make them ideal for selective adsorption of target molecules. In this study, an NH2-MIL-88B(Fe) (= Fe3O(NH2-BDC)3) MOF-based enrichment layer was prepared on a diamond attenuated total reflection (ATR) crystal for in situ Fourier transform infrared (FTIR) spectroscopic measurements of ortho-phosphates in water. A workflow for single-use enrichment layers was established, and an automated flow system was used to apply aqueous phosphate solutions. Using internal referencing and compensating for variation in MOF film depositions, phosphate analysis reproducibility increased from 74% to 94%. The Langmuir adsorption model was used to derive a limit of detection (LOD) of 0.18 mg L-1 phosphorus in water. Overall, this work demonstrates the effectiveness of NH2-MIL-88B(Fe) MOFs as enrichment layers for aqueous phosphate sensing. Our results provide a promising avenue for the development of sensitive and selective sensors for environmental and biomedical applications.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Characterization of CBD oils, seized on the Belgian market, using infrared spectroscopy: Matrix identification and CBD determination, a proof of concept.

The availability of cannabidiol (CBD) oil products has increased in recent years. No analytical controls are mandatory for these products leading to uncertainties about composition and quality. In this paper, a methodology was developed to identify the oil matrix and to estimate the CBD content in such samples, using mid-infrared and near-infrared spectroscopy. Different oils were selected based on the information labeled on products and were bought in food stores in order to create a sample set with a variety of matrices. These oils were spiked with CBD to obtain samples with CBD levels from 0% to 20%. The first part of the study was focused on the qualitative analysis of the oil matrix. A classification model, based on Soft Independent Modeling of Class Analogy, was build using the spiked oils to distinguish between the different oil matrices. For both spectroscopic techniques, the sensitivity, the specificity, the accuracy and the precision were equal to 100%. These models were applied to determine the oil matrix of seized samples. The second part of the study was focused on the quantitative estimation of CBD. After determination of CBD in seized samples using gas chromatography-tandem mass spectrometry, partial least square regression (PLS-R) models were built, one for each matrix in the sample set. Both techniques were able to classify unknown oily samples according to their matrix, and although only few samples were available to evaluate the PLS-R models, the approach clearly showed promising results for the estimation of the CBD content in oil samples.