Carbonyl stretching band in amides as Lorentz oscillator. Insights into anharmonicity and local environment in the liquid phase from NIR and MIR spectra of N-methylformamide and di-N,N-methylformamide.

We investigated the anharmonicity and intermolecular interactions of N-methylformamide (NMF) and di-N,N-methylformamide (DMF) in the neat liquid phase with particular interest in the amide bands. The vibrational spectra, complex refractive index, and complex electric permittivity were determined in in the mid- (MIR) and near-infrared (NIR) regions (11,500-560 cm-1; 870-17857 nm). Dispersion analysis was based on the Classical Damped Harmonic Oscillator (CDHO) and simultaneous modelling of the real and imaginary components of the spectra. This data delivered insights into the vibrational energy dissipation and self-association in liquid amides. Identification of the MIR and NIR bands was based on anharmonic GVPT2//B3LYP/6-311++G(d,p) calculations. DMF and NMF follow distinct self-association, evidenced in the MIR fingerprint by the two components of the νCO, the analog of the Amide I band. These conclusions are supported by the structural information derived from the NIR spectra. Furthermore, the contribution of overtones and combination bands in the MIR spectra of amides was examined. The conclusions on molecular interactions and structural dynamics of NMF and DMF contribute to a deeper understanding of the effects of changes in the local environment of the amide group.

Rapid on-site analysis of soil microplastics using miniaturized NIR spectrometers: Key aspect of instrumental variation.

Contamination by microplastics, a global environmental concern, demands effective monitoring. While current methods focus on characterizing the smallest particles, their low throughput hinders practical assessment. Miniaturized near-infrared (NIR) spectroscopy offers high-throughput capabilities and rapid on-site analysis, potentially filling this gap. However, diverse sensor characteristics result in significant differences among handheld NIR spectrometers. This study characterizes the analytical performance of these instruments for identifying soil microplastics, comparing miniaturized devices MicroNIR 1700ES, NeoSpectra Scanner, microPHAZIR, nanoFTIR-NIR, NIR-S-G1, and SCiO sensor against a reference benchtop instrument, the NIRFlex N-500. Detection of common polymers, ABS, EVAC, HDPE, LDPE, PA6, PMMA, POM, PET, PS, PTFE, and SBR, at low concentrations (0.75 % w/w) was possible without sample preparation. Sensor selection proved crucial; FT instruments N-500 and NeoSpectra Scanner provided the most accurate analysis, while other handheld instruments faced various challenges. Covariance analysis, Principal Component Analysis (PCA), and mid-level data fusion revealed that miniaturized NIR spectrometers can successfully screen microplastics on-site. However, the ability of each sensor to discriminate certain groups of polymers strongly depends on its spectral characteristics. This study demonstrates the importance of sensor selection in the development of portable NIR spectroscopy for environmental monitoring of microplastics.

Phytochemical profiling of oak bark extract: A combined approach using near-infrared spectroscopy and liquid chromatography-mass spectrometry.

The aim of the presented study was to qualitatively and quantitatively determine the chemical composition of oak bark extracts in order to gain insights into the effectiveness as alternative medication for various diseases. The primary emphasis was on developing a near-infrared spectroscopy (NIRS) method for precise quantification of two key polyphenolic compounds, specifically gallic acid and catechin, in form of a fast and non-destructive quality control. A comprehensive dataset consisting of 48 samples from various production batches was analyzed throughout this research. Qualitative analysis was conducted using High Performance Liquid Chromatography coupled with a mass detector (LC-MS) to separate and identify individual components of the oak bark extract. Individual components were identified, confirmed and quantified using existing literature combined with appropriate standard references. Whereas the predominant nature of identified substances was of polyphenolic nature. Furthermore, a semi-quantitative assessment was additionally performed for eight identified constituents to identify their chemical stability or possible occurring transformations during storage, utilizing quantification via internal standard met in order to identify fluctuations and chemical variability within oakbark, five key components were precisely quantified using LC-MS and corresponding standard substances. For this purpose, HPLC measurements coupled to an Ultraviolet/Visible (UV/Vis) detector were utilized as reference method. NIRS measurements were performed on a FT-NIR benchtop device in transmission mode. Partial least squares regression (PLSR) was then applied for model building, after identifying the optimal spectral pretreatment. Model evaluation was performed using leave-one-out-cross validation followed by evaluation of an independent test set. The model proved promising results for the quantification of gallic acid on the benchtop device with a standard error of cross validation (SECV) of 13.41 mg/L and a standard error of prediction (SEP) of 19.33 mg/L, while the absolute concentrations of the different batches analyzed ranged from 126.49 to 332.54 mg/L. For the quantification of catechin the SECV was reported at 23.61 mg/L, the SEP at 32.35 mg/L with sample concentrations falling between 13.50 and 383.72 mg/L. In this study, we introduce various analytical methodologies for both qualitative and quantitative assessment of a complex phytochemical sample, specifically oak bark extract, aimed at identifying and confirming the presence of active compounds within the extract.

Exploring near-infrared spectroscopy and hyperspectral imaging as novel characterization methods for anaerobic gut fungi.

Neocallimastigomycota are a phylum of anaerobic gut fungi (AGF) that inhabit the gastrointestinal tract of herbivores and play a pivotal role in plant matter degradation. Their identification and characterization with marker gene regions has long been hampered due to the high inter- and intraspecies length variability in the commonly used fungal marker gene region internal transcribed spacer (ITS). While recent research has improved methodology (i.e. switch to LSU D2 as marker region), molecular methods will always introduce bias through nucleic acid extraction or PCR amplification. Here, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are introduced as two nucleic acid sequence-independent tools for the characterization and identification of AGF strains. We present a proof-of-concept for both, achieving an independent prediction accuracy of above 95% for models based on discriminant analysis trained with samples of three different genera. We further demonstrated the robustness of the NIRS model by testing it on cultures of different growth times. Overall, NIRS provides a simple, reliable, and nondestructive approach for AGF classification, independent of molecular approaches. The HSI method provides further advantages by requiring less biomass and adding spatial information, a valuable feature if this method is extended to mixed cultures or environmental samples in the future.

Soft confinement of water in aliphatic alcohols: MIR/NIR spectroscopic and DFT studies.

Here, we present the first examination of the state of water under a soft confinement in eight aliphatic alcohols including cyclopentanol, 1-pentanol, 1-hexanol, 1-heptanol, 1-octanol, 1-decanol, 2-octanol and 3-octanol. Due to relatively large size of the aliphatic part, water has limited solubility in all studied alcohols. Water content in saturated solutions was determined by Karl Fischer titration and correlated with the spectroscopic data. This way, we determined the molar absorptivity of the ν2+ν3 combination mode. The effect of addition of water and temperature variation was monitored by ATR-IR and NIR spectroscopy. Analysis of the experimental results was guided by DFT calculations, which provided the structures, harmonic MIR spectra and binding energies of selected alcohol-water complexes. Our studies demonstrated that the state of water in alcohols is related to its solubility, which depends on structure of solvent molecules. The solubility of water in 1-alcohols decreases on increasing of the chain length, but for long chain alcohols this effect is less evident. More apparent solubility reduction appears in going from the primary to secondary alcohols. The effective shielding of the OH group in the linear alcohols is achieved when on both sides of the OH group are ethyl or longer substituents, while the shielding by methyl groups is less efficient. Water is much better soluble in the cyclic alcohols as compared with the linear ones due to better accessibility of the OH group. The soft confinement of water in aliphatic alcohols allows for flexible structural arrangements and interactions. Even at low water content, we did not observe free molecules of water. At these conditions, the molecules of water are singly or doubly bonded to the OH groups from the alcohol. Increasing solubility of water reduces the number of the free OH groups and leads to formation of water clusters. Obtained results allow concluding that in alcohols with sizable aliphatic part the molecules of water are confined in the vicinity of the OH groups.

Species-Specific quantification of bioactive boswellic acids in Boswellia resin using NIR spectroscopy, HPLC and Multivariate data analysis.

The bioactive compounds Acetyl-11-keto-ß-boswellic acid (AKBA) and 11-keto-ß-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R2val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques.

Unveiling the synergy of NIRS and enrichment technologies: A comprehensive review of in-sorbent-based detection and quantification strategies.

This comprehensive review paper aims to captivate the applicability of in-sorbent detection, where near-infrared spectroscopy (NIRS) converges with enrichment technologies. For this purpose, we collected and summarized information regarding the combination of several sophisticated analytical enrichment techniques with NIRS to further explore and develop this synergistic approach. Peer-reviewed publications, matching the criteria of in situ NIR measurements prior analyte elution, have been collected, investigated, and concluded within this review. Investigations according to used materials, commercial or self-made, composition, organic or inorganic and applied analytical methodologies have been carried out. Applications extending over a multitude of chemical fields, from environmental to medicinal applications. As this review concludes, the combination of these techniques further expands the applicability of NIRS and moreover tries to solve the long-standing issue of the comparably low sensitivity regarding this vibrational technique.

Performance of benchtop and portable spectroscopy equipment for discriminating Iberian ham according to breed.

Iberian ham is a highly appreciated product and according to Spanish legislation different labels identify different products depending on the genetic purity. Consequently, "100% Iberian" ham from purebred Iberian animals is more expensive than "Iberian" ham from Iberian x Duroc crosses. The hypothesis of this study was that to avoid labelling fraud it is possible to distinguish the breed (Iberian or Iberian x Duroc) of acorn-fed pigs of Iberian ham without any prior preparation of the sample by using spectroscopy that is a rapid and reliable technology. Moreover, portable devices which can be used in situ could provide similar results to those of benchtop equipment. Therefore, the spectra of the 60 samples (24 samples of 100% Iberian ham and 36 samples of Iberian x Duroc crossbreed ham) were recorded only for the fat, only for the muscle, or for the whole slice with two benchtop near-infrared (NIR) spectrometers (Büchi NIRFlex N-500 and Foss NIRSystem 5000) and five portable spectrometers including four portable NIR devices (VIAVI MicroNIR 1700 ES, TellSpec Enterprise Sensor, Thermo Fischer Scientific microPHAZIR, and Consumer Physics SCiO Sensor), and one RAMAN device (BRAVO handheld). The results showed that, in general, the whole slice recording produced the best results for classification purposes. The SCiO device showed the highest percentages of correctly classified samples (97% in calibration and 92% in validation) followed by TellSpec (100% and 81%). The SCiO sensor also showed the highest percentages of success when the analyses were performed only on lean meat (97% in calibration and 83% in validation) followed by microPHAZIR (84% and 81%), while in the case of the fat tissue. Raman technology showed the best discrimination capacity (96% and 78%) followed by microPHAZIR (89% and 81%). Therefore, spectroscopy has proved to be a suitable technology for discriminating ham samples according to breed purity; portable devices have been shown to give even better results than benchtop spectrometers.

Prediction of Biliary Complications After Human Liver Transplantation Using Hyperspectral Imaging and Convolutional Neural Networks: A Proof-of-concept Study.

BACKGROUND: Biliary complications (BCs) negatively impact the outcome after liver transplantation. We herein tested whether hyperspectral imaging (HSI) generated data from bile ducts (BD) on reperfusion and machine learning techniques for data readout may serve as a novel approach for predicting BC. METHODS: Tissue-specific data from 136 HSI liver images were integrated into a convolutional neural network (CNN). Fourteen patients undergoing liver transplantation after normothermic machine preservation served as a validation cohort. Assessment of oxygen saturation, organ hemoglobin, and tissue water levels through HSI was performed after completing the biliary anastomosis. Resected BD segments were analyzed by immunohistochemistry and real-time confocal microscopy. RESULTS: Immunohistochemistry and real-time confocal microscopy revealed mild (grade I: 1%-40%) BD damage in 8 patients and moderate (grade II: 40%-80%) injury in 1 patient. Donor and recipient data alone had no predictive capacity toward BC. Deep learning-based analysis of HSI data resulted in >90% accuracy of automated detection of BD. The CNN-based analysis yielded a correct classification in 72% and 69% for BC/no BC. The combination of HSI with donor and recipient factors showed 94% accuracy in predicting BC. CONCLUSIONS: Deep learning-based modeling using CNN of HSI-based tissue property data represents a noninvasive technique for predicting postoperative BC.

State of water in various environments: Aliphatic ketones. MIR/NIR spectroscopic, dielectric and theoretical studies.

This work provides new insight into the state of water in a series of aliphatic ketones. For our studies, we selected nine aliphatic ketones of different size and structure to examine the effect of various structural motifs on behavior of water in the mixtures. Our results reveal that conformational flexibility of aliphatic chains in the linear ketones allows for effective shielding of the carbonyl group, and this flexibility is the main reason for poor solubility of water. Hence, in the linear ketones molecules of water are involved mostly in ketone-water interactions, while the water-water interactions are rare. Higher solubility of water in the cyclic ketones allows for creation of clusters of water, where the molecules are in water-like environment. The temperature rise in wet cyclic ketones increases population of ketone-water interactions at the expense of the water-water ones, while in the linear ketones and 2,6-dimethylcyclohexanone at an elevated temperature there is an increase in the population of singly bonded water at the expense of the doubly bonded one. DFT calculations reveal that the substitution of cyclohexanone by a single methyl group does not affect the strength of the ketone-water interactions, while it has a significant impact on the solubility of water in the ketone. The most important conclusion from this study is that the accessibility of the carbonyl group is the most important factor determining the intermolecular interactions and solubility of water in aliphatic ketones.

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review.

Estimating postmortem intervals (PMI) is crucial in forensic investigations, providing insights into criminal cases and determining the time of death. PMI estimation relies on expert experience and a combination of thanatological data and environmental factors but is prone to errors. The lack of reliable methods for assessing PMI in bones and soft tissues necessitates a better understanding of bone decomposition. Several research groups have shown promise in PMI estimation in skeletal remains but lack valid data for forensic cases. Current methods are costly, time-consuming, and unreliable for PMIs over 5 years. Raman spectroscopy (RS) can potentially estimate PMI by studying chemical modifications in bones and teeth correlated with burial time. This review summarizes RS applications, highlighting its potential as an innovative, nondestructive, and fast technique for PMI estimation in forensic medicine.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review.

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Ambient mass spectrometry and near-infrared spectroscopy - a direct comparison of methods for the quantification of sucralose in e-liquids.

E-liquids have become increasingly popular in society in recent years. A wide variety of flavors and nicotine strengths make it possible for every user to get a product according to their wishes. Many of these e-liquids are marketed with countless different flavors, which are often characterized by a strong and sweet smell. Sweeteners, such as sucralose, are therefore commonly added as sugar substitutes. However, recent studies have shown the potential formation of highly toxic chlorinated compounds. This can be explained by the high temperatures (above 120 °C) within the heating coils and the used basic composition of these liquids. Nevertheless, the legal situation is composed of proposals without clear restrictions, only recommendations for tobacco products. For this reason, a high level of interest lies within the establishment of fast, reliable and cost-effective methods for the detection of sucralose in e-liquids. In this study, a number of 100 commercially available e-liquids was screened for sucralose in order to identify the suitability of ambient mass spectrometry and near-infrared spectroscopy for this application. A highly sensitive high-performance liquid chromatography coupled to a tandem mass spectrometer method was used as reference method. Furthermore, the advantages and limitations of the two mentioned methods are highlighted in order to provide a reliable quantification of sucralose. The results clearly revile the necessity for product quality due to the absence of declaration on many of the used products. Further on, it could be shown, that both methods are suitable for the quantification of sucralose in e-liquids, with beneficial economic and ecological aspects, over classical analytical tools including high-performance liquid chromatography. Clear correlations between the reference and novel developed methods are displayed. In summary, these methods enable an important contribution to ensure consumer protection and elimination of confuse package labelling.

Investigation of Water Interaction with Polymer Matrices by Near-Infrared (NIR) Spectroscopy.

The interaction of water with polymers is an intensively studied topic. Vibrational spectroscopy techniques, mid-infrared (MIR) and Raman, were often used to investigate the properties of water-polymer systems. On the other hand, relatively little attention has been given to the potential of using near-infrared (NIR) spectroscopy (12,500-4000 cm-1; 800-2500 nm) for exploring this problem. NIR spectroscopy delivers exclusive opportunities for the investigation of molecular structure and interactions. This technique derives information from overtones and combination bands, which provide unique insights into molecular interactions. It is also very well suited for the investigation of aqueous systems, as both the bands of water and the polymer can be reliably acquired in a range of concentrations in a more straightforward manner than it is possible with MIR spectroscopy. In this study, we applied NIR spectroscopy to investigate interactions of water with polymers of varying hydrophobicity: polytetrafluoroethylene (PTFE), polypropylene (PP), polystyrene (PS), polyvinylchloride (PVC), polyoxymethylene (POM), polyamide 6 (PA), lignin (Lig), chitin (Chi) and cellulose (Cell). Polymer-water mixtures in the concentration range of water between 1-10%(w/w) were investigated. Spectra analysis and interpretation were performed with the use of difference spectroscopy, Principal Component Analysis (PCA), Median Linkage Clustering (MLC), Partial Least Squares Regression (PLSR), Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) and Two-Dimensional Correlation Spectroscopy (2D-COS). Additionally, from the obtained data, aquagrams were constructed and interpreted with aid of the conclusions drawn from the conventional approaches. We deepened insights into the problem of water bands obscuring compound-specific signals in the NIR spectrum, which is often a limiting factor in analytical applications. The study unveiled clearly visible trends in NIR spectra associated with the chemical nature of the polymer and its increasing hydrophilicity. We demonstrated that changes in the NIR spectrum of water are manifested even in the case of interaction with highly hydrophobic polymers (e.g., PTFE). Furthermore, the unveiled spectral patterns of water in the presence of different polymers were found to be dissimilar between the two major water bands in NIR spectrum (νs + νas and νas + δ).

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference.

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives.

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Portable vs. Benchtop NIR-Sensor Technology for Classification and Quality Evaluation of Black Truffle.

Truffles represent the best known and most expensive edible mushroom. Known as Ascomycetes, they belong to the genus Tuber and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of up to 3000-5000 euros per kilogram (Tuber magnatum PICO). Amongst black truffles, the species Tuber melanosporum VITTAD. is highly regarded for its organoleptic properties. Nonetheless, numerous different sorts of black truffle are offered at lower prices, including Tuber aestivum VITTAD., Tuber indicum and Tuber uncinatum, which represent the most frequently consumed types. Because truffles do not differ visually for inexperienced consumers, food fraud is likely to occur. In particular, for the highly prized Tuber melanosporum, which morphologically forms very similar fruiting bodies to those of Tuber indicum, there is a risk of fraud via imported truffles from Asia. In this study, 126 truffle samples belonging to the four mentioned species were investigated by four different NIR instruments, including three miniaturized devices-the Tellspec Enterprise Sensor, the VIAVI solutions MicroNIR 1700 and the Consumer Physics SCiO-working on different technical principles. Three different types of measurement techniques were applied for all instruments (outer shell, rotational device and fruiting body) in order to identify the best results for classification and quality assurance in a non-destructive manner. Results provided differentiation with an accuracy up to 100% for the expensive Tuber melanosporum from Tuber indicum. Classification between Tuber melanosporum, Tuber indicum, Tuber aestivum and Tuber uncinatum could also be achieved with success of 100%. In addition, quality monitoring including discrimination between fresh and frozen/thawed, and prediction of the approximate date of harvesting, was performed. Furthermore, feasibility studies according to the geographical origin of the truffle were attempted. The presented work compares the performance for prediction and quality monitoring of portable vs. benchtop NIR devices and applied measurement techniques in order to be able to present a suitable, accurate, fast, non-destructive and reliable method for consumers.

Rapid discrimination of Curcuma longa and Curcuma xanthorrhiza using Direct Analysis in Real Time Mass Spectrometry and Near Infrared Spectroscopy.

This study describes a newly developed method for the fast and straightforward differentiation of two turmeric species using Direct Analysis in Real Time mass spectrometry and miniaturized Near Infrared spectroscopy. Multivariate analyses (PCA and LDA) were performed on the mass spectrometric data, thus creating a powerful model for the discrimination of Curcumalonga and Curcumaxanthorrhiza. Cross-validation of the model revealed correctness-scores of 100% with 20-fold as well as leave-one-out validation techniques. To further estimate the models prediction power, seven retail samples of turmeric powder were analyzed and assorted to a species. Looking for a fast, non-invasive, cost-efficient and laboratory independent method, miniaturized NIR spectrometers offer an alternative for quality control of turmeric species. However, different technologies implemented to compensate for their small size, lead to different applicability of these spectrometers. Therefore, we investigated the three handheld spectrometers microPHAZIR, MicroNIR 2200 and MicroNIR 1700ES for their application in spice analysis in hyphenation to PCA, LDA and ANN methods used for the discriminant analysis. While microPHAZIR proved to be the most valuable device for differentiating C.longa and C.xanthorrhiza, MicroNIR 1700ES offered the worst results. These findings are interpreted on the basis of a quantum chemical simulation of the NIR spectrum of curcumin as the representative constituent. It was found that the information accessible to MicroNIR 1700ES that is relevant to the analyzed constituents is located in the spectral region prone to interferences with the matrix, likely limiting the performance of this spectrometer in this analytical scenario.

A unique approach for in-situ monitoring of the THCA decarboxylation reaction in solid state.

The decarboxylation of Δ9-tetrahydrocannabinolic acid (THCA) plays pivotal role in the potency of medical cannabis and its extracts. Our present work aims to draw attention to mid-infrared (MIR) spectroscopy to in-situ monitor and decipher the THCA decarboxylation reaction in the solid state. The initial TG/DTG curves of THCA, for a first time, outlined the solid-solid decarboxylation dynamics, defined the endpoint of the process and the temperature of the maximal conversion rate, which aided in the design of the further IR experiment. Temperature controlled IR spectroscopy experiments were performed on both THCA standard and cannabis flower by providing detailed band assignment and conducting spectra-structure correlations, based on the concept of functional groups vibrations. Moreover, a multivariate statistical analysis was employed to address the spectral regions of utmost importance for the THCA â†’ THC interconversion process. The principal component analysis model was reduced to two PCs, where PC1 explained 94.76% and 98.21% of the total spectral variations in the THCA standard and in the plant sample, respectively. The PC1 plot score of the THCA standard, as a function of the temperature, neatly complemented to the TG/DTG curves and enabled determination of rate constants for the decarboxylation reaction undertaken on several selected temperatures. The predictive capability of MIR was further demonstrated with PLS (R2X = 0.99, R2Y = 0.994 and Q2 = 0.992) using thermally treated flower samples that covered broad range of THCA/THC content. Consequently, a progress in elucidation of kinetic models of THCA decarboxylation in terms of fitting the experimental data for both, solid state standard substance and a plant flower, was achieved. The results open the horizon to promote an appropriate process analytical technology (PAT) in the outgrowing medical cannabis industry.