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The contributions of the first overtone and binary combination bands into the NIR spectral envelope of thymol in
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Near-infrared spectroscopy (NIRS) is an effective analytical/quality control tool in various appliances, i.e. in phytopharmaceutical industry. While multivariate analysis gives NIRS the desired level of analytical performance it lacks in providing deeper physical insights. Quantum mechanical (QM) simulation of NIR spectra is becoming feasible for c...
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... 9 It can also aid in digestion by relaxing smooth muscles, reduces menstrual cramps, and alleviates respiratory difficulties. 10,11 flame ionization detector (GC-FID). 15,16 GC-MS has proven to be a valuable tool in the quantification of the constituents of essential oils, but this method has a limitation in that it is unable to distinguish between closely related compounds or isomers unless an internal standard is used. ...
Objective: The conventional method for the characterization of volatile compounds has been mostly limited to the use of gas chromatography. This work offers an alternative method to address the limitations that might be encountered with the use of the gas chromatography method. Furthermore, this work addresses the scarcity of the analysis of these materials in the far infrared/terahertz region as previous studies have been focused mainly on the mid-infrared region. Methods: The far infrared/terahertz spectra of carvacrol and thymol were analyzed using the Fourier transform infrared (FTIR) and gas chromatography-mass spectroscopy (GC-MS). Density function theory (DFT) calculations were employed for the interpretation of the FTIR experimental spectra. Results: Thymol and carvacrol spectra were characterized by distinct absorption bands in the 200-600 cm⁻¹ (6-18 THz). There were appearances of bands at similar energies for these isomers. These bands appeared at 320.7 cm⁻¹ (9.6 THz), 439.2 cm⁻¹ (13.2 THz), 525.2 cm⁻¹ (15.8 THz), and 586.8 cm⁻¹ (17.6 THz) for thymol and at 313.6 cm⁻¹ (9.4 THz), 419.8 cm⁻¹ (12.6 THz), 521.2 cm⁻¹ (15.7 THz), and 566.9 cm⁻¹ (17.0 THz) for carvacrol. Despite these similarities, there were also significant differences in the spectra of these materials. The theoretical peak frequencies were in agreement with the experimental absorption band frequencies. Two seemingly common absorption bands were observed in the spectra of thymol and carvacrol. However, peak assignments revealed that these bands have different vibrational modes. The GC-MS results show that the retention time of thymol and carvacrol are very close at 18.5 and 18.8 respectively. Conclusion: This work underscores the ability of far infrared/terahertz waves in identifying materials with similar structural features. Furthermore, it supplements the limited studies of these isomers in the far infrared/terahertz region. The GC-MS results emphasize the need for a complementary method that can improve the characterization of these materials.
... thymol analysis in Thymi herba. (127) The analysis of dairy products has recently become particularly popular with the use of miniaturized NIR spectrometers, where it is worth noting that the research effort is channeled directly toward the development of improved analytical methods for milk analysis. (39)(40)(41)(42)(43)(44)(45)(46)(47) Among those, noteworthy here are studies that developed ef cient methods for distinguishing between organic and conventional milk, control of authenticity and detection of milk falsi cation, or the distinction between plain and lactose-free milk directly in the eld. ...
Near-infrared (NIR) spectroscopy is widely used in
qualitative and quantitative analysis in various fields
of applications. Compared to conventional methods of
analytical chemistry, NIR spectroscopy offers many practical
advantages in terms of speed, efficiency, minimal
(or no) requirements for sample preparation, and the
applicability to many types of samples. In the past decade,
the technology of portable NIR spectrometers has rapidly
advanced, which enabled new applications of this technique
in science and industry where the capacity to
perform spectral analysis directly on site is the key advantage.
Miniaturization introduced NIR spectroscopy to
practical use in analytical scenarios that were unattainable
for standard laboratory equipment. The advantages of
miniaturized NIR spectroscopy are particularly evident in
several areas where fast and nondestructive on-site analysis
is essential – e.g. natural products and resources, and
agri-food items in particular. The sample types commonly
focused in these applications are characterized by chemical
complexity and diversity depending on the geographic
origin, conditions of cultivation and/or storage, or harvest
time. Miniaturization necessitated different engineering
solutions that introduced wide diversity in the elements
used for construction of portable spectrometers. Consequently,
the applicability of these instruments and their
performance in a given application attract keen attention.
Intensive studies are devoted to method development and
evaluation of miniaturized NIR sensors in a variety of
analytical problems. This article discusses the essential
topics related to fundamentals and applications of miniaturized
NIR spectrometers, with an emphasis on practical
applications in food and agriculture sectors. However, an
overview of the design principles of portable NIR instruments
and their relationships with analytical figures of
merit are provided as well in an attempt to draw a comprehensive
picture of the state-of-the-art and future potential
of this increasingly influential analytical technique.
... This method involves the disciplines of mathematics and statistics, which are mixed into an algorithm to find the relationship. The most popular algorithms for processing near-infrared spectral data are PCR and PLSR [11]- [13]. Both algorithms are reported to have excellent performance in building spectral data calibration models if there is a linear response between the predictor and the parameter constituents of the sample. ...
The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.
... This method involves the disciplines of mathematics and statistics, which are mixed into an algorithm to find the relationship. The most popular algorithms for processing near-infrared spectral data are PCR and PLSR [11]- [13]. Both algorithms are reported to have excellent performance in building spectral data calibration models if there is a linear response between the predictor and the parameter constituents of the sample. ...
The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.
... Consequently, this difference between the capabilities to capture chemical information from a sample is likely one of the reasons for the observed disparity in the analytical performance when analyzing target ingredients in plant material [85]. The interpretation of the characteristic NIR bands relevant to analysis of active ingredients in plant extract was also focused in other studies, e.g., for thymol in Thymi herba [86]. ...
... Some infrared (IR) spectroscopic studies have been performed for thymol in the solid state [12,13] and in solution [14]. However, as far as we are aware, IR spectra of the monomeric compound have never been reported, nor has its light-induced transformations. ...
... Before performing the irradiations, a near-IR spectrum was recorded to identify the position of the 2νOH band. This band appears as a doublet with a main component centered at 7061 cm −1 , and a shoulder at 7047 cm −1 (see Figure S3), which is very close to its position observed in the near-IR spectrum of thymol dissolved in CCl 4 solution (7056 cm −1 ) [14]. The shape of the 2νOH band is identical to that of the fundamental νOH feature observed in the mid-IR spectrum, meaning that the higher and lower frequency components should be ascribed, respectively, to the 2νOH absorptions of the gt and tt conformers. ...
The conformational space of the natural product thymol (2-isopropyl-5-methylphenol) was investigated using quantum chemical calculations at the B3LYP and MP2 levels, which revealed the existence of four types of conformers differing in the orientation of the isopropyl and hydroxyl groups. Thymol monomers were isolated in noble gas (Ar and Xe) matrices (at 15 K) and characterized by IR spectroscopy. With the support of B3LYP harmonic vibrational calculations, the two most stable trans-OH-conformers, differing in the isopropyl orientation, were identified in the cryomatrices. The two less stable cis-OH conformers were not detected as they shall undergo fast tunneling to the most stable ones. Annealing experiments in a Xe matrix up to 75 K did not lead to any conversion between the two isolated conformers, which is in accordance with the significative energy barrier computed for rotamerization of the bulky isopropyl group (~24 kJ mol−1). Vibrational excitation promoted by broadband or by narrowband irradiation, at the 2ν(OH) frequencies of the isolated conformers, did not lead to any conversion either, which was interpreted in terms of a more efficient energy transfer to the hydroxyl rotamerization (associated with a lower energy barrier and a light H-atom) than to the isopropyl rotamerization coordinate. Broadband UV irradiation experiments (λ > 200 nm) led to a prompt transformation of matrix isolated thymol, with spectroscopic evidence suggesting the formation of isomeric alkyl-substituted cyclohexadienones, Dewar isomers and open-chain conjugated ketenes. The photochemical mechanism interpretation concords with that reported for analogous phenol derivatives.
... Quantum-mechanical simulations of NIR spectra of a variety of compounds are significant from the point of view of physiochemical and analytical spectroscopy. The examples range from basic molecules (alcohols, nitriles, carboxylic acids) [84][85][86][87] to complex molecules with importance in biophysical science (fatty acids, nucleobases) [88,89], materials science and industry [90], and analytical chemistry (vitamins, natural drugs, polyphenols, alkaloids, food adulterants) [91][92][93][94][95]. The simulated NIR spectra largely increase the level of detail in the band assignments compared to the one available in conventional methods of spectral analysis (Table 2). ...
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.
... alcohols [47,48], phenol [49] or nitriles [50] and much improve one's understanding of the fine lineshape features appearing in NIR spectra. However, particular attention should be given to simulation of NIR spectra of practically meaningful molecules [38] ranging from vitamins [51], terpenes [52], alkaloids [53], nucleobases [54] through simple carboxylic acids [55,56] to long-chain fatty acids [57] and polymers [58]. ...
... Simulated NIR spectra open the possibility to interpret how the chemical information is expressed indirectly in the calibration models. Noteworthy here is the study focused on thymol, a prototypic polyphenol and monoterpene with prime importance for therapeutic properties of numerous medicinal plants [52]. As known, vibrations of OH group give rise to very intense and outstanding NIR bands. ...
... Highlighted are the wavenumber regions qualitatively independent of sample phase and concentration; A: 6000-5600 cm − 1 ; B: 4490-4000 cm − 1 . Reproduced (CC-BY 4.0 license) from Ref.[52]. ...
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, provide 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 very 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.
... p0715 Significant progress has been made in recent years in quantum chemical calculations of NIR spectra [84,85]. An example of how this new tool can be used favorably for the analysis of medicinal plant material has been demonstrated lately [86]. The theoretical simulation of the NIR spectrum of the active ingredient can significantly improve the understanding of the behavior of that substance in the sample. ...
The properties of herbal medicines are related to certain classes of compounds such as alkaloids, flavonoids, essential oils, and others. During the last four decades, mid-infrared (MIR; i.e., infrared, IR; 2500–25,000 nm; 4000–400 cm− 1) and near-infrared (NIR) spectroscopy (800–2500 nm; 12,500–4000 cm− 1) has become one of the most attractive and widely used methods for analysis rather than traditionally applied separation methods (including liquid chromatography and electrophoresis) for the following reasons: NIR spectroscopy is a noninvasive analytical tool allowing a fast and simultaneous qualitative and quantitative characterization of herbal medicines and their constituents. Infrared imaging techniques are becoming more and more attractive to understand the function and the biochemical composition of plant tissue. In this contribution, the principle, technique, and methodology are described, followed by a discussion of quantitative and qualitative application possibilities. Finally, some hints to helpful regulatory issues are summarized.
... Thymol is an interesting molecule, a simple terpene compound that is very popular in medicinal plants and it is responsible for the antioxidant potential of the plant and the medicinal products derived from it. 7 It is, therefore, a molecule of a quite simple structure but still very relevant to one of the typical applications of NIR spectroscopy -phytopharmaceutical analysis. 8 Despite the structural simplicity, the NIR spectrum of thymol isolated in a fairly inert solvent (carbon tetrachloride), which largely eliminates any interfering matrix effects, still presents a considerable complexity with multiple overlapping peaks. ...
Between 18th to 21st October 2021, the 20th International Conference on NIR spectroscopy in Beijing took place. Despite this time being held as a virtual event, it was a highly successful symposium met with high interest from the wide audience - as evidenced by many excellent presentations, around which numerous vivid discussions developed. During the conference, four workshops were offered, focused at discussing few areas essential for NIR spectroscopy and its applications. Excellent workshops were provided by Professors Heinz Siesler, Hui Yan, Dolores Pérez-Marín and Tom Fearn, in which invaluable knowledge was shared with the participants of the conference. Among these renowned experts, I had the honour to offer my contribution with the workshop aimed at the physicochemical foundations of NIR spectroscopy, an area that seldom is exhaustively presented in the textbooks. The workshop aimed at shedding light on the complex world of overtone and combination bands, and was met with a considerable interest from the participants. As many questions have been asked both during the dedicated Q&A session, as well as through other channels and private correspondence, I would like to provide a short recapitulation of the workshop in the form of this brief article. Some of the most essential ‘take home messages’, such as the origin of the intensity variation of the overtone bands and the famous ‘selection rule’ of the harmonic oscillator, among others will be briefly outlined here.
