Molecular structure and properties of 1-(2-hydroxy-4,5-dimethylphenyl)ethanone were experimentally investigated by X-ray diffraction technique and vibrational spectroscopy. Experimental results on the molecular structure of the reported compound were supported with computational studies using the density functional theory (DFT), with the Becke-3-Lee-Yang-Parr (B3LYP) functional and the 6-311+G(3df,p) basis set. Potential energy distribution (PED) and potential energy surface (PES) analyses were performed to identify characteristic frequencies and reliable conformational analysis correspondingly. The compound crystallizes in monoclinic space group C2/c with the CO up-OH down conformation. There is a good agreement between the experimentally determined geometrical parameters and vibrational frequencies of the compound to those predicted theoretically.
Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.
Caries is one of the non-communicable diseases that has a high prevalence trend. The current methods used to detect caries require sophisticated laboratory equipment, professional inspection, and expensive equipment such as X-ray imaging device. A non-invasive and economical method is required to substitute the conventional methods for the detection of caries. UV absorption spectroscopy coupled with chemometrics analysis has emerged as a good potential candidate for such an application. Data preprocessing methods such as mean centre, autoscale and Savitzky-Golay smoothing were implemented to enhance the signal-to-noise ratio of spectra data. Various classification algorithms namely K-nearest neighbours (KNN), logistic regression (LR) and linear discriminant analysis (LDA) were implemented to classify the severity of dental caries into International Caries Detection and Assessment System (ICDAS) scores. The performance of the prediction model was measured and comparatively analysed based on the accuracy, precision, sensitivity, and specificity. The LDA algorithm combined with the Savitzky-Golay preprocessing method had shown the best result with respect to the validation data accuracy, precision, sensitivity and specificity, where each had values of 0.90, 1.00, 0.86 and 1.00 respectively. The area under the curve of the ROC plot computed for the LDA algorithm was 0.95, which indicated that the prediction algorithm was capable of differentiating normal and caries teeth excellently.
Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
Fatty hydroxamic acid (FHA) immobilized in polyvinyl chloride (PVC) has been studied as a sensor element of an optical fibre chemical sensor for V(V). By using this instrument, V(V) in solution has been determined in the log concentration range of 0-2.5 (i.e. 1.0-300 mg/L). The detection limit was 1.0 mg/L. The relative standard deviation (R.S.D.) of the method for the reproducibility study at V(V) concentration of 200 mg/L and 300 mg/L were calculated to be 2.9% and 2.0%, respectively. Interference from foreign ions was also studied at 1:1 mole ratio of V(V):foreign ions. It was found that, Fe(III) ion interfered most in the determination of vanadium(V). Excellent agreement with ICP-AES method was achieved when the proposed method was applied towards determination of V(V).
In this study, novel active nanolayers in combination with surface plasmon resonance (SPR) system for zinc ion (Zn(2+)) detection has been developed. The gold surface used for the SPR system was modified with the novel developed active nanolayers, i.e. chitosan and chitosan-tetrabutyl thiuram disulfide (chitosan-TBTDS). Both chitosan and chitosan-TBTDS active layers were fabricated on the gold surface by spin coating technique. The system was used to monitor SPR signal for Zn(2+) in aqueous media with and without sensitivity enhancement by TBTDS. For both active nanolayers, the shift of resonance angle is directly proportional to the concentration of Zn(2+) in aqueous media. The higher shift of resonance angle was obtained for chitosan-TBTDS active nanolayer due to a specific binding of TBTDS with Zn(2+). The chitosan-TBTDS active nanolayer enhanced the sensitivity of detection down to 0.1 mg/l and also induced a selective detection towards Zn(2+).
A metal-free mesoporous carbon nitride (MCN) was investigated for the first time as an adsorbent for N-nitrosopyrrolidine (NPYR), which is one of the nitrosamine pollutants. Under the same condition, the adsorption capability of the MCN was found to be higher than that of the MCM-41. Since the adsorption isotherm was consistent with Langmuir and Freundlich model equations, it was suggested that the adsorption of NPYR molecules on the MCN occurred in the form of mono-molecular layer on the heterogeneous surface sites. It was proposed that MCN with suitable adsorption sites was beneficial for the adsorption of NPYR. The evidence on the interaction between the NPYR molecules and the MCN was supported by fluorescence spectroscopy. Two excitation wavelengths owing to the terminal N-C and N=C groups were used to monitor the interactions between the emission sites of the MCN and the NPYR molecules. It was confirmed that the intensity of the emission sites was quenched almost linearly with the concentration of NPYR. This result obviously suggested that the MCN would be applicable as a fluorescence sensor for detection of the NPYR molecules. From the Stern-Volmer plot, the quenching rate constant of terminal N-C groups was determined to be ca. two times higher than that of the N=C groups on MCN, suggesting that the terminal N-C groups on MCN would be the favoured sites interacted with the NPYR. Since initial concentration can be easily recovered, the interactions of NPYR on MCN were weak and might only involve electrostatic interactions.
Many macrofungus sclerotia are well-known medicinal herbs, health food and nutritional supplements. However, the prevalent adulterant commercial products are major hindrances to their incorporation into mainstream medical use in many countries. The mushroom sclerotia of Lignosus rhinocerotis, Poria cocos, Polyporus umbellatus, Pleurotus tuber-regium and Omphalia lapidescens are commonly used in traditional Chinese medicine. In this study, IR macro-fingerprint method was used in the identification of these sclerotia. The results showed that the spectrum of L. rhinocerotis (LR) was comparable with P. cocos with 94.4% correlation, except that the peak at 1543cm(-1) of LR appeared in lower intensity. The spectrum of P. umbellatus and P. tuber-regium was also correlated (91.5%), as both spectra could be clearly discriminated in that P. umbellatus spectrum has small base peaks located at the range of 1680-1500cm(-1). O. lapidescens was not comparable with all the other sclerotia as its spectrum was totally different. Its base peak was broad and derivated equally along the range. The first IR has revealed the dissimilarity among five mushrooms sclerotia. The second derivative and 2DIR further enhanced the identification in detail.
A new isomers of thiourea derivatives, namely N-(4-chlorobutanoyl)-N'-(2-methylphenyl)-thiourea (1a), N-(4-chlorobutanoyl)-N'-(3-methylphenyl)thiourea (1b) and N-(4-chlorobutanoyl)-N'-(4-methylphenyl)thiourea (1c) have been synthesized by refluxing mixture of equimolar amounts of 4-chlorobutanoylisothiocyanate with 2, 3 or 4-toluidine, respectively. The three isomers were characterized by spectroscopic (UV/vis, FT-IR and NMR) and X-ray crystallography techniques. To investigate the isomerization effect on spectroscopic data, DFT and TD-DFT calculations have been carried out using five hybrid functionals (B3LYP, B3P86, CAM-B3LYP, M06-2X and PBE0) to predict UV/vis absorption bands (n→π∗ and π→π∗), (1)H and (13)C NMR chemical shifts, FT-IR vibration modes and X-ray parameters (bonds, bond angles and torsion angles) for 1a, 1b and 1c isomers. The results showed that the isomerization effect is significant on λ(MAX) absorption bands, while for IR and NMR the effect is negligible. In accordance with previous studies, B3LYP, B3P86 and PBE0 gave the most reliable to predict the excitation energies of thiourea derivatives.
Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is a precious traditional Chinese medicinal herb and has been perennially used to treat various illness. However, there were unethical sellers who adulterated wild A. roxburghii with tissue cultured and cultivated ones. Therefore, there is an urgent need for an effective authentication method to differentiate between these different types of A. roxburghii. In this research, the infrared spectroscopic tri-step identification approach including Fourier transform infrared spectroscopy (FT-IR), Second derivative infrared spectra (SD-IR) and two-dimensional correlation infrared spectra (2D-IR) was used to develop a simple and rapid method to discriminate between wild, cultivated and tissue cultivated A. roxburghii plant. Through this study, all three types of A. roxburghii plant were successfully identified and discriminated through the infrared spectroscopic tri-step identification method. Besides that, all the samples of wild, cultivated and tissue cultivated A. roxburghii plant were analysed with the Soft Independent Modelling of Class Analogy (SIMCA) pattern recognition technique to test and verify the experimental results. The results showed that the three types of A. roxburghii can be discriminated clearly as the recognition rate was 100% for all three types and the rejection rate was more than 60%. 70% of the validated samples were also identified correctly by the SIMCA model. The SIMCA model was also validated by comparing 70 standard herbs to the model. As a result, it was demonstrated that the macroscopic IR fingerprint method and the classification analysis could discriminate not only between the A. roxburghi samples and the standard herbs, it could also distinguish between the three different types of A. roxburghi plant in a direct, rapid and holistic manner.
A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the 'others' type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the "Collective Voting" (CV) package, which is easily scalable, maintained and used in practice.
Dioscorea oppositifolia is an important crop and functional food. D. oppositifolia tuber is often adulterated with D. persimilis, D. alata, and D. fordii tuber in the commercial market. This study proposed an integrated Fourier transform infrared spectroscopy (FT-IR) with chemometric approach to differentiate these four Dioscorea species. A total of 107 Dioscorea spp. tuber samples were collected from different locations in China. Principal Component Analysis (PCA), PCA-Class, and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were utilised to classify the FT-IR spectra. In this PCA is unable to differentiate the Dioscorea spp. tuber effectively. However, PCA-Class and OPLS-DA can distinguish spp. these 4 species Dioscorea tuber with high accuracy, sensitivity, and specificity. Additionally, the RMSEE, RMSEP and RMSECV values for OPLS-DA model were low, showing that it is a good model. The combination of FT-IR with the PCA-Class and OPLS-DA is practical in discriminating Dioscorea spp. tubers.
The interaction behaviours between components of polyacrylate (PAc)/poly(ethylene oxide) (PEO) and lithium perchlorate (LiClO(4)) were investigated in detail by Attenuated Total Reflectance (ATR)-Fourier Transformed Infrared (FTIR) spectroscopy. Solution cast films of the PAc/PEO and PAc/PEO/LiClO(4) were examined. No obvious shifting of the characteristic ether and ester group stretching modes of PEO and PAc was observed, indicating incompatibility of the binary PAc/PEO blend. The spectroscopic studies on the PAc/PEO/LiClO(4) blends reveal that Li(+) ions coordinate individually to the polymer components at the ether oxygen of PEO and the C-O of the ester group of PAc. Frequency changes observed on the nu(C-O-C) and omega(CH(2)) of PEO confirm the coordination between PEO and Li(+) ions resulting in crystallinity suppression of PEO. The absence of experimental evidence on the formation of PEO-Li(+)-PAc complexes suggests that LiClO(4) does not enhance the compatibility of PAc/PEO blend.
Copper ion recognition and DNA interaction of a newly synthesized fluorescent Schiff base (HPyETSC) were investigated using UV-vis and fluorescent spectroscopy. Examination using these two techniques revealed that the detection of copper by HPyETSC is highly sensitive and selective, with a detection limit of 0.39 μm and the mode of interaction between HPyETSC and DNA is electrostatic, with a binding constant of 8.97×10(4) M(-1). Furthermore, gel electrophoresis studies showed that HPyETSC exhibited nuclease activity through oxidative pathway.
Natural styrylpyrones isolated from fungi are known for various biological activities including antioxidant activity by scavenging free radicals. UV/vis spectra play an important role in elucidating chemical structures of these compounds via identification of chromophore units. With the aim of predicting the UV/vis spectra of a series of natural styrylpyrones, we tested TD-DFT, CIS and ZINDO methods in gas and in PCM solvent. The results showed that the individual or combined B3P86 and B3LYP hybrid functionals are suitable to predict the maximum wavelength absorption bands (λmax) for styrylpyrones. The structure property relationship (SPR) study emphasized the role of (i) structural parameters (e.g., hydrogen bond and the length of conjugated double bonds) and (ii) electronic descriptors (e.g., ionization potential, electronic affinity, hardness and electrophilicity) in bathochromic and hypsochromic shifts of maximum wavelength absorption bands (λmax) of styrylpyrone derivatives.
FT-IR spectrum of (2E)-3-(3-nitrophenyl)-1-[4-piperidin-1-yl]prop-2-en-1-one was recorded and analyzed. The vibrational wavenumbers were computed using HF and DFT quantum chemical calculations. The data obtained from wavenumber calculations are used to assign IR bands. Potential energy distribution was done using GAR2PED software. The geometrical parameters of the title compound are in agreement with the XRD results. NBO analysis, HOMO-LUMO, first and second hyperpolarizability and molecular electrostatic potential results are also reported. The possible electrophile attacking sites of the title molecule is identified using MEP surface plot study. Molecular docking results predicted the anti-leishmanic activity for the compound.
Interactive association of an antifungal drug, climbazole (CBZ) with the carrier protein in bovine circulation, bovine serum albumin (BSA) was explored by fluorescence and absorption spectroscopy along with in silico techniques. The fluorescence and absorption spectral alterations of the protein upon addition of CBZ affirmed the complex foration between CBZ and BSA. The inverse temperature dependence behaviour of the KSV values as well as the hyperchromic result of the protein's absorption signals characterized CBZ-triggered quenching of BSA fluorescence as the static quenching. A weak binding affinity (Ka = 3.12-1.90-× 103 M-1) was reported towards the CBZ-BSA association process. Interpretation of thermodynamic data (entropy change = +14.68 J mol-1 K-1 and enthalpy change = -15.07 kJ mol-1) and in silico analyses anticipated that hydrophobic forces, van der Waals forces and hydrogen bonds were the key intermolecular forces in the complex stabilization. Inclusion of CBZ to BSA produced microenvironmental perturbations around Tyr and Trp residues, and also significantly defended temperature-induced destabilization of BSA. The binding locus of CBZ was detected in the proximity of Sudlow's sites I (subdomain IIA) and II (subdomain IIIA) of BSA, exhibiting greater preference towards site II, as revealed by competitive site-marker displacement investigations and in silico analysis. The stability of the CBZ-BSA complex was further validated by the molecular dynamics simulation assessments.
Binding mechanisms of two selected pesticides, propazine (PRO) and quinoxyfen (QUI) with bovine serum albumin (BSA) was examined using fluorescence, absorption and molecular docking methods. Intrinsic fluorescence of BSA was quenched in the presence of both PRO and QUI. The quenching was ascertained to be conversely linked to temperature, which suggested the contribution of static quenching process in the PRO-BSA and QUI-BSA complex formations. This results were validated by the enhancement in absorption spectrum of BSA upon binding with PRO and QUI. Binding constant values (Kf = 9.55-0.60 × 10-3 M-1 for PRO-BSA system; Kf = 7.08-5.01 × 102 M-1 for QUI-BSA system) and number of binding site (n) values for the PRO-BSA and QUI-BSA systems at different temperatures affirmed a weak binding strength with a set of equivalent binding sites on BSA. Thermodynamic data obtained for both the PRO-BSA and QUI-BSA interactions predicted that the association process was spontaneous and non-covalent contacts such as hydrophobic interactions, van der Waals forces and hydrogen bonds participated in the binding reactions. This result was further supported by the molecular docking assessments. Three-dimensional spectral results revealed the microenvironmental alterations near tryptophan (Trp) and tyrosine (Tyr) residues in BSA by the addition of PRO and QUI. The docking analysis demonstrated the binding pattern for the PRO-BSA and QUI-BSA systems and disclosed the preferred binding site of both PRO and QUI as site I (subdomain IIA) of BSA.
Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.
Binding studies between a multi-targeted anticancer drug, sunitinib (SU) and human serum albumin (HSA) were made using fluorescence, UV-vis absorption, circular dichroism (CD) and molecular docking analysis. Both fluorescence quenching data and UV-vis absorption results suggested formation of SU-HSA complex. Moderate binding affinity between SU and HSA was evident from the value of the binding constant (3.04×104M-1), obtained at 298K. Involvement of hydrophobic interactions and hydrogen bonds as the leading intermolecular forces in the formation of SU-HSA complex was predicted from the thermodynamic data of the binding reaction. These results were in good agreement with the molecular docking analysis. Microenvironmental perturbations around Tyr and Trp residues as well as secondary and tertiary structural changes in HSA upon SU binding were evident from the three-dimensional fluorescence and circular dichroism results. SU binding to HSA also improved the thermal stability of the protein. Competitive displacement results and molecular docking analysis revealed the binding locus of SU to HSA in subdomain IIA (Sudlow's site I). The influence of a few common ions on the binding constant of SU-HSA complex was also noticed.