Derivative Spectra Research Articles

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.

Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

A New qNMR Compliant Savitzky-Golay Apodization Function for Resolution Enhancement.

In this work, we introduce a novel NMR apodization function designed to enhance spectral resolution while maintaining compatibility with qNMR standards. This function is based on a modified Savitzky-Golay filter, adapted for time-domain application. It effectively suppresses the negative components typically associated with derivative spectra, while also ensuring the preservation of quantitative integrity in NMR analyses.

Application of infrared spectroscopy to study carbon-deuterium kinetics and isotopic spectral shifts at the single-cell level

Microbial communities play crucial roles in shaping natural ecosystems, impacting human well-being, and driving advancements in industrial biotechnology. However, associating specific metabolic functions with bacteria proves challenging due to the vast diversity of microorganisms within these communities. In the past decades stable isotope probing (SIP) approaches, coupled with vibrational spectroscopy, have emerged as a novel method for revealing microbial metabolic roles and interactions in complex communities. In this study, we employed various combinations of heavy stable isotopes (D, 13C, 15N, and 18O), to evaluate all possible isotopic spectral shifts in the mid-IR region using Fourier-Transform Infrared (FT-IR) and Optical Photothermal Infrared (O-PTIR) spectroscopy, at both bulk community and single-cell levels. Additionally, we conducted a time-course study to explore the kinetics of CD vibration in Escherichia coli bacteria, allowing time-based sampling and assessment of isotopic labeling kinetics. The FT-IR and O-PTIR, along with the second derivative spectra of E. coli cells cultured in minimal medium supplemented with various combinations of heavy isotopes exhibited notable similarities. Several spectral shifts in primary vibrational peaks were observed due to the incorporation of heavy isotopes into biomolecules. Remarkably, the incorporation of deuterium into amide groups, resulting in the formation of nitrogen-deuterium bonds, caused a shift in amide A and B into the silent region, overlapping with CD signature peaks. The incorporation of 18O into the ester group of lipids and the carbonyl group of proteins resulted in a notable shift to the lower wavenumber region. Additionally, the second derivative of FT-IR spectral data highlighted the integration of 18O into α-helix and β-sheet structures. Furthermore, the spectra, second derivative, and PC-DFA scores plot of FT-IR data collectively illustrated the practicality of monitoring 13C and D incorporation into E. coli bacterial cells within the first 30-min incubation period. Significance: The findings of this study suggest that FT-IR and O-PTIR can serve as efficient tools for monitoring the incorporation of heavy isotopes into bacteria at both the population and single-cell levels. Additionally, the SIP approach allowed us to assign two new deuterium-associated vibrational peaks to their corresponding functional groups, which to the best of our knowledge have not been reported previously.

Implantation of impressive chromatographic and built-in UV spectrophotometry approaches for sustainable Estimation of olopatadine and mometasone in pharmaceuticals; eco-scale and BAGI applications

The development of analytical methods that adhere to the principles of sustainable green chemistry is essential in the modern scientific arena. This research presents two novel methods for the concurrent quantification of olopatadine (OLO) and mometasone (MOM), targeting sustainable green chemistry principles in their raw materials, dosage forms and combined nasal spray formulations. The first method is a rapid isocratic high-performance liquid chromatography (HPLC) that achieves excellent resolution within 4 min, with retention times of 2.37 ± 0.1 min for OLO and 4.05 ± 0.1 min for MOM. It features optimal sensitivity, with detection limits of 0.13 µg/mL for OLO and 0.03 µg/mL for MOM. It utilizes a cyano column with an isocratic mobile phase of acetonitrile and 0.1 M OPA (50: 50, v/v), making it accessible to standard laboratory equipment. The second method is UV–visible spectrophotometry, which includes first-order derivative measurement (D1) and ratio derivative spectra (RDS) methods. These spectroscopic approaches provide linear response range of 0.2–20.0 µg/mL for OLO and 4.0–40.0 µg/mL for MOM, respectively serving as cost-effective alternatives for labs with limited resources. Validation results show high accuracy (98–102 %) and precision (RSD < 2 %). Green assessments using Eco-Scale indicate scores of 86 for HPLC and 100 for spectrophotometry, while the “blueness” concept via BAGI demonstrates strong sustainability with scores of 87.5 and 90. These methods effectively quantify OLO and MOM in their raw materials, different formulations and combined nasal spray, providing a sustainable and reliable approach for pharmaceutical analysis. This work is the first green assessment for the determination of OLO and MOM in pure drug and in the pharmaceutical formulations using less hazard solvent and simple technique.

Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

Chemometric approaches for discriminating manufacturers of Korean handmade paper using infrared spectroscopy

The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

Photooxidation of Toluene Derivatives under HAT and ROS Synergy.

The valorization of toluene offers a dual solution by addressing its environmental impact while also facilitating the synthesis of a diverse array of valuable fine chemicals and pharmaceutical intermediates, thus ensuring both ecological sustainability and economic viability. We report herein a synergistic approach that harmonizes hydrogen atom transfer (HAT) process with the generation of reactive oxygen species (ROS) under mild condition and low catalyst loading, which enables the efficient synthesis of a broad spectrum of esteemed benzoic acid derivatives and aryl ketones through the photocatalytic oxidation of toluene derivatives. Mechanistic elucidation reveals that the HAT reagent anthraquinone has both the capabilities to abstract hydrogen atoms and the ability to generate singlet oxygen 1O2 during energy transfer with triplet oxygen 3O2, and the combination of these two potencies significantly improves the catalytic efficiency of the reaction. This study not only introduces the amalgamation of HAT with ROS generation but also delineates a systematic approach for the selection of HAT reagents with energy transfer proficiency for ROS generation in catalytic oxidation reactions.

Smart mathematically filtered UV spectroscopic methods for quality assurance of rosuvastatin and valsartan from formulation

Abstract Valsartan and rosuvastatin together in a binary form have been utilized to reduce hypertension and hyperlipidemia to control cardiovascular complications. This study depicts the simple three mathematically manipulated UV spectroscopic techniques for the estimation of rosuvastatin and valsartan in the formulation. The first method is simple UV absorption at 310 nm by RST and the first derivatization method for VTN. Determining the magnitude difference of a ratio spectrum at two identified wavelengths is the second approach, and determination of the magnitude of the first derivatives of the ratio spectra of RST and VTN constitute the third technique. The selection of wavelengths, divisor concentrations, and peak amplitudes were optimized and validated. The straight line was constructed in the range of 1–30 and 2–25 µg/ml for RST and VST by the normal and first derivatization method. By using the magnitude difference and magnitude of first derivative ratio spectra approaches, the concentrations of 1–12 and 2–25 µg/ml for RST and VTN, respectively, displayed a straight line. The limit of quantification was less than 1 µg/ml for RST and less than 2 µg/ml for VTN. It was eventually found that the accuracy, expressed as a percentage recovery, ranged between 98.94 and 99.55% for RST and 100.36 and 101.08% for VTN. The % RSD did not exceed 1.82 and 1.91 for RST and VTN, respectively. The three techniques were used to accurately measure RST and VTN in their binary formulations and physically mixed solutions, and the results were statistically compared to the previously published HPLC technique. The outstanding recovery achieved by using the authentic standard addition approach validated the methods’ supplemental accurateness. The Analytical Greenness and Red Green Blue procedures verified the eco-friendliness of the suggested UV spectroscopic approaches, which were also found to be superior to the documented HPLC methods.

Rapid and simultaneous determination of ultratrace pefloxacin and fleroxacin residues in wastewater by Al3+ sensitized first derivative synchronous fluorescence spectrometry

In this study, a new first derivative synchronous fluorescence spectrometry (SFS) for the accurate and sensitive determination of pefloxacin (PFX) and fleroxacin (FLX) in wastewater was established. PFX and FLX were complexed with Al3+ to significantly enhance their fluorescence intensity. Because the conventional fluorescence spectra of PFX-Al3+ and FLX-Al3+ overlap significantly, the wavelength difference (Δλ = 155 nm) was chosen for SFS scanning to narrow spectral band. And the narrowed synchronous fluorescence spectra was subjected to first derivative processing. Finally, the zero intersections of the first derivative synchronous fluorescence spectra at 281.6 nm and 273.6 nm were chosen for the determination of PFX and FLX, cleverly used of spectral parameters to eliminate the serious interference between PFX-Al3+ and FLX-Al3+ and the interference of wastewater background. The limits of detection of PFX and FLX were 0.0086 ng·mL−1 and 0.0068 ng·mL−1 with linear ranges from 0.1–40 ng·mL−1 and 0.2–80 ng·mL−1, respectively. Recoveries of PFX and FLX in wastewater were 88.60 %–98.60 % and 90.00 %–95.60 %. The first derivative SFS established in this study used spectral separation instead of chromatographic separation, so that sample pretreatment was simple and the detection speed was fast. Compared the conventional method of HPLC, the test results of above two methods were close. And the new method had been successfully used for the determination of PFX and FLX residues in wastewater with good results. Therefore, the new first derivative SFS was expected to become a routine detection technology for PFX and FLX residues in wastewater and widely used.

FTIR Reveals Novel Insights into 5‐FU and Carmofur's Impact on Breast Cancer Cells

AbstractIn this study, the effects of 5‐FU, and its derivate Carmofur (Car) on MCF‐7 and MDA‐MB‐231 cells, were examined using FTIR. Both drugs showed a similar response in the protein and nucleic acid region of MCF‐7 but opposite results in MDA‐MB‐231 cells. Under the influence of the drugs, the absorption intensity of the proteins decreased in MCF‐7, while it increased in MDA‐MB‐231 cells. The rate of change was higher in Car. In the fingerprint region, both cells showed the opposite effect. Although the intensity of absorbance in MCF‐7 increased with 5‐FU and Car, a decrease was measured in MDA‐MB‐231 cells. An exception in the nucleic acid region was the peak assigned to the asymmetric PO2 peaks in RNAs. Although a specific marker for apoptotic cells, ester‐carbonyl binding, appeared in MCF‐7 cells with 5‐FU and Car at 1745 cm−1, this binding was not seen in MDA‐MB‐231 cells. The amide I region of the cells was analyzed using the second derivative spectrum. This study aimed to use FTIR spectroscopy to evaluate the response of two different tumor cells to chemotherapy and to provide a clinical prognosis.

Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress.

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra (R') was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra (log(1/R)) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability.

Oscillation spectra of surface resistance of low-dimensional organic conductors in a tilted magnetic field

The resonance spectrum of the surface resistance derivative in organic conductors has been calculated numerically for the out-of-plane and in-plane magnetic field orientation. Not all but some of the resonances in a given series of transitions are present in the spectra. They are observed as maxima in the surface resistance derivative curve. For tilted magnetic fields, apart from the resonances for transitions between the adjacent surface states, there also appear resonances corresponding to the transitions between more distant surface states. The calculations allow for the obtain of additional information on the surface properties for fabrication of devices based on organic molecular conductors.

Interface stiffness identification of rough and weak bonded interface using developed ultrasonic reflection phase derivative spectrum

The thickness and interface roughness of coatings both affect the interface bonded quality. Existed ultrasonic testing methods based on traditional phase screen approximation or spring model assumption are difficult to simultaneously identify the interface roughness and stiffness of coating. This paper, a new method for integrated identifying coating thickness, interface roughness, and interface stiffness using developed ultrasonic reflection phase derivative spectrum (URPDS) is proposed. A phase-screen-approximated spring-model (PSASM) for ultrasound vertically propagating into rough and weak bonded interface is constructed. On basis of PSASM, a URPDS of coating/substrate structure is developed for identifying the interface stiffness and other parameters of coated parts. Cross-correlation analysis is used to eliminate the phase deviation of URPDS introduced by reference signal and initial phase of tested signal. Sensitivity analysis is used to determine the high-sensitivity regions of URPDS to interface roughness and interface stiffness. Genetic algorithm optimization is used to achieve integrated identification of coating thickness, interface roughness, and interface stiffness. The rationality of PSASM is verified through numerical simulation using a series of coating/substrate models with rough and weak bonded interface, and the relationship between the high-sensitivity regions and the high-precision measurement ranges of interface roughness Rq and interface stiffness Kn is clarified. Ultrasonic experiments are implemented on Nickel-coating samples and coated parts using plane wave probe. The coating thickness, interface roughness, and interface stiffness could be identified accurately, which shows that the proposed URPDS method can identify the interface stiffness of rough contacted dissimilar media or coated parts with rough interface.

Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum

Efficiently sorting coal gangue and identifying coal types are vital operations in coal preparation, yet they are traditionally resource-consuming, labor-intensive, and potentially hazardous. This work puts forward an straightforward method employing mid-infrared spectroscopy with first derivative spectrum to address these issues. The proposed technique focuses on the delineation and enhancement of characteristic spectra to detect subtle differences among samples. The method utilizes just a few characteristic spectra of 3740–3700 cm−1, 1790–1750 cm−1, 1615–1583 cm−1, 1580–1540 cm−1, 1550–1440 cm−1, 1270–1210 cm−1 and 867–854 cm−1 to achieve 100 % high-accuracy classification of coal gangue and identification of coal types with total 250 spectra, such as bituminite, anthracite, lignite, roof sandstone and gangue, without the need for secondary sample processing or the assistance of machine learning algorithms, simplifying the process considerably. Such a strategy not only significantly improves the efficiency of coal sorting but also endorses real-time on-site detection. It offers a theoretical foundation for advanced coal separation technology and its implementation in real-world mining operations.

Paddy seed viability prediction based on feature fusion of color and hyperspectral image with multivariate analysis

Seed viability is essential to have a homogeneous plant population. The seed industry cannot adopt traditional procedures for seed viability evaluation since they are destructive, time-consuming, and need chemicals. This study aimed to investigate the potential of combining hyperspectral and color image features to differentiate viable and non-viable paddy seeds. The hyperspectral and color image of the 355 paddy seeds was captured and later used to examine their viability. An image processing algorithm was developed to extract features from color images of paddy seeds and investigated significant differences in the retrieved feature data using variance analysis. The spectra were extracted from the selected region of interest (ROI) of the hyperspectral paddy seed image and averaged. In the next step, the partial least square discrimination analysis (PLS-DA) model was developed to distinguish viable and non-viable paddy seeds. Initially, the PLS-DA model was developed using spectral data with different preprocessing techniques, and the result obtained an accuracy of 88.9 % in the calibration set and 86.1 % in the prediction set using Savitzky-Golay 2nd derivative preprocessed spectra. With the fusion of spectral and significant color image features, the model's accuracy improved to 93.3 % and 90.9 % in the calibration and prediction sets, respectively. Results also showed that the fusion of selected color image features with Savitzky-Golay 2nd derivative preprocessed spectra could achieve higher F1-score, recall, and precision values. The visualization map for the viable and non-viable paddy seeds was also developed utilizing the most effective predictive model. The results demonstrate the possibility of using the fusion of the hyperspectral and color image features to sort seeds according to viability, which may be applied in developing an online seed sorting method.

PREDIKSI KANDUNGAN MINYAK DAN ASAM LEMAK BEBAS BUAH SAWIT SECARA NON-DESTRUKTIF MENGGUNAKAN NEAR-INFRARED SPECTROSCOPY DAN METODE KALIBRASI HYBRID

The study used near-infrared reflectance spectroscopy and hybrid calibration methods to predict oil and free fatty acid content of oil palm fruitlets non-destructively. The reflectance and chemical content of oil palm fruitlets were measured and the calibration between near infrared spectra and chemical content was performed using hybrid calibration methods. The developed hybrid calibration model was validated to predict oil and free fatty acid of other oil palm fruitlets. The best models to predict oil and free fatty acid of oil palm fruitlets respectively were the hybrid model of Partial Least Square-Artificial Neural Network with 25 factor components (R2 = 0.96; SEP= 2.21%, RPD = 4.79) and 19 factor components (R2 = 0.96; SEP= 0.25%, RPD = 4.24) using Savitzky-Golay first derivative spectra pre-treatment.

A Spectral Detection Method Based on Integrated and Partition Modeling for Trace Copper in High-Concentration Zinc Solution.

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid.

Unraveling the infrared spectrum of graphene oxide

The applicability of infrared spectroscopy toward graphene oxide (GO) and its derivatives is currently very limited, because the exact location of absorbance bands, originated by the main functional groups of GO, in the spectrum is still under debate. This obstacle leads to widespread misinterpretation of FTIR spectra in literature, and to invalid conclusions on attaining target functionalization reactions. In this work, by involving GO in four types of reactions, for the first time, we identify absorbance bands, originated by the main functional groups of GO: epoxides and tertiary alcohols. Also, we identify the bands, associated with carboxyl and carboxylate groups. In addition, we reconfirm the band assignment for absorbed water molecules (1619 cm−1) and covalent sulfates (1221 cm−1 and 1410 cm−1). Finally, we show conditions for appearance, and the exact position of the band of the C=C bond stretch. All together we accurately assign eight previously unidentified or incorrectly assigned absorbance bands in the fingerprint region of the spectrum, plus three bands which appear in the spectra of specific forms of GO. The assignment of the bands is independently confirmed by the 13C SSNMR spectroscopy. Summing up the newly assigned and the re-confirmed bands, for the first time, we report a complete library of the main absorbance bands in the FTIR spectrum of GO and GO derivatives.

Green method for quantification of lavender and sweet orange essential oils in blends by synchronous fluorescence first derivative

A simple green method for the quantification of sweet orange and lavender essential oils (EOs) content in blends was developed using synchronous fluorescence of the first derivative spectra and sample preparation by surfactant-free microemulsions (SFMEs). Excitation and emission wavelengths pairs (λex/λem) were determined for both EOs (at 336/436 nm for sweet orange and at 330/388 nm for lavender). Optimization was conducted to establish the condition to prepare the SFMEs systems: 50 μL of oily phase containing EO diluted in octan-1-ol (1:4, v/v), 2.0 mL of water and propan-1-ol up to 5.0 mL final volume. Different proportions of EO in the oily phase were evaluated for analytical curves construction. Synchronous fluorescence measurements were performed and the acquired data were corrected by inner filter effect. Considering the first derivative of the synchronous scanning, data were extracted at 316.8 nm (Δλ = 100 nm) and 352.0 nm (Δλ = 58 nm) for sweet orange and lavender EOs, respectively, with correlation coefficients above 0.99. Limits of quantification of 7.97 μg mL−1 and 65.80 μg mL−1 were obtained indicating that 0.46% of sweet orange and 3.69% of lavender EOs can be quantified in mixtures. Simulated blends (25:75%, 50:50% and 75:25%, volume proportions) were also evaluated with recoveries of 71.5 – 128.9% and coefficient of variation between 3.1% – 11.6%. Additionally, the method's greenness was evaluated through three green metrics (Analytical Eco-Scale, GAPI, and AGREE) with consistent results.