Infrared Spectral Data Research Articles

Remote sensing-based green and blue agricultural water footprint estimation at the river basin scale

Since the development of Water Footprint environmental indicator, significant research on blue and green crop water use and the respective water footprint estimations has been published. Such research is commonly approached using different methodologies that leverage tabulated values for crop development characterisation, while studies based on remote sensing data are less abundant, despite crop monitoring using remote sensing-based vegetation indices having demonstrated great capabilities and operability. To help fill this gap, we present a methodology that uses a remote sensing vegetation index time series from Sentinel-2 satellite near infra-red and red spectral bands data to derive basal crop coefficient time series to subsequently be used under the Remote Sensing-based Soil Water Balance approach that follows the globally operative FAO56 procedure. It provides pixel-based temporal and spatially distributed estimations of net irrigation requirements and adjusted crop evapotranspiration, with the aim being to divide up the latter and estimate the remote sensing-based green and blue crop water use and the subsequent green and blue water footprint. This is all done under the Agricultural Water Footprint Assessment framework for a growing crop or tree. This methodology was applied over a large, crop-diverse Spanish river basin district (Júcar) and across two different climatological years (humid vs. dry). Its feasibility was demonstrated by the acceptable behaviour of the remote sensing-based blue crop water use estimation for different herbaceous and woody crops, against the official dataset for irrigation water accounting at two water management scales (of a relative mean absolute error of 15.4 % in the case of the largest water user association and of 17.1 % in the case of the river basin water authorities’ own estimations). The proposed approach, which we call Remote Sensing-based Agricultural Water Accounting and Footprint, aims to provide reliable and accurate spatially and temporally distributed thematic cartography about the remote sensing-based blue and green crop water use and water footprint. This information is essential for water managers with the goal of generating transparent and complementary information to incorporate into their own working scales.

Machine learning on white mica short-wave infrared (SWIR) spectral data in the Tengjia Au deposit, Jiaodong peninsula (Eastern China): A prospecting indicator for lode gold deposits

Lode gold deposits are the primary source of global gold resources and possess significant mineralization potential at depth, necessitating new strategies to locate deep concealed orebodies. The Tengjia Au deposit, a newly-discovered concealed altered rock-type lode gold deposit (50 t @ 3.89 g/t), is located within the Zhaoping metallogenic belt of the illustrious gold-rich Jiaodong peninsula in Eastern China. It is distinguished by pervasive phyllic alteration associated with gold mineralization, making it an ideal target for mineral geochemical exploration in lode gold deposits. The mineralization and alteration at Tengjia unfold across three distinct stages, delineated by mineral assemblages and textural relationships: K-feldspar-quartz (I), quartz-sericite-native gold-sulfide (Ⅱ), quartz-calcite-galena-sphalerite (Ⅲ) stages.Systematic analysis of short wavelength infrared (SWIR) spectra, coupled with petrographic observation, has unveiled an abundance of white micas (montmorillonite, muscovite, illite, paragonite, and phengite) within Stage Ⅱ at Tengjia. The Al-OH absorption feature wavelengths (Pos2200), as well as illite crystallinity (IC) values, exhibit a discernible shift towards longer wavelengths (>2204 nm) and higher values (>1.4) in the vicinity of ore deposition, which likely resulted from intense water–rock interaction between ore-forming fluid and wall rocks. Discriminant analysis of the orthogonal partial least squares method (OPLS-DA) shows that the absorption wavelengths corresponding to Water, –OH, and Al-OH effectively differentiate between ore and wall-rock samples. Additionally, analysis using the random forest algorithm (RF) demonstrates that spectral data from Tengjia white micas can reliably classify orebodies, achieving an accuracy of 83.2 %. Hence, the findings suggest that the unique SWIR spectral features of white micas offer a valuable tool for detecting the concealed Tengjia gold mineralization. This study proposes a novel approach that integrates machine learning technology with SWIR analysis for the identification of concealed lode gold deposits.

Photooxidation‐induced conformational changes and degradation behaviors of poly(butylene succinate) and poly(butylene succinate‐co‐adipate)

AbstractTwo biodegradable polyesters, poly(butylene succinate) (PBS) and poly(butylene succinate‐co‐adipate) (PBSA), were artificially aged in a UV irradiation test chamber, and their photodegradation behaviors investigated. The attenuated total reflection (ATR) infrared (IR) spectra of the initial and UV‐aged samples revealed conformational changes in the tetramethylene sequences of the photoaged PBS and PBSA. The one‐dimensional wide‐angle X‐ray scattering (WAXS) profiles showed that the (110) d‐spacing of the α‐form crystal progressively decreased during UV aging, suggesting that the crystalline polymer chains became arranged more closely along the fiber axis. Accordingly, we conclude that PBS and PBSA transform from their TGTGT to nearly TTTTT conformations upon photoaging. The observed WAXS, small‐angle X‐ray scattering, and IR spectral data suggest that photodegradation preferentially occurs in the amorphous phase of each polymer. The less crystallizable butylene adipate co‐unit enhances the degradability of PBSA, with prolonged photooxidation leading to partial degradation of the crystalline region of the copolymer. This finding is consistent with and may be linked to the higher biodegradability of PBSA compared to PBS.

A high-precision retrieval method for methane vertical profiles based on dual-band spectral data from the GOSAT satellite

Satellite remote sensing has become an important method of monitoring methane concentrations. The full-physics algorithm is widely used for methane vertical profile retrieval, but the method is relatively time-consuming. Most current studies have focused on the retrieval of methane vertical profiles from thermal infrared (TIR) spectral data only, resulting in low accuracy in the near-surface retrieval results. To obtain the atmospheric methane vertical profile from satellite sounding data quickly and accurately, this paper proposes a high-precision retrieval method based on the ResNet18 model, a conventional and effective deep convolutional neural network, using TIR band and shortwave infrared (SWIR) band spectral data from the GOSAT. The model performs well on the test set, with mean absolute error (MAE) below 12.78 ppb and root mean square error (RMSE) below 23.71 ppb with pressure greater than 150 hPa. The accuracy of the retrieval results and GOSAT CH4 vertical profile products are validated using airborne measurement data. In the mid-latitude region, using NOAA airborne measurement data as reference values, the average of MAE and RMSE of the retrieval results between 500 hPa and 900 hPa are reduced by 12.05 ppb and 14.07 ppb, respectively, compared to the GOSAT CH₄ vertical profile product. In the low-latitude region, using CARIBIC airborne measurement data as reference values, the average of MAE and RMSE between 300 hPa and 600 hPa are reduced by 11.18 ppb and 12.20 ppb, respectively. The validation results show that the methane vertical profile can be quickly and accurately retrieved based on the ResNet18 model, combined with dual-band spectral data, temperature, and surface pressure.

A chemistry-based explainable machine learning model based on NIR spectra for predicting wood properties and understanding wavelength selection

ABSTRACT A chemistry-based explainable machine learning (ML) approach was used to predict wood properties using near infrared (NIR) spectral data collected from rough and smooth surfaces, and to provide better understanding of the role of important NIR wavelengths (features) in the performance of ML models. NIR spectra collected from western hemlock (Tsuga heterophylla) and coastal Douglas-fir (Pseudotsuga menziesii) boards with rough and smooth surfaces were fed into random forest and TreeNet; a gradient boosting machine algorithm, for predicting wood density, modulus of elasticity (MOE) and modulus of rupture (MOR). The TreeNet model could predict the MOE, MOR, and density with R2 of 0.66, 0.64, and 0.64 using spectra collected from rough surface and R2 of 0.54, 0.46, and 0.46 using spectra collected from smooth surface. TreeNet outperformed the random forest, and for both ML algorithms higher R2 and lower error were obtained using NIR data collected from rough surfaces. This suggested that for Douglass fir and western hemlock, NIR spectra could be collected on a sawn surface prior to surface planing. However, it is difficult to generalize the impact of surface roughness on the performance of predictive model as different factors (e.g. what constitutes a smooth or rough surface, variability of data set in terms of wood properties) impact the success of predictive models. NIR features having the greatest influence on TreeNet models were examined and consistently had wood chemistry specific band assignments. The most important features occurred in the O-H first overtone, and C–H second overtone regions and a narrow zone (approximately 2400–2500 nm) of the C–H stretch C–C stretch combination region. Important features also differed by property and with surface roughness. Explaining ML model performance using the relative importance of the NIR features showed the importance of wood chemistry related information when developing models, however MOE and MOR TreeNet models based on smooth surface NIR spectra showed an increased importance of water related features. Overall, the chemistry-based explainable machine learning model approach allows for identification of important NIR features, and regions, and aids in understanding how they contribute to the performance of NIR-based wood property predictive models.

Formulation of multicomponent inclusion complex of cyclodextrin-amino acid with Chrysin: Physicochemical characterization, cell viability and apoptosis assessment in human primary glioblastoma cell line

Chrysin (CR) is a water-insoluble drug reported for different therapeutic effects. The microwave irradiation method was used in this study to create a multicomponent inclusion complex (CR-MC) containing CR (drug) and carrier hydroxyl propyl beta cyclodextrin (HP β CD) and L-arginine (LA). The prepared inclusion complex (CR-MC) was evaluated for dissolution study and results were compared with chrysin physical mixture (CR-PM). Further, the samples were assessed for infra-red (IR), nuclear magnetic resonance (NMR), differential scanning calorimeter (DSC), scanning electron microscope (SEM) and molecular docking. Finally, the cell viability, reactive oxygen species and flow cytometer studies were also assessed to check the potential of the prepared inclusion complex on the human primary glioblastoma cell line (U87-MG cell). The phase solubility findings revealed a stability constant (773 mol L−1) as well as a complexation efficiency of 0.027. The dissolution study displayed a significant increase in CR release from CR-MC (99.03 ± 0.39%) > CR-PM (70.58 ± 1.16%) > pure CR (35.29 ± 1.55%). NMR and IR spectral data revealed no interaction between CR and carriers. SEM and DSC study results revealed the conversion into amorphous form. The molecular docking results illustrated a high docking score, which supports the findings of complex formation. The cell viability, reactive oxygen species, and flow cytometry studies results showed enhanced activity from CR-MC against the tested human primary glioblastoma cell line. From the results it has been observed that chrysin solubility significantly increased after complexation and there in vitro activity also enhanced against cancer cell line.

Effect of wheat roasting conditions and wheat type on short-wave infrared (SWIR) spectral data of whole and milled wheat by ANOVA-simultaneous component analysis

ANOVA-simultaneous component analysis (ASCA) was used to investigate the effect of roasting and wheat type on shortwave-infrared (SWIR) spectra of whole wheat and flour through assessment of statistical significance and characterisation of the contributing spectral features. The full factorial experimental design included two wheat types, three roasting temperatures and three roasting frequencies. SWIR spectral images were collected from the two roasted wheat types and their two milled samples. Three ASCA models, one for each wheat conformation (kernel, whole wheat flour, white flour) were investigated. It was evidenced that all factors and interaction in the whole wheat flour model had a significant (p ≤ 0.05) effect on spectral data. Only the factor roasting frequency was not significant in white flour model and only the interaction between roasting frequency and wheat type was not significant for the kernel model. The main variations in the loading line plots were identified and characterised by chemical structural differences that occur within the sample. The effect of roasting frequency had a more adverse effect on protein stability, moisture evaporation, water soluble carbohydrates and aromatic amino acids, compared to roasting temperature. A Rapid Visco-Analyser (RVA) was used to further investigate difference in wheat type as almost all spectral data sets differed significantly. The most prominent difference between the two wheat types was observed as differences in amylase activity and presence of lipids. ASCA applied to SWIR whole wheat and flour spectral data effectively characterised the significant effect of roasting on wheat starch and protein structures.

Development of a quantitative method to evaluate the printability parameters of water-based ink using visible and near infrared spectroscopy

Water-based inks are widely used in green packaging and printing. The printability parameters of water-based inks, such as viscosity (alcohol concentration (AC)) and color (toning additive concentration (toning yellow concentration/toning red concentration, TYC/TRC)), can only be controlled manually in many printing companies. The printability parameters of water-based inks with different additives were analyzed using spectral preprocessing, variable selection, and model-building methods with visible and near infrared (vis-NIR) spectral data (380∼980 nm). Model performance was compared using the root mean square error of cross-validation (RMSEC) and the coefficient of determination (R2). The results of the experiment indicate that the viscosity of the water-based inks can be quantitatively predicted using the principal component analysis and back propagation neural network model (PCA-BPNN) combined with Savitzky-Golay (SG) smoothing in the spectral subrange, which is superior to the PLS regression model. The R2c and r2p of the PCA-BPNN model were up to 0.998 and 0.993, and the RMSEC and RMSEP values obtained were 0.21 and 0.34. Similarly, the concentration of toning yellow and toning red can be quantitively predicted using the PCA-BPNN model combined with SG smoothing in the 617∼726 nm spectral range, which is better than iPLS regression model. These results indicate that the use of vis-NIR spectroscopy and chemometrics is a promising strategy, reliable for predicting the printability parameters of water-based inks, and provides the technical basis for subsequent implementation of online inspection.

Antioxidant Activity Enhancement Effect of Silver-Ionized Water: Silver Cation Prepared by Electrolysis

In the present study, tap water, alkaline electrolyzed water (AlEW) and tourmaline water (TMW) were used as the electrolytes to generated the silver-ionized water (SIW), AlEW-SIW and TMW-SIW, respectively. The antioxidant properties of the samples containing ascorbic acid (AsA) were investigated by WST-kit method. The results showed that the SOD activity of AsA (2 mmol/L) dissolved in SIW (66.0%) was enhanced by about 8% compared to that of the tap water (57.9%). The SOD activity of the AlEW-SIW solution (77.3%), which was higher than that of the SIW solution, and lower than that of the AlEW solution (83.6%). The SOD activity of the TMW-SIW solution (83.0%) was similar to that of the TMW solution (82.5%). Furthermore, to classify the sample solutions, discriminant analyses were performed based on near infrared (NIR) spectral data, which was consistent with the results of the WST-kit method. The SOD activity of the AlEW-SIW and TMW-SIW solutions decreased slowly with storage time, and their SOD activities were higher than that of AlEW, TMW and the tap water solutions at storage time of 14 days. In summary, AlEW-SIW and TMW-SIW showed similar antioxidant activity enhancement as AlEW and TMW, and they also maintained the stability of the antioxidant activity of AsA during storage.

Multidimensional soil salinity data mining and evaluation from different satellites

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm−1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm−1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm−1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

Cr(III) and Ni(II) complexes of isatin-hydrazone ligand: preparation, characterization, DFT studies, biological activity, and ion-flotation separation of Ni(II)

In the current work, a ligand N'1-((E)-2-hydroxy-3H-indol-3-ylidene)-N'3-((E)-2-oxoindolin-3-ylidene)malonohydrazide (H4MDI) and its chelates [Cr2(H2MDI)(H2O)2Cl4] and [Ni2(MDI)(H2O)6].4H2O have been isolated and characterized using different physico‐chemical methods, as infrared (IR), thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) in the solid state, and 1H‐NMR, 13C‐NMR and UV/visible in solution. For evaluating the geometries for the synthesized ligand and its complexes, calculations were performed using Gaussian 9 program with density functional theory (DFT). Magnetic and UV/visible measurements proposed that both Cr(III) and Ni(II) chelates have octahedron coordination frameworks. On the other hand, the IR spectral data revealed that the ligand behaves as a binegtive hexadentate in [Cr2(H2MDI)(H2O)2Cl4] and as a tetranegative hexadentate in [Ni2(MDI)(H2O)6].4H2O. The molecular modeling was done, and illustrated bond lengths, bond angles, molecular electrostatic potential, and chemical reactivity for the inspected compounds. In addition, the behavior of thermal decomposition for prepared complexes was discussed. Two comparable methods (Coats-Redfern and Horowitz-Metzger) were used to calculate the kinetic parameters of the resulted thermal decomposition stages. Furthermore, the ion-flotation process was used for the separation of Ni(II) from aqueous media via the prepared ligand as a chelating agent and oleic acid as a surfactant. Moreover, the antimicrobial behavior of the synthesized moieties was investigated against various bacterial and fungal strains. H4MDI has the most pronounced activity with minimum inhibitory concentration (MIC) of 0.78 µg/mL for both E. coli, and C. Albicans, while Ni(II) complex shows the activity against S. aureus, E. coli, and C. Albicans with MIC of 3.42, 6.84, and 1.71 µmol/L, respectively. Finally, the in-vitro cytotoxic activity of the prepared compounds against human hepatocellular carcinoma cells (HePG-2) has been examined, and the obtained results revealed that H4MDI and its Ni(II) complex show an average activity against HePG-2 with IC50 of 9.7 and 7.7 µmol/L, respectively compared with the 5-flurouracil (applied standard drug) with IC50 = 7.9 µmol/L.

Rapid Estimation of Water Stress in Choy Sum (Brassica chinensis var. parachinensis) Using Integrative Approach.

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90–100% FC as no water stress (NWS); 80–90% FC for light water stress (LWS); 70–80% FC for moderate water stress (MWS); and 60–70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSIW) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSIW, VIs, and water stress indicators were statistically analyzed with R2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSIW obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSIW and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

Abstract P2-08-07: Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure

Abstract Introduction: Ductal carcinoma in situ (DCIS) is a potential precursor of invasive breast cancer. The uncertain trajectory of DCIS, to progress to invasive disease or to remain in situ, currently drives treatment, despite lack of proven benefit. Therefore, understanding the molecular features of the DCIS trajectory may prevent overtreatment of this disease. Breast calcifications are a common feature in DCIS and are seen mammographically in over 80% of cases. Calcifications have been largely characterised based only on x-ray morphology; their chemical composition, their association with the surrounding soft tissue and role in DCIS and invasive breast cancer biology is largely unexplored. In this regard, a bio-photonic approach, based on infrared (IR) and Raman spectroscopy in combination with machine learning, was used to study DCIS by probing the chemical composition of calcifications and the surrounding soft tissue in breast lesions. The main aim of the work is to identify molecular compositional changes in calcifications and in soft tissue that potentially accompany or drive the progression of DCIS to invasive breast cancer, or indicates a stable DCIS phenotype. Methods: Serial tissue sections from 303 patients with (i) ‘pure DCIS’ (DCIS without recurrence) (n=158), (ii) ‘DCIS with invasive recurrence’ (DCIS from a patient who subsequently was known to develop invasive disease) (n=123) and (iii) ‘DCIS plus invasive cancer contemporaneously’ (n=22), were measured using mid-IR imaging and Raman mapping. The same calcifications and soft tissue regions from specific DCIS ducts were targeted across the techniques on the serial sections. Spectral images were analysed using cluster analysis followed by unsupervised and supervised machine learning classification models to identify spectral features associated with the progression of DCIS to invasive breast cancer. Results: Segmentation of IR and Raman spectral images based on cluster analysis identified important histopathological features including calcifications, epithelium, necrotic areas, connective tissue and stroma based on the spectral heterogeneity. Based on analysis of Raman calcification data from 145 patients with (i) ‘pure DCIS’ (n=90) and (ii) ‘DCIS with invasive recurrence’ (n=55), an area under the receiver operating characteristic (AUROC) mean value of 85% was obtained in distinguish pure DCIS from DCIS that later recurred as invasive cancer. The calcification features appeared to indicate pathology specific changes in phosphate and carbonate content and appearance of magnesium whitlockite. Similar analysis of the surrounding soft tissue spectral features showed an AUROC mean value of 76%, which showed changes in protein secondary structure and content, particularly in the necrotic regions surrounding calcifications. In addition, classification models are being developed and refined from the IR spectral data, the initial results of which have shown an AUROC value of only 54% from the same patients’ data. Perspectives: It is anticipated that the current novel approaches allowing label-free measurement of calcifications and soft tissue will provide important cues in understanding DCIS prognosis and could be a promising way forward in determining DCIS management. Current and future efforts include identification of specific discriminatory spectral features for molecular and pathological correlation. Acknowledgments: This work was supported by Cancer Research UK and by KWF Kankerbestrijding (ref. C38317/A24043). Citation Format: Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Allison Hall, Sarah Pinder, Ihssane Bouybayoune, Lorraine King, Jeffrey Marks, Esther Lips, Thomas Lynch, Donna Pinto, Jelle Wesseling, Shelley Hwang, Keith Rogers, Nick Stone, on behalf of the Grand Challenge PRECISION consortium. Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-08-07.

A 1D-SP-Net to Determine Early Drought Stress Status of Tomato (Solanum lycopersicum) with Imbalanced Vis/NIR Spectroscopy Data

Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato (Solanum lycopersicum); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner.

Separation and Characterization of Novel Degradation and Process Related Impurities of Bedaquiline Bulk Drug

Bedaquiline (BDQ) is a new drug approved by United States Food and Drug Administration (USFDA) in 2012 for the treatment of drug-resistant tuberculosis, which has become a major threat globally. The manuscript presents the development of three liquid chromatography (LC) based analytical methods. The first is a stability indicating RP-HPLC (reverse phase-high performance liquid chromatography) method to analyze the BDQ in presence of its degradation products. Another UPLC/ESI–MS (ultra-performance liquid chromatography/electron spray ionization–mass spectrometry) method was developed for the identification of different degradation based and process related impurities and the third, preparative HPLC method was developed for the isolation of major degradation products. Eleven degradation products and one process related impurity were identified using UPLC/ESI–MS whereas preparative HPLC was used to isolate two degradation products and their chemical structure was elucidated using nuclear magnetic resonance, mass and infra-red spectral data.

Achieving joint calibration of soil Vis-NIR spectra across instruments, soil types and properties by an attention-based spectra encoding-spectra/property decoding architecture

Due to the differences in instruments and measurement methods, soil visible-near infrared (Vis-NIR) spectral data collected worldwide cannot be combined to provide consistent calibration models, reducing the benefit of producing soil spectra libraries. We propose an attention-based spectra encoding-spectra/property decoding model for soil Vis-NIR spectral analysis to solve this problem. By introducing the attention mechanism, the model can use the same parameters to achieve a joint calibration regardless of the length of the input spectral data and the type of soil samples. The model design pays attention to wavelengths combination, which accords to the essence of Vis-NIR analysis. Three datasets, including the LUCAS, Brazilian, and Kenyan datasets, were used to train and test the attention model. An alternative convolution neural network (CNN) encoding architecture that allows joint calibration was also introduced. Comparing with conventional partial least squares regression (PLSR) and CNN encoding model, the present attention model obtained the best results on all three datasets. This is the first time that an attention-based model has been applied to soil Vis-NIR spectra analysis. The model can process spectra data with different sizes and focus on the relation between different wavelengths, providing a joint calibration. It has the potential to become a “one model for all” in soil Vis-NIR spectra research.

Simple and rapid FTIR spectral data and chemometric analysis based method for evaluation of the quality of Indian Sandalwood oil

ABSTRACT The authentication of Indian Sandalwood oil and the detection of adulterants are issues of increasing importance in the sandalwood oil-based industries such as food, perfumery, cosmetics and aromatherapy industries. In this paper, Fourier-Transform Infrared (FT-IR) spectral data combined with Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were used for the authentication of pure and adulterated sandalwood oil. The pure Indian sandalwood oil was extracted by three different methods (i.e. solvent extraction, hydro-distillation and steam distillation) and blended with various proportion of diethyl phthalate (DEP) and diethylhexyl phthalate (DEHP) separately. The pure and adulterated oil samples were evaluated using FTIR-ATR (attenuated total reflection) spectroscopic technique. The results lead to the conclusion that FT-IR spectroscopy with chemometric techniques could be successfully used as a simple, rapid and non-destructive method for the quality assessment of the high-valued Indian Sandalwood oil.

Quantifying acetaldehyde in astronomical ices and laboratory analogues: IR spectra, intensities, 13C shifts, and radiation chemistry.

Acetaldehyde is of interest to astrochemists for its relevance to both interstellar and cometary chemistry, but little infrared (IR) spectral data have been published for the solid phases of this compound. Here we present IR spectra of three forms of solid acetaldehyde, with spectra for one form being published for the first time. Direct measurements of band strengths and absorption coefficients also are reported for the first time for amorphous aldehyde, the form of greatest interest for astrochemical work. An acetaldehyde band strength at ~1350 cm-1 that has been used as a reference for about 20 years is seen to be in error by about 80% when compared to the direct measurements presented here. Spectra and peak positions also are presented for H13C(O)13CH3, and then used for the first identification of ketene as a radiation product of solid acetaldehyde.

Synthesis, molecular docking, dynamic simulations, kinetic mechanism, cytotoxicity evaluation of N-(substituted-phenyl)-4-{(4-[(E)-3-phenyl-2-propenyl]-1-piperazinyl} butanamides as tyrosinase and melanin inhibitors: In vitro, in vivo and in silico approaches.

In the current research work, different N-(substituted-phenyl)-4-{(4-[(E)-3-phenyl-2-propenyl]-1-piperazinyl}butanamides have been synthesized according to the protocol described in scheme 1. The synthesis was initiated by reacting various substituted anilines (1a-e) with 4-chlorobutanoyl chloride (2) in aqueous basic medium to give various electrophiles, 4-chloro-N-(substituted-phenyl)butanamides (3a-e). These electrophiles were then coupled with 1-[(E)-3-phenyl-2-propenyl]piperazine (4) in polar aprotic medium to attain the targeted N-(substituted-phenyl)-4-{(4-[(E)-3-phenyl-2-propenyl]-1-piperazinyl}butanamides (5a-e). The structures of all derivatives were identified and characterized by proton-nuclear magnetic resonance (1H NMR), carbon-nuclear magnetic resonance (13C NMR) and Infra-Red (IR) spectral data along with CHN analysis. The in vitro inhibitory potential of these butanamides was evaluated against Mushroom tyrosinase, whereby all compounds were found to be biologically active. Among them, 5b exhibited highest inhibitory potential with IC50 value of 0.013±0.001µM. The same compound 5b was also assayed through in vivo approach, and it was explored that it significantly reduced the pigments in zebrafish. The in silico studies were also in agreement with aforesaid results. Moreover, these molecules were profiled for their cytotoxicity through hemolytic activity, and it was found that except 5e, all other compounds showed minimal toxicity. The compound 5a also exhibited comparable results. Hence, some of these compounds might be worthy candidates for the formulation and development of depigmentation drugs with minimum side effects.

THE METAL COMPLEXES OF NOVEL SCHIFF BASE CONTAINING ISATIN: CHARACTERIZATION, ANTIMICROBIAL, ANTIOXIDANT AND CATALYTIC ACTIVITY STUDY

Objectives: The aim of our work was to synthesize novel mixed ligand-metal complexes and evaluation of antimicrobial, antioxidant assay, and analysis of catalytic oxidation of cyclohexane.
 Methods: The complexes were characterized by means of various physicochemical techniques such as elemental analysis, molar conductance, magnetic susceptibility, infrared (IR), electronic absorption, 1H NMR (proton magnetic resonance), and mass spectral studies. The antimicrobial screening study was done by disc diffusion method. The catalytic activity of the complexes was observed in the oxidation of cyclohexane using eco-friendly hydrogen peroxide as oxidant.
 Results: On comparing the 1H NMR and IR spectral data of free ligand and its complexes, it was found to be azomethine (CH=N) proton which is formed in the free ligand. During complexation, the azomethine proton is coordinated to the metal ion and the phenolic oxygen is coordinated to the metal ion by deprotonation. The analytical data and mass spectra of the ligand and the complexes confirm the stoichiometry of metal complexes as being of the (MLY)Cl type and the metal to ligand ratio is 1:1. The antimicrobial, antioxidant, and catalytic potential were evaluated and the result shows the better activity of the complexes than the ligand.
 Conclusion: It was found to be copper(II) and zinc(II) complexes which are effective against all the bacteria when compared to standard drug streptomycin. Copper(II) complex was found to be effective antibacterial agent against Aspergillus niger and Aspergillus flavus in comparison to the standard drug Nystatin. The zinc complex exhibited good catalytic activity.