Spectral Data Research Articles

A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer.

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40keV, 70keV, 100keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics featuresselected were used to generate a composedradiomics signature (Com-RS). A nomogram was developed using age, CEA, and theCom-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

N-Guanylurea dinitramide: Optimal synthesis process, basic properties and spectroscopic data

N-Guanylurea dinitramide (GUDN), also known as FOX-12, is indeed a fascinating organic dinitramide salt with high insensitivity and good thermal stability. It shows great promise for use in next-generation insensitive munitions. In this study, GUDN was synthesized through the nitration reaction of ammonium sulfamate salt with an acid mixture (HNO3/H2SO4) and guanylurea salts in water. The optimal conditions for this synthesis have been determined, including reaction temperature at -40 oC, the nitration reaction time of 45 minutes, and specific molar ratios of the components, which include guanylurea sulfate, ammonium sulfamate, and the mixture of sulfuric and nitric acid. Additionally, various properties and spectral data of GUDN, such as FTIR, 1H-NMR, 13C-NMR, DSC-TG, XRD, and UV-Vis, have been thoroughly studied in the referenced article.

Convenient synthesis and antibacterial evaluation of new bipyrazolyl and pyrazolopyridazine derivatives utilizing 4‐acetyl‐5‐methyl‐N‐phenyl‐1‐(p‐tolyl)‐1H‐pyrazole‐3‐carboxamide as a versatile precursor

Abstract4‐(3‐(Dimethylamino)acryloyl)‐5‐methyl‐N‐phenyl‐1‐(p‐tolyl)‐1H‐pyrazole‐3‐carboxamide was prepared via refluxing of 4‐acetyl‐5‐methyl‐N‐phenyl‐1‐(p‐tolyl)‐1H‐pyrazole‐3‐carboxamide with dimethylformamide dimethyl acetal. The target bipyrazoles were obtained from regioselective reaction of the latter enaminone with different hydrazonoyl halides at reflux in chloroform in the presence of trimethylamine. The regioselectivity was confirmed chemically upon refluxing with hydrazine hydrate in ethanol. The structures of the newly synthesized compounds were established on the basis of their elemental analyses and spectral data. The specified compounds were tested for antibacterial activity against gram‐negative bacteria (Escherichia coli) and (Pseudomonas aeruginosa) and a gram‐positive bacteria (Bacillus subtilis) and (Staphylococcus aureus) using disk diffusion method. Antibacterial studies revealed that compounds 8a, 10b, 10c, 11d, and 14b showed weak to moderate activity against the tested bacteria compared to ampicillin.

Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

Synthesis and Biological Evaluation of 3-(2-Benzoyl-4-chlorophenyl)-4H-spiro [indole-3,2-[1,3]thiazolidine]-2,4(1H)- dione Derivatives for Anti-tubercular and Antimicrobial Activities

Objectives: To synthesize a new scaff fold of 3'-(2-Benzoyl-4-chlorophenyl)-4'H-spiro [indole-3,2'-[1,3] thiazolidine]-2,4'(1H)-dione derivatives and to assess the compounds for anti-tubercular and antimicrobial activities. Methods : A new series of 3'-(2-Benzoyl-4-chlorophenyl)-4'H-spiro [indole-3,2'- [1,3] thiazolidine]-2,4'(1H)-dione derivatives were prepared by synthesizing schiff’s bases followed by condensing schiff base with 2-mercaptopropanoic acid in the presence of zinc chloride. Synthesized compounds were characterized by IR, NMR and Mass spectral data. Their antimicrobial, antitubercular and also docking simulations were evaluated. Findings : Compound 3c (floro substituent) showed potent activity against Mycobacterium tuberculosis H37Rv with MIC value of 3.125 µg/ml when compared with standard Isoniazid. Compound 3c, 3d and 3f displayed better antimicrobial activity against used bacterial and fungal species with MIC range of 3-20µg/ml comparable to standard drugs ciprofloxacin and ketoconazole respectively. In silico studies revealed that compound 3c, 3f and 3d had a better binding affinity with M. tuberculosis enoyl-CoA hydratase EchA6 with ΔG values of -9.6kcal/mol, -9.4kcal/mol, and -8.6kcal/mol respectively comparable to isoniazid of -5.4kcal/mol. Compound 3c, 3f and 3d also displayed good binding affinity with DNA gyrase and lanosterol methylase. Novelty : Introducing benzoyl moiety to the thiazolidine ring potentiates the antitubercular and antimicrobial activity of spiro indole derivatives. Keywords: AntiTB, Antimicrobial, Molecular Docking, Spiro indole, Thiazolidine

Discrimination of New and Aged Seeds Based on On-Line Near-Infrared Spectroscopy Technology Combined with Machine Learning

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

SSAformer: Spatial–Spectral Aggregation Transformer for Hyperspectral Image Super-Resolution

SSAformer: Spatial–Spectral Aggregation Transformer for Hyperspectral Image Super-Resolution

High-precision spectra captured by a spectral camera and suppression of their nonlinearity

The high sensitivity of photoplethysmography (PPG) spectral signals provides conditions for extracting dynamic spectra carrying nonlinear information. By the idea of spatial conversion precision, this paper uses a spectral camera to collect highly sensitive spectral data of 24 wavelengths and proposes a method for extracting dynamic spectra of three different optical path lengths and their joint modeling. In the experiment, the models of the red blood cells and white blood cells established by the joint spectra achieved good results, with the correlation coefficients above 0.77. This study has great significance for achieving high-precision noninvasive quantitative analysis of human blood components.

Synthesis, Binding and Docking Studies of Indole‐1,3,4‐Thiadiazole Derivatives As Potent Α‐Amylase and Lox Inhibitors

AbstractSeries of indole‐1,3,4‐thiadiazole compounds (IT 1–7) were synthesized and characterized by FT‐IR, 1H NMR, 13C NMR and ESI‐MS spectral data. Further, the compounds (IT 1–7) were screened for antidiabetic, antioxidant and anti‐inflammatory activities. Most of these compounds exhibited moderate to good antidiabetic, antioxidant and anti‐inflammatory activities against α‐amylase enzyme, ABTS and lipoxygenase enzyme, respectively. Among these, IT‐1 displayed the better activity compared to that of standard drugs. The mechanism of binding of IT‐1, IT‐5 and IT‐6 with a transporter protein, human serum albumin was investigated by fluorescence spectroscopic and circular dichroism studies. These compounds quenched the intrinsic fluorescence intensity of HSA and moderate binding was evident from the values of binding constants which are in the order of 105 M−1. The molecular docking studies of the compounds were performed against human pancreatic alpha‐amylase in complex with montbretin A (PDB ID: 4W93). The studied compounds interacted with amino acid residues (ASP197, GLU240 and TYR151) which were similar to that of the reference drug, acarbose. The ADME properties of designed compounds were studied using SwissADME software. All the compounds obeyed Lipinski's rule of five. All the compounds showed low or non‐toxicity towards different toxicity classes as analyzed using ProTox‐II online tool.

Countering Calcium Blooming With Personalized Contrast Media Injection Protocols: The 1-2-3 Rule for Photon-Counting Detector CCTA.

Photon-counting detector computed tomography (PCD-CT) enables spectral data acquisition of CT angiographies allowing for reconstruction of virtual monoenergetic images (VMIs) in routine practice. Specifically, it has potential to reduce the blooming artifacts associated with densely calcified plaques. However, calcium blooming and iodine attenuation are inversely affected by energy level (keV) of the VMIs, creating a challenge for contrast media (CM) injection protocol optimization. A pragmatic and simple rule for calcium-dependent CM injection protocols is investigated and proposed for VMI-based coronary CT angiography with PCD-CT. A physiological circulation phantom with coronary vessels including calcified lesions (maximum CT value >700 HU) with a 50% diameter stenosis was injected into at iodine delivery rates (IDRs) of 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 2.5, and 3.0 g I/s. Images were acquired using a first-generation dual-source PCD-CT and reconstructed at various VMI levels (between 45 and 190 keV). Iodine attenuation in the coronaries was measured at each IDR for each keV, and blooming artifacts from the calcified lesions were assessed including stenosis grading error (as % overestimation vs true lumen). The IDR to achieve 300 HU at each VMI level was then calculated and compared with stenosis grading accuracy to establish a general rule for CM injection protocols. Plaque blooming artifacts and intraluminal iodine attenuation decreased with increasing keV. Fixed windowing (representing absolute worst case) resulted in stenosis overestimation from 77% ± 4% at 45 keV to 5% ± 2% at 190 keV, whereas optimized windowing resulted in overestimation from 29% ± 3% at 45 keV to 4% ± 1% at 190 keV. The required IDR to achieve 300 HU showed a strong linear correlation to VMI energy (R2 = 0.98). Comparison of this linear plot versus stenosis grading error and blooming artifact demonstrated that multipliers of 1, 2, and 3 times the reference IDR for theoretical clinical regimes of no, moderate, and severe calcification density, respectively, can be proposed as a general rule. This study provides a proof-of-concept in an anthropomorphic phantom for a simple pragmatic adaptation of CM injection protocols in coronary CT angiography with PCD-CT. The 1-2-3 rule demonstrates the potential for reducing the effects of calcium blooming artifacts on overall image quality.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas: A Case Study of Songnen Plain, Northeast China

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas: A Case Study of Songnen Plain, Northeast China

Dibutyltin(IV) Formulations as Potential Antitubercular Scaffold: DFT Assisted Emerging Advances and Mechanism of Antitubercular Action

Abstract The interplay between N, O-orthometallated, O, O′-chelated and dibutyltin(IV) groups in biopotent hybrid formulations to combat tuberculosis is deciphered. The present study emphasized on the design, generation, spectroscopic as well as DFT assisted structural characterization, understanding structure-antitubercular activity relationship and mode of action of some dibutyltin(IV) formulations against Mycobacterium tuberculosis as possible antituberculosis agents. Dibutyltin(IV) formulations of the general formula Bu2SnLL′ (where LH = heterocyclic/non-fluorinated/fluorinated β-diketones and L′H = aminophenol/substituted aminophenols) have been generated by the reaction of Bu2SnCl2 with sodium salts of the ligands in 1:1:1 molar ratio. The newly generated dibutyltin(IV) formulations have been characterized with the assistance of spectroscopic techniques and mass studies. Computational calculations of some representative complexes (1 and 2) also supported the obtained spectral data. Antituberculosis activity analysis was also performed on some representative dibutyltin(IV) complexes (1, 4 and 8). Out of these, complex 4 demonstrated highest activity against Mycobacterium tuberculosis. The structure-antitubercular activity relationship and mechanism of action of the active complexes against Mycobacterium tuberculosis have also been investigated. Intercluster electron hopping mechanism of mode of action has also been suggested.

Synergistic exploration of antimicrobial potency, cytotoxicity, and molecular mechanisms: A tripartite investigation integrating in vitro, in vivo, and in silico approaches for pyrimidine‐based metal (II) complexes

We synthesized and characterized bioactive metal (II) complexes from a pyrimidine‐based Schiff base ligand, 4‐[2‐(4‐chlorobenzylidene) hydrazinyl]‐7H‐pyrrolo[2,3‐d]pyrimidine. Through thorough elemental analysis and various physicochemical techniques, we characterized both the ligand and its Zn(II), Cd(II), and Hg(II) complexes. Coordination of the metallic ion was established via nitrogen atoms from the ligand's pyrrolo and pyrimidine rings, yielding tetrahedral complexes of the type [M(PPHpCB)2]. These complexes are identified as non‐electrolytes due to their low molar conductance. Furthermore, we investigated the coordination behavior of the pyrimidine‐based Schiff base ligand (HPPHpCB) with metal ions through comprehensive spectroscopic analysis. 1H NMR spectral data showed bonding between HPPHpCB and metal ions, while UV spectra confirmed intra‐ligand and ligand‐to‐metal charge transfer transitions, indicating coordination through nitrogen atoms. The determination of tetrahedral geometry for metal complexes confirmed their diamagnetic properties. Analysis using 13C NMR and electrospray ionization mass spectra (ESI‐MS) revealed variations in chemical shifts, confirming successful complex formation and elucidating structural modifications, electronic interactions, and metal–ligand coordination modes. Moreover, evaluation of in vitro antimicrobial activities using the minimum inhibitory concentration (MIC) approach revealed significant antibacterial and antifungal activities for Hg(II) and Cd(II) complexes, respectively. The Hg(II) complex displayed superior antioxidant activity, while Zn(II) and Cd(II) complexes also demonstrated considerable potential. In vivo, cytotoxicity assessments against Artemia salina provided insights into the cytotoxic properties of ligands and metal complexes. Computational techniques suggested their potential in combating infectious ailments. Overall, our study establishes the synthesis, characterization, and multifaceted biological activities of metal (II) complexes derived from the pyrimidine‐based Schiff base ligand, highlighting their promising role in combating various infectious diseases.

Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data.

Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of N = 30 yeast isolate samples representing 5 yeast strains (K-97, S-33, T-58, US-05, WB-06), high-resolution LC-MS-Orbitrap data were collected. Reference spectra were then generated for each strain from the combined data of each sample of that strain. Sample strains were then predicted by computing the 2D cross-correlation of each sample against the reference spectra, followed by application of correction factors measuring the asymmetry of the 2D correlation functions.

Tackling unbalanced datasets for yellow and brown rust detection in wheat

This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing machine learning models and the SMOTE (Synthetic Minority Oversampling Technique) augmentation technique to tackle unbalanced datasets. Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (GNB) models were assessed. Overall, SVM and RF models showed higher accuracies, particularly when utilizing SMOTE-enhanced datasets. The RF model achieved 70% accuracy in detecting yellow rust without data alteration. Conversely, for brown rust, the SVM model outperformed others, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection. It emphasizes the need for further research in data processing methodologies, particularly in exploring the impact of techniques like SMOTE on model performance.

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Efficient identification of broad absorption line quasars using dimensionality reduction and machine learning

Raman and Mid-Infrared Spectroscopy Coupled With Machine–Deep Learning for Adulterant Detection in Ground Turmeric

The intricate nature of the global food supply chain and the presence of regulations spanning multiple jurisdictions contribute to an increased likelihood of food adulteration. This underscores the need for effective monitoring methods to guarantee the safety and nutritional quality of our food. In this context, the application of infrared spectroscopy-based techniques emerges as an environmentally friendly, non-invasive, and waste-minimizing solution for authenticating food products. Infrared spectra serve as unique molecular fingerprints, offering a multidimensional representation of how chemical bonds in the material interact with infrared light. Chemometrics, which are primarily linear-based models, play a crucial role in extracting essential information from spectral data, enabling dimensionality reduction, classification, and predictive analysis. Recent progress in the field of big data science and artificial intelligence has brought forth machine learning and deep learning algorithms explicitly designed to uncover features from complex multidimensional data, encompassing both linear and nonlinear relationships. These advancements have the potential to enhance the detection of adulterants in food products. This study assesses the accuracy of various shallow machine learning models and a deep learning model based on a one-dimensional convolutional neural network (1D CNN). The evaluation is conducted using Raman and infrared spectral data obtained from ground turmeric samples that were deliberately adulterated with five distinct substances. The study highlights the improved classification accuracy achieved through the implementation of the 1D CNN model.

Spectral studies of thermally stable Dy3+/Sm3+ co-doped Li2Ba5W3O15 phosphors for warm white LEDs

In the current study, a series of Li2Ba5W3O15: RE3+ (RE = Dy, Sm) [LBW:Dy3+/Sm3+] phosphors were prepared using a high-temperature solid-state method. X-ray diffraction, Scanning electron microscopy with energy-dispersive x-ray analysis scans showed that the crystal form was consistent with the standard LBW and comprised small irregularly shaped particles. Diffuse reflectance spectral (DRS) data was utilized to calculate the band gaps. Fluorescence study shows that LBW material doped with Dy3+ and Sm3+ yield distinct colors at 496 nm (blue) for Dy3+ and 582 nm (green-yellow), 612 nm (yellow), and 669 nm (red) for Sm3+ when excited by near-ultraviolet (336 nm) light. The observation of energy transfer between Dy3+ and Sm3+ ions play a role in modifying the luminescence of LBW:Dy3+/Sm3+ co-doped phosphors. With a constant excitation wavelength (λ Ex), different levels of activator doping lead to a change in the emission colors from their neutral white light to a deep orange-red region for LBW:Dy3+/Sm3+ phosphors. The decay curves demonstrate a decrease in lifetime with an increase in the concentration of activator ions (Sm3+). For D3S5 phosphor, the temperature-dependent photoluminescence characteristics were analyzed under λ Ex = 336 nm excitation. The results indicate excellent luminescence thermal stability with an activation energy of 0.16 eV at λ Ex = 336 nm. With its low color-correlated temperature and good thermal stability, the prepared phosphor sample shows potential as a solid-state emitting phosphor that can be used with UV chip stimulation for warm white LED applications.