Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds

Size-Tunable Band Structure and Optical Properties of Colloidal Silicon Nanocrystals Synthesized via Thermal Disproportionation of Hydrogen Silsesquioxane Polymers

Dodecane-capped silicon nanocrystals (NCs) were synthesized by using a low-temperature (800-1100 °C) polymer variant of traditional hydrogen silsesquioxane thermal disproportionation. Highly crystalline Si NCs having tunable diameters (3.0-6.7 nm) and thus photoluminescence (PL) peaks (1.68-1.29 eV) were attained via changes in the maximum annealing temperature. Modifications in the NC band structure with diameter were explored by comparison of emission with absorption spectra obtained from diffuse reflectance spectroscopy. Large apparent energy shifts between onsets and PL were noted, being significant for smaller NCs (≤∼4.0 nm). This, along with comparatively "softer" onsets, is commensurate with density of states elongation around PL peaks associated with increasing confinement predicted for indirect semiconductor nanostructures. Tauc analyses of absorption additionally revealed three distinguishable optical transitions in all NCs: attributed to indirect Γ25'-Δ1 in lower energy ranges (likely the emission progenitor), indirect Γ25'-L1 overtaken by quasi-direct Γ-X wave function mixing for NC diameters ≤∼4.0 nm within the midenergy regime, and direct Γ25'-Γ15 transitions at energies nearing and above ∼3 eV.

Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy

Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% - 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp2 of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.

Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content

This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroller to collect spectrum data by communicating with the spectrum sensor. The spectrum sensor used is the AS7341 model, but its limited number of spectral acquisition channels and low resolution may limit the exploration and analysis of spectral data. To verify the performance of the device and system, this experiment collected spectral data of Hami melon leaf samples and combined it with a chlorophyll meter for related measurements and analysis. In the experiment, twelve regression algorithms were tested, including linear regression, decision tree, and support vector regression. The results showed that in the original spectral data, the ETR method had the best prediction effect at a wavelength of 515 nm. In the training set, RMSEc was 0.3429, and Rc2 was 0.9905. In the prediction set, RMSEp was 1.5670, and Rp2 was 0.8035. In addition, eight preprocessing methods were used to denoise the original data, but the improvement in prediction accuracy was not significant. To further improve the accuracy of data analysis, principal component analysis and isolation forest algorithm were used to detect and remove outliers in the spectral data. After removing the outliers, the RFR model performed best in predicting all wavelength combinations of denoised spectral data using PBOR. In the training set, RMSEc was 0.8721, and Rc2 was 0.9429. In the prediction set, RMSEp was 1.1810, and Rp2 was 0.8683.

Application of Rapid Identification and Determination of Moisture Content of Coptidis Rhizoma From Different Species Based on Data Fusion

BACKGROUND

For thousands of years, traditional Chinese medicine (TCM) has been clinically proven, and doctors have highly valued the differences in utility between different species.

OBJECTIVE

This study aims to replace the complex methods traditionally used for empirical identification by compensating for the information loss of a single sensor through data fusion. The research object of the study is Coptidis rhizoma (CR).

METHOD

Using spectral optimization and data fusion technology, near infrared (NIR) and mid-infrared (MIR) spectra were collected for CR. PLS-DA (n = 134) and PLSR (n = 63) models were established to identify the medicinal materials and to determine the moisture content in the medicinal materials.

RESULTS

For the identification of the three species of CR, the mid-level fusion model performed better than the single-spectrum model. The sensitivity and specificity of the prediction set coefficients for NIR, MIR, and data fusion qualitative models were all higher than 0.95, with an AUC value of 1. The NIR data model was superior to the MIR data model. The results of low-level fusion were similar to those of the NIR optimization model. The RPD of the test set of NIR and low-level fusion model was 3.6420 and 3.4216, respectively, indicating good prediction ability of the model.

CONCLUSIONS

Data fusion technology using NIR and MIR can be applied to identify CR species and to determine the moisture content of CR. It provides technical support for the rapid determination of moisture content, with a fast analysis speed and without the need for complex pretreatment methods.

HIGHLIGHTS

This study is the first to introduce spectral data fusion technology to identify CR species. Data fusion technology is feasible for multivariable calibration model performance and reduces the cost of manual identification. The moisture content of CR can be quickly evaluated, reducing the difficulty of traditional methods.

TEMPO-Oxidized Cellulose Nanofibril Films Incorporating Graphene Oxide Nanofillers

To design a new system of novel TEMPO-oxidized cellulose nanofibrils (TOCNs)/graphene oxide (GO) composite, 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)-mediated oxidation was utilized. For the better dispersion of GO into the matrix of nanofibrillated cellulose (NFC), a unique process combining high-intensity homogenization and ultrasonication was adopted with varying degrees of oxidation and GO percent loadings (0.4 to 2.0 wt%). Despite the presence of carboxylate groups and GO, the X-ray diffraction test showed that the crystallinity of the bio-nanocomposite was not altered. In contrast, scanning electron microscopy showed a significant morphological difference in their layers. The thermal stability of the TOCN/GO composite shifted to a lower temperature upon oxidation, and dynamic mechanical analysis signified strong intermolecular interactions with the improvement in Young's storage modulus and tensile strength. Fourier transform infrared spectroscopy was employed to observe the hydrogen bonds between GO and the cellulosic polymer matrix. The oxygen permeability of the TOCN/GO composite decreased, while the water vapor permeability was not significantly affected by the reinforcement with GO. Still, oxidation enhanced the barrier properties. Ultimately, the newly fabricated TOCN/GO composite through high-intensity homogenization and ultrasonification can be utilized in a wide range of life science applications, such as the biomaterial, food, packaging, and medical industries.

Comprehensive Metabolomic Fingerprinting Combined with Chemometrics Identifies Species- and Variety-Specific Variation of Medicinal Herbs: An Ocimum Study

Identification of plant species is a crucial process in natural products. Ocimum, often referred to as the queen of herbs, is one of the most versatile and globally used medicinal herbs for various health benefits due to it having a wide variety of pharmacological activities. Despite there being significant global demand for this medicinal herb, rapid and comprehensive metabolomic fingerprinting approaches for species- and variety-specific classification are limited. In this study, metabolomic fingerprinting of five Ocimum species (Ocimum basilicum L., Ocimum sanctum L., Ocimum africanum Lour., Ocimum kilimandscharicum Gurke., and Hybrid Tulsi) and their varieties was performed using LC-MS, GC-MS, and the rapid fingerprinting approach FT-NIR combined with chemometrics. The aim was to distinguish the species- and variety-specific variation with a view toward developing a quality assessment of Ocimum species. Discrimination of species and varieties was achieved using principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA), data-driven soft independent modelling of class analogy (DD-SIMCA), random forest, and K-nearest neighbours with specificity of 98% and sensitivity of 99%. Phenolics and flavonoids were found to be major contributing markers for species-specific variation. The present study established comprehensive metabolomic fingerprinting consisting of rapid screening and confirmatory approaches as a highly efficient means to identify the species and variety of Ocimum, being able to be applied for the quality assessment of other natural medicinal herbs.

Rapid Determination of Geniposide and Baicalin in Lanqin Oral Solution by Near-Infrared Spectroscopy with Chemometric Algorithms during Alcohol Precipitation

The selection of key variables is an important step that improves the prediction performance of a near-infrared (NIR) real-time monitoring system. Combined with chemometrics, NIR spectroscopy was employed to construct high predictive accuracy, interpretable models for the rapid detection of the alcohol precipitation process of Lanqin oral solution (LOS). The variable combination population analysis-iteratively retaining informative variables (VCPA-IRIV) was innovatively introduced into the variable screening process of the model of geniposide and baicalin. Compared with the commonly used synergy interval partial least squares regression, competitive adaptive reweighted sampling, and random frog, VCPA-IRIV achieved the maximum compression of variable space. VCPA-IRIV-partial least squares regression (PLSR) only needs to use about 1% of the number of variables of the original data set to construct models with Rp values greater than 0.95 and RMSEP values less than 10%. With the advantages of simplicity and strong interpretability, the prediction ability of the PLSR models had been significantly improved simultaneously. The VCPA-IRIV-PLSR models met the requirements of rapid quality detection. The real-time detection system can help researchers to understand the quality rules of geniposide and baicalin in the alcohol precipitation process of LOS and provide a reference for the optimization of a LOS quality control system.

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.

Mn5+-activated Ca6Ba(PO4)4O near-infrared phosphor and its application in luminescence thermometry

The near-infrared luminescence of Ca₆Ba(PO₄)₄O:Mn⁵⁺ is demonstrated and explained. When excited into the broad and strong absorption band that spans the 500-1000 nm spectral range, this phosphor provides an ultranarrow (FWHM = 5 nm) emission centered at 1140 nm that originates from a spin-forbidden ¹E → ³A₂ transition with a 37.5% internal quantum efficiency and an excited-state lifetime of about 350 μs. We derived the crystal field and Racah parameters and calculated the appropriate Tanabe-Sugano diagram for this phosphor. We found that ¹E emission quenches due to the thermally-assisted cross-over with the ³T₂ state and that the relatively high Debye temperature of 783 K of Ca₆Ba(PO₄)₄O facilitates efficient emission. Since Ca₆Ba(PO₄)₄O also provides efficient yellow emission of the Eu²⁺ dopant, we calculated and explained its electronic band structure, the partial and total density of states, effective Mulliken charges of all ions, elastic constants, Debye temperature, and vibrational spectra. Finally, we demonstrated the application of phosphor in a luminescence intensity ratio thermometry and obtained a relative sensitivity of 1.92%K^-1 and a temperature resolution of 0.2 K in the range of physiological temperatures.

Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions

Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400-1,000 nm (VIS/NIR) and 900-2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants' total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R 2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm^-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R ²) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g^-1, R ² of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g^-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

Photoluminescent coordination polymer bulk glasses and laser-induced crystallization

Over centimeter-sized luminescent coordination polymer glasses were fabricated. They showed high transparency (over 80%) and strong green emission at room temperature. The glass-to-crystal transformation by laser irradiation was demonstrated.

Simulated NIR spectra as sensitive markers of the structure and interactions in nucleobases

Near-infrared (near-IR; NIR) spectroscopy is continuously advancing in biophysical and biochemical fields of investigation. For instance, recent progresses in NIR hyperspectral imaging of biological systems may be noted. However, interpretation of NIR bands for biological samples is difficult and creates a considerable barrier in exploring the full potential of NIR spectroscopy in bioscience. For this reason, we carried out a systematic study of NIR spectra of adenine, cytosine, guanine, and thymine in polycrystalline state. Interpretation of NIR spectra of these nucleobases was supported by anharmonic vibrational analysis using Deperturbed Vibrational Second-Order Perturbation Theory (DVPT2). A number of molecular models of nucleobases was applied to study the effect of the inter-molecular interactions on the NIR spectra. The accuracy of simulated NIR spectra appears to depend on the intra-layer interactions; in contrast, the inter-layer interactions are less influential. The best results were achieved by combining the simulated spectra of monomers and dimers. It is of particular note that in-plane deformation bands are far more populated than out-of-plane ones and the importance of ring modes is relatively small. This trend is in contrast to that observed in mid-IR region. As shown, the local, short-range chemical neighborhood of nucleobase molecules influence their NIR spectra more considerably. This suggests that NIR spectra are more sensitive probe of the nucleobase pairing than mid-IR ones. The obtained results allow, for the first time, to construct a frequency correlation table for NIR spectra of purines and pyrimidines.