Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools.

In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and ß-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and ß-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and ß-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.

Evolution of Microstructure and Mechanical Properties of Ti-6Al-4V Alloy under Heat Treatment and Multi-Axial Forging.

The mechanical properties of various Ti-6Al-4V alloys are influenced by their respective microstructures. This study generated an ultrafine-grain (UFG) Ti-6Al-4V alloy featuring bimodal grain distribution characteristics achieved through initial heat treatment, multi-axial forging (MF), and annealing. The study also extensively examined the evolution process of the alloy's microstructure. By subjecting the materials to heat treatments at 900 °C with air cooling and 950 °C with air cooling, both materials were found to be consisted of primary α (αp) and transformed ß (αs+ß) regions with different proportions. Following MF, the sample treated at 900 °C displays a microstructure featuring UFGs of α+ß surrounding larger micron-sized αp grains. On the other hand, the sample treated at 950 °C displays a microstructure distinguished by twisted αs lamellar and fragmented ß grains surrounding larger micron-sized αp grains. Following annealing, no significant grain growth was observed in the sample. The geometrically necessary dislocations (GNDs) within the UFGs were eliminated, though some GNDs persisted within the αp grains. The samples undergoing the 900 °C heat treatment, MF, and subsequent annealing exhibited elevated strength (1280 MPa) and total elongation (10.7%). This investigation introduces a novel method for designing the microstructure of the Ti-6Al-4V alloy to achieve superior performance.

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

INTRODUCTION: Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. OBJECTIVE: The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. MATERIALS AND METHODS: A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. RESULTS: The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. CONCLUSION: This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.