Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation.

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

Identification and determination of different processed products and their extracts of Crataegi Fructus by infrared spectroscopy combined with two-dimensional correlation analysis.

The fruit of Crataegus sp. is known as "Shanzha (SZ)" in China and is widely used in the food, beverage, and traditional Chinese medicine (TCM) industries. SZ usually requires thermal processing to reduce the irritation of its acidity to the gastric mucosa. Different processed products of SZ resulting from thermal processing have different or even opposite functions in clinical applications. In addition, 5-hydroxymethylfurfural (5-HMF) intermediates produced during thermal processing are carcinogenic to humans. Therefore, the aim of this study was to explore a rapid and accurate method by Fourier transform infrared spectroscopy (FT-IR) for the identification of different processed products and the determination of 5-HMF in extracts. In qualitative identification, a three-stage infrared spectroscopy identification method (raw spectra, the second derivative spectra, and two-dimensional correlation (2DCOS) spectra) was developed to distinguish different processed products of SZ step by step. In quantitative determination, partial least squares regression combined with different variable selection methods, especially the 2DCOS method, was applied to determine the 5-HMF content. The results show that temperature-induced 2DCOS synchronous spectra can effectively identify different processed products of SZ by shape, intensity, and position of auto-peaks or cross-peaks, and the variables selected by power spectra from concentration-induced 2DCOS synchronous spectra have better prediction ability for 5-HMF compared to full variables. The above results demonstrate that 2D-COS analysis is a potential tool in qualitative and quantitative analysis, which can improve sample identification accuracy and determination capabilities. This study not only establishes a rapid and accurate method for the identification of different processed products but also provides a practical reference for food safety and the efficient use of TCM.

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration.

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Bio-straw or not? Determinants of consumption intention under the plastic straw ban.

A new plastic ban has banned the use of single-use non-degradable plastic drinking straws in China's food and beverage industry by the end of 2020. However, this has caused widespread discussion and complaints on social media. What are consumers' reactions and what factors influence consumers to choose bio-straws (substitutes for plastic straws) are unclear. Therefore, this research collected 4367 effective comments (177,832 words in total) on "bio-straws" from social media and extracted keywords based on grounded theory to generate questionnaires. Structural equation modeling was used to analyze the consumption intention and influencing factors of 348 consumers regarding the ban. The results indicate the following: (1) consumer opinion on straws can be summarized into five main categories, namely, consumers' user experience, consumer subjectivity, policy awareness, policy acceptance, and consumption intention; (2) consumer subjectivity, policy awareness, and policy acceptance directly affect consumption intention significantly, while user experience affects consumption intention indirectly; and (3) user experience and consumer subjectivity play significant roles in mediating these relationships. From the perspective of consumers, this study provides an important basis for policymakers to formulate single-use plastic alternative policies in the future.

Improved Principal Component Analysis (IPCA): A Novel Method for Quantitative Calibration Transfer between Different Near-Infrared Spectrometers.

Given the labor-consuming nature of model establishment, model transfer has become a considerable topic in the study of near-infrared (NIR) spectroscopy. Recently, many new algorithms have been proposed for the model transfer of spectra collected by the same types of instruments under different situations. However, in a practical scenario, we need to deal with model transfer between different types of instruments. To expand model applicability, we must develop a method that could transfer spectra acquired from different types of NIR spectrometers with different wavenumbers or absorbance. Therefore, in our study, we propose a new methodology based on improved principal component analysis (IPCA) for calibration transfer between different types of spectrometers. We adopted three datasets for method evaluation, including public pharmaceutical tablets (dataset 1), corn data (dataset 2), and the spectra of eight batches of samples acquired from the plasma ethanol precipitation process collected by FT-NIR and MicroNIR spectrometers (dataset 3). In the calibration transfer for public datasets, IPCA displayed comparable results with the classical calibration transfer method using piecewise direct standardization (PDS), indicating its obvious ability to transfer spectra collected from the same types of instruments. However, in the calibration transfer for dataset 3, our proposed IPCA method achieved a successful bi-transfer between the spectra acquired from the benchtop and micro-instruments with/without wavelength region selection. Furthermore, our proposed method enabled improvements in prediction ability rather than the degradation of the models built with original micro spectra. Therefore, our proposed method has no limitations on the spectrum for model transfer between different types of NIR instruments, thus allowing a wide application range, which could provide a supporting technology for the practical application of NIR spectroscopy.

An Integrated Strategy to Identify and Quantify the Quality Markers of Xinkeshu Tablets Based on Spectrum-Effect Relationship, Network Pharmacology, Plasma Pharmacochemistry, and Pharmacodynamics of Zebrafish.

Xinkeshu tablets (XKST), a traditional Chinese patent medicine (CPM), have served in the clinical treatment of cardiovascular diseases (CVDs) for decades. However, its pharmacodyamic material basis was still unclear, and the holistic quality control has not been well established due to the lack of systematic research on the quality markers. In this experiment, the heart rate recovery rate of a zebrafish larva was used to evaluate the traditional pharmacological effect of XKST i.e., antiarrhythmic effect. The HPLC fingerprints of 16 batches of XKST samples were obtained, and antiarrhythmic components of XKST were identified by establishing the spectrum-effect relationship between HPLC fingerprints and heart rate recovery rate of zebrafish larva with orthogonal signal correction and partial least squares regression (OSC-PLSR) analysis. The anticardiovascular disease components of XKST were identified by mapping the targets related to CVDs in network pharmacology. The compounds of XKST absorbed and exposed in vivo were identified by ultra-high performance liquid chromatography Q-Exactive high-resolution mass spectrometry (UHPLC-Q-Exactive HRMS). Based on the earlier studies, combined with five principles for identifying quality markers and verified by a zebrafish arrhythmia model, danshensu, salvianolic acid A, salvianolic acid B, daidzein, and puerarin were identified as quality markers of XKST. In total, 16 batches of XKST samples were further quantified with the method established in this study. Our study laid the foundation for the quality control of XKST. The integrated strategy used in the study of XKST could be applied for the identification and quantification of quality markers of other CPMs as well.

Implementation of Dynamic and Static Moisture Control in Fluidized Bed Granulation.

The application of process analysis and control is essential to enhance process understanding and ensure output material quality. The present study focuses on the stability of the feedback control system for a fluidized bed granulation process. Two strategies of dynamic moisture control (DMC) and static moisture control (SMC) were established based on the in-line moisture value obtained from the near-infrared sensor and control algorithm. The performance of these strategies on quality consistency control was examined using process moisture similarity analysis and principal component analysis. The stable moisture control performance and low batch-to-batch variability indicated that the DMC method was significantly better than other granulation methods. In addition, the investigation of robustness further showed that the implemented DMC method was able to produce predetermined target moisture values by varying process parameters. This study provides an advanced and simple control method for fluidized bed granulation quality assurance.

Multi critical quality attributes monitoring of Chinese oral liquid extraction process with a spectral sensor fusion strategy.

The traditional Chinese medicine (TCM) extraction process is a complicated dynamic system with many variables and disturbance. Therefore, multi critical quality attributes (CQAs) monitoring is of great significance to understand the whole process. Spectroscopy is a powerful process analytical tool used for process understanding. However, single senor sometimes could not provide comprehensive information. Sensor fusion is a very practical method to overcome this deficiency. In this study, the extraction process of Xiao'er Xiaoji Zhike Oral Liquid (XXZOL) was carried out in pilot scale, where near infrared (NIR) spectroscopy and mid infrared (MIR) spectroscopy were collected to determine the concentrations of seven CQAs (synephrine, arecoline, chlorogenic acid, forsythoside A, naringin, hesperidin and neohesperidin) during extraction process. Based on fused data blocks, fusion partial least squares (PLS) models were established. Two fusion data blocks are obtained from the concatenation of original spectra (low-level data fusion) and the concatenation of characteristic variables based on band selection (mid-level data fusion) respectively. The results indicated that for all seven analytes, the mid-level data fusion models were superior to the single spectral models, with the prediction performance significantly improved. Specifically, the coefficients of determination (Rp2 and Rt2) of NIR, MIR and fusion quantitative models were all higher than 0.95. The relative standard errors of prediction (RSEP) values were all within 10%, except for models of neohesperidin, which were 10.76%, 12.39%, 12.05%, 10.03% for NIR, MIR, low-level and mid-level models respectively. These results demonstrate that it is feasible to monitor the extraction process of Xiao'er Xiaoji Zhike Oral Liquid more accurately and rapidly by fusing NIR and MIR spectroscopy, and the proposed approach also has vital and valuable reference value for the rapid monitoring of the mixed decoction process of other TCM.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation.

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

Copper-Catalyzed Aldehyde Exchanged Amidation.

The synthesis of bioactive amides has been the pursuit of chemists. Herein secondary amides incorporated with an aldehyde group were first generated using aldehydes and secondary amines. Various (hetero)aryl aldehydes and even aliphatic aldehydes (>40 examples) were converted into the desired products in moderate to excellent yields (up to 89%). A plausible mechanism involving a Cu(I/II/III) catalytic cycle combined with radical rearrangement was proposed and confirmed with four key intermediates detected by high-resolution mass spectrometry.

Water as a probe to understand the traditional Chinese medicine extraction process with near infrared spectroscopy: A case of Danshen (Salvia miltiorrhiza Bge) extraction process.

Extraction process is not only a critical manufacturing unit but also the initial process of various extracts and preparations. Taking the most extensive Chinese herbal medicine Danshen (Salvia miltziorrhiza Bge) as an example, salvianolic acid B (Sal B) is its main active pharmaceutical ingredient but lacks accurate characterization of the extraction process. As one of process analytical technologies, near-infrared spectroscopy (NIRS) technology has been widely applied for monitoring pharmaceutical extraction process. In most past studies, water spectral information is often eliminated due to its high absorption. However, this study proposed a method of using water spectrum to understand the whole extraction process and to quickly determine the content of Sal B. Principal component analysis (PCA) was first utilized to investigate the whole extraction process, then the reconstructed spectrum based on PCA was established and analyzed by Aquaphotomics, and finally the partial least squares regression (PLSR) quantitative model of Sal B was established. PCA and Aquaphotomics results showed the whole extraction process could be considered as a dynamic change from structure breaker to structure maker, and the dominance of highly H-bonded water structures increases with the extraction time. Also, the Sal B quantitative model with water spectrum showed higher accuracy and stability than other methods, which parameters (RMSEC, RMSECV, RMSEP, R2c, R2cv, R2p, RPD) were 0.2408 mg/mL, 0.2939 mg/mL, 0.2584 mg/mL, 0.9536, 0.9300, 0.9494, 4.6298, respectively, and the paired t-test showed that Sal B content measured by NIR and HPLC methods had no significant differences (p > 0.05). In conclusion, all result indicated that water can be used as a probe to understand the traditional Chinese medicine extraction process with NIRS.

Trends-process analytical technology in solid oral dosage manufacturing.

Quality is the lifeline for all pharmaceuticals. Process analytical technology (PAT), introduced by FDA in 2004, providing significant opportunities for improving pharmaceutical development, manufacturing, and quality assurance. Therefore, in this review various PAT tools were introduced for solid oral dosage manufacture quality monitor and control. Throughout this review, we would like to provide more information to both researchers and manufacturers in order to improve the drug quality.

Egr-1 and RNA POL II facilitate glioma cell GDNF transcription induced by histone hyperacetylation in promoter II.

The specific mechanisms for epigenetic regulation of gene transcription remain to be elucidated. We previously demonstrated that hyperacetylation of histone H3K9 in promoter II of glioma cells promotes high transcription of the glial cell line-derived neurotrophic factor (GDNF) gene. This hyperacetylation significantly enhanced Egr-1 binding and increased the recruitment of RNA polymerase II (RNA POL II) to that region (P < 0.05). Egr-1 expression was abnormally increased in C6 glioma cells. Further overexpression of Egr-1 significantly increased Egr-1 binding to GDNF promoter II, while increasing RNA POL II recruitment, thus increasing GDNF transcription (P < 0.01). When the acetylation of H3K9 in the Egr-1 binding site was significantly reduced by the histone acetyltransferase (HAT) inhibitor curcumin, binding of Egr-1 to GDNF promoter II, RNA POL II recruitment, and GDNF mRNA expression were significantly downregulated (P < 0.01). Moreover, curcumin attenuated the effects of Egr-1 overexpression on Egr-1 binding, RNA POL II recruitment, and GDNF transcription (P < 0.01). Egr-1 and RNA POL II co-existed in the nucleus of C6 glioma cells, with overlapping regions, but they were not bound to each other. In conclusion, highly expressed Egr-1 may be involved in the recruitment of RNA POL II in GDNF promoter II in a non-binding manner, and thereby involved in regulating GDNF transcription in high-grade glioma cells. This regulation is dependent on histone hyperacetylation in GDNF promoter II.