Mid-infrared and near-infrared spectroscopy for rapid detection of Gardeniae Fructus by a liquid-liquid extraction process

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Multi-criteria decision analysis for the evaluation and screening of sustainable aviation fuel production pathways

The aviation sector, a significant greenhouse gas emitter, must lower its emissions to alleviate the climate change impact. Decarbonization can be achieved by converting low-carbon feedstock to sustainable aviation fuel (SAF). This study reviews SAF production pathways like hydroprocessed esters and fatty acids (HEFA), gasification and Fischer-Tropsch Process (GFT), Alcohol to Jet (ATJ), direct sugar to hydrocarbon (DSHC), and fast pyrolysis (FP). Each pathway's advantages, limitations, cost-effectiveness, and environmental impact are detailed, with reaction pathways, feedstock, and catalyst requirements. A multi-criteria decision framework (MCDS) was used to rank the most promising SAF production pathways. The results show the performance ranking order as HEFA > DSHC > FP > ATJ > GFT, assuming equal weight for all criteria.

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.

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 (R_p² and R_t²) 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.

Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy

Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), one of the best-selling TCM formulations, was established for comprehensive quality evaluation. Near infrared spectral scanning was carried out on 101 batches of commercial LOS under the penetrated vial state and traditional state. RAW 264.7 cells were cultured to detect the anti-inflammatory ability of LOS, and the reference concentrations of epigoitrin, geniposide, and baicalin were obtained by HPLC. The quantitative models were optimized by three kinds of variable selection methods. The correlation coefficients of prediction value of the models were greater than 0.94. The system also passed the external validation. The performance of the non-invasive models was similar to the traditional models. The established non-destructive system can be applied to the rapid quality inspection of LOS to avoid unqualified drugs from entering the market and ensure drug effectiveness. The biological activity index of LOS was introduced and predicted by NIRs for the first time, which provides a new idea about the quality control of TCM formulations.

Rapid monitoring approaches for concentration process of lanqin oral solution by near-infrared spectroscopy and chemometric models

Qualitative and quantitative detection methods based on near-infrared spectroscopy (NIRs) have been proposed in the process analysis of traditional Chinese medicine in recent years. In this study, rapid monitoring methods were developed for quality control of concentration process of lanqin oral solution (LOS). Partial least squares regression (PLSR) method was adopted to construct quantitative models for epigoitrin, geniposide, baicalin, berberine hydrochloride and density. Simultaneously, the genetic algorithm joint extreme learning machine (GA-ELM) was first applied in qualitative analysis of NIRs to distinguish end point of concentration process. Results of PLSR models were satisfactory with the relative standard error of calibration valued at 3.80%, 3.75%, 3.79%, 11.5% and 1.22% for epigoitrin, geniposide, baicalin, berberine hydrochloride and density respectively, and the residual predictive deviation values were higher than 3. For qualitative analysis, the GA-ELM model obtained 100% prediction accuracy. The PLSR quantitative models and the end point discrimination model constructed by GA-ELM correspond with the requirements of practical applications. The results indicate that NIRs in combination with chemometrics has great potential in improving the efficiency in production.

Perspective on Thin Film Waveguides for on-Chip Mid-Infrared Spectroscopy of Liquid Biochemical Analytes

Miniaturized spectrometers offering low cost, low reagent consumption, high throughput, sensitivity and automation are the future of sensing and have significant applications in environmental monitoring, food safety, biotechnology, pharmaceuticals, and healthcare. Midinfrared (MIR) spectroscopy employing complementary metal oxide semiconductor (CMOS) compatible thin film waveguides and microfluidics shows great promise toward highly integrated and robust detection tools and liquid handling. This perspective provides an overview of the emergence of thin film optical waveguides used for evanescent field sensing of liquid chemical and biological samples for MIR absorption spectroscopy. The state of the art of new material and waveguide systems used for spectroscopic measurements in the MIR is presented. An outlook on the advantages and future of waveguide-based MIR spectroscopy for application in clinical settings for point-of-care biochemical analysis is discussed.

LC-electrolyte switch in a contiguous time segments to analyze multi-components: Simultaneous determination of phenolic acids and iridoids in rat plasma after inhalation administration of Reduning aerosol

The optimization of electrolytes, kinds and concentrations, in mobile phase for multiple constituents analyzing using high-performance liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI-MS) was usually compromised to ensure good LC separation of partial components. However, the compromised electrolytes could lead to ionization suppression of some of the analytes. To solve the compromise of electrolytes within various components, taking phenolic acids and iridoids as a case, we used electrolyte switch in contiguous running time segments of UPLC-ESI-MS/MS to ensure chromatographic separation of chlorogenic acid, neochlorogenic acid and cryptochlorogenic acid and improve the response of geniposide. Then the method was applied for pharmacokinetic study of the four components in rat after inhaling Reduning aerosol for the first time. The complete separation of the three chlorogenic acid isomers was achieved and the LLOQs of neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, and geniposide were 1, 1, 3, and 0.2 ng/mL, respectively. In conclusion, we developed a sensitive and time-saving LC-MS/MS method for the quantitative analysis of chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, and geniposide in rat plasma, and this method appears to be useful for pharmacokinetic studies of Reduning aerosol. The method provided a sight to alleviate compromise of electrolytes in mobile phase for HPLC-ESI-MS in analyzing multi-components.

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of r_p² (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Systematically Characterizing Chemical Profile and Potential Mechanisms of Qingre Lidan Decoction Acting on Cholelithiasis by Integrating UHPLC-QTOF-MS and Network Target Analysis

Qingre Lidan Decoction (QRLDD), a classic precompounded prescription, is widely used as an effective treatment for cholelithiasis clinically. However, its chemical profile and mechanism have not been characterized and elucidated. In the present study, a rapid, sensitive, and reliable ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry method was established for comprehensively identifying the major constituents in QRLDD. Furthermore, a network pharmacology strategy based on the chemical profile was applied to clarify the synergetic mechanism. A total of 72 compounds containing flavonoids, terpenes, phenolic acid, anthraquinones, phenethylalchohol glycosides, and other miscellaneous compounds were identified, respectively. 410 disease genes, 432 compound targets, and 71 related pathways based on cholelithiasis-related and compound-related targets databases as well as related pathways predicted by the Kyoto Encyclopedia of Genes and Genomes database were achieved. Among these pathways and genes, pathway in cancer and MAPK signaling pathway may play an important role in the development of cholelithiasis. EGFR may be a crucial target in the conversion of gallstones to gallbladder carcinoma. Regulation of PRKCB/RAF1/MAP2K1/MAPK1 is associated with cell proliferation and differentiation. Thus, the fingerprint coupled with network pharmacology analysis could contribute to simplifying the complex system and providing directions for further research of QRLDD.