Combining convolutional neural networks and in-line near-infrared spectroscopy for real-time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

Quantitative analysis of moisture content and particle size in a fluidized bed granulation process using near infrared spectroscopy and acoustic emission combined with data fusion strategies

Monitoring granule property is essential for fluidization maintenance and product quality control in fluidized bed granulation (FBG). In this study, two non-invasive techniques, near-infrared (NIR) spectroscopy and acoustic emission (AE), were applied for quantitative analysis of moisture content (MC) and median particle size (D50) in a FBG process, combined with chemometrics and data fusion strategies. Partial least squares (PLS) and support vector machine (SVM) regression models were established based on NIR and AE spectral data. The optimal quantitative models were identified considering the effect of spectra preprocessing and variable selection. In the comparison study, the best separate models for MC and D50 quantification were based on NIR and AE, respectively. The NIR model exhibited the better prediction ability with the determination coefficient of validation set (R2v) of 0.9815, root mean square error of validation set (RMSEv) of 0.2226 %, and residual predictive deviation (RPD) of 7.4674 for MC. Meanwhile, the AE model presented the better prediction performance with R2v of 0.9710, RMSEv of 18.2643 μm, and RPD of 5.9740 for D50. Furthermore, among three data fusion strategies, the high-level fusion model achieved the best overall performance on D50 quantification with R2v of 0.9863, RMSEv of 12.5707 μm, and RPD of 8.6798. The results indicated that both NIR and AE are effective monitoring tools for MC and D50 analysis in fluidized bed granulation process. In addition, a more accurate and reliable analysis of particle size can be achieved by combining NIR and AE technology with high-level data fusion.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction

Aqueous extraction is the most common and cost-effective means of obtaining active ingredients from medicinal plants. However, botanical extracts generally contain high pigment content and complex chemical composition posing a challenge for the process analysis of aqueous extraction. Here, we employed Raman spectroscopy to monitor the physical and chemical properties during the extraction process using convolution neural network (CNN) with background subtraction. Real-time spectra were first preprocessed to eliminate fluorescence background interference. Next, two types of CNN models, the one-dimensional CNN (1D-CNN) based on one preprocessing method, and two-dimensional CNN (2D-CNN) based on a concatenation of differentially pretreated data blocks, were used to receive the preprocessed spectra data. Two case studies were conducted for 1D- and 2D-CNN: the extraction of Aurantii fructus, and the co-extraction of Radix Salvia miltiorrhiza and Rhizoma Ligusticum chuanxiong. Furthermore, partial least squares (PLS) models and sequential preprocessing through orthogonalization (SPORT) models were developed and compared with 1D-CNN and 2D-CNN, respectively. CNN-based methods were superior to other models in terms of prediction accuracy, with 2D-CNN yielding the best results. These results indicated that preprocessing and CNN methods were highly complementary, and could effectively remove the fluorescence effect and artefacts introduced by pretreatment in spectral profile. To the best of our knowledge, this is the first study to demonstrate that a combination of preprocessing and CNN leads to improved prediction performance of analytes when using Raman spectroscopy for online monitoring high-pigmented samples.

Multimodal integrated strategy for the discovery and identification of quality markers in traditional Chinese medicine

With the modernization and internationalization of traditional Chinese medicine (TCM), the requirement for quality control has increased. The quality marker (Q-marker) is an important standard in this field and has been implemented with remarkable success in recent years. However, the establishment of Q-markers remains fragmented and the process lacks systematicity, resulting in inconsistent quality control and insufficient correlation with clinical efficacy and safety of TCM. This review introduces four multimodal integrated approaches that contribute to the discovery of more comprehensive and accurate Q-markers, thus aiding in the establishment of new quality control patterns based on the characteristics and principles of TCM. These include the whole-process quality control strategy, chemical-activity-based screening method, efficacy, safety, and consistent combination strategy, and TCM theory-guided approach. Furthermore, methodologies and representative examples of these strategies are described, and important future directions and questions in this field are also proposed.

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Four multimodal integrated strategies were introduced to establish Q-markers.

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Quality control of TCM should focus on the entire process chain.

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The identification of Q-markers needs to be guided by TCM theory.

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Ensuring efficacy, safety, and consistency is an essential goal of Q-markers.

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Multidisciplinary techniques are the driving force for improving Q-markers.

In-line monitoring and endpoint determination of percolation process of herbal medicine using ultraviolet spectroscopy combined with convolutional neural network

OBJECTIVES

As a common step in the herbal medicine production process, percolation usually lacks effective process monitoring methods and is often conducted with fixed process parameters. In this study, an in-line ultraviolet (UV) spectroscopy was used for monitoring the Caulis Sinomenii percolation process.

METHODS

The spectra and concentration data of 156 percolation samples from five batches were collected. Convolutional neural networks (CNNs) were used to develop quantitative calibration models. The mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) were compared to select the proper loss function for developing the CNN models. Meanwhile, partial least square regression (PLSR) was also used to develop calibration models for performance comparison.

KEY FINDINGS

The CNN models with MAPE or MAE as the loss function could provide accurate predictions for all samples. However, CNN models adopting MSE as the loss function tended not to predict low-concentration samples accurately. The CNN models mostly achieved satisfactory results without any preprocessing techniques and surpassed PLSR models in all the performance metrics.

CONCLUSIONS

An in-line UV spectroscopy system combining the CNN algorithm was implemented to monitor the percolation process of Caulis Sinomenii. The system can accurately determine the endpoint of the percolation process.

Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract

The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production can be regarded as noise parameters. In this study, based on the QbD concept, the ethanol precipitation process of Carthami Flos extract was optimized, considering a noise parameter. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as critical process parameters (CPPs), using a definitive screening design. The mathematical models among CPPs and evaluation indicators were established. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, refrigeration temperature was treated as a noise parameter. A calculation method for the design space in the presence of the noise parameter was proposed. The design space was calculated according to the probability of reaching the standards of evaluation indicators. Controlling parameters within the design space was expected to reduce the influence of noise parameter fluctuations on the quality of the ethanol precipitation supernatant. With more data obtained, the design space was updated. In industry, it is also recommended to adopt a similar idea: that is, continuing to collect industrial data and regularly updating mathematical models, which can further update the design space and make it more stable and reliable.

Research progress on the ethanol precipitation process of traditional Chinese medicine

Ethanol precipitation is a purification process widely used in the purification of Chinese medicine concentrates. This article reviews the research progress on the process mechanism of ethanol precipitation, ethanol precipitation process application for bioactive component purification, ethanol precipitation and traditional Chinese medicine quality, ethanol precipitation equipment, critical parameters, parameter research methods, process modeling and calculation methods, and process monitoring technology. This review proposes that ethanol precipitation technology should be further developed in terms of five aspects, namely, an in-depth study of the mechanism, further study of the effects on traditional Chinese medicine quality, improvement of the quality control of concentrates, development of new process detection methods, and development of a complete intelligent set of equipment.