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

Zhang, Jin; Xu, Xiuhua; Li, Lian; Li, Haoyuan; Gao, Lele; Yuan, Xiaomei; Du, Haochen; Guan, Yongxia; Zang, Hengchang

doi:10.1016/j.saa.2022.121317

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…Based on PCA, the two phases of the extraction process were essentially consistent with that presented in previous published articles, demonstrating the feasibility of using NIR spectroscopy for characterizing the extraction process of herbal medicines was good. 12,28 3.1.2. Development of calibration models 3.1.2.1.…”

Section: Resultsmentioning

confidence: 99%

NIR spectroscopy as a PAT tool for the extraction process of Gegen Qinlian Decoction: end-point determination and fault detection

Zhou

Zhang

et al. 2023

New J. Chem.

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Section: Resultsmentioning

confidence: 99%

NIR spectroscopy as a PAT tool for the extraction process of Gegen Qinlian Decoction: end-point determination and fault detection

Zhou

Zhang

et al. 2023

New J. Chem.

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“…The results showed that the fusion of spectroscopy data with the partial least squares regression (PLSR) algorithm could effectively determine the content of the index compounds and achieve better prediction results, and the fusion technique could provide a more comprehensive and effective quality assessment of Rhizoma Coptidis. Zhang et al [ 73 ] studied the extraction process of Xiao’er Xiaoji Zhike Oral Liquid (XXZOL) by first determining the concentrations of seven key quality attributes during the extraction process, and then establishing low-level and mid-level fusion models based on two types of spectroscopy data in the NIR and MIR with PLS. The results of MATLAB 2019a software showed that the prediction of the data fusion model was better than that of the spectroscopy model for seven key quality attributes, which led to faster, more comprehensive, and more complete detection of information in the extraction process.…”

Section: Application Of Data Fusion Technology In Traditional Chinese...mentioning

confidence: 99%

Application of Data Fusion in Traditional Chinese Medicine: A Review

Huang,

Ma,

Dai

et al. 2023

Sensors

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Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.

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“…The rami cations of imbalanced sample distribution on geographical origin identi cation within P. notoginseng datasets are profound, signi cantly impacting the performance of machine learning models [39][40][41][42][43][44][45][46][47][48][49][50][51][52] . Biased model training in favor of the majority class jeopardizes sensitivity to minority classes, hindering the models' ability to generalize to underrepresented origins.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of data preprocessing methods and machine learning models for geographical origin prediction in an imbalanced Panax notoginseng dataset using near-infrared spectroscopy

Cheng,

Buhari,

Liu

2024

Preprint

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This study explores the application of near-infrared spectroscopy (NIRS) and machine learning to accurately determine the geographical origin of Panax notoginseng (P. notoginseng), a critical component in traditional Chinese medicine. Given the complexity of P. notoginseng geographical origin identification, especially in the face of imbalanced datasets, the study systematically evaluates a range of data preprocessing methods, including autocorrelation, data standardization, Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay (S-G) smoothing, first-order derivative (1D), second-order derivative (2D), and Principal Component Analysis (PCA). Furthermore, it assesses various machine learning models such as Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and neural networks in this context. First by assembling and preparing a substantial dataset of NIRS of P. notoginseng from different geographical locations. The dataset's imbalance, reflective of real-world scenarios, necessitates specialized data handling strategies. The study meticulously applies each preprocessing technique to this dataset, followed by the deployment of different machine learning models. This dual approach allows for an in-depth comparison of how each combination influences the accuracy of geographical origin prediction. Findings of the study reveal that specific combinations of data preprocessing methods and machine learning models yield substantial improvements in predicting the geographical origin of P. notoginseng. These combinations are pivotal in addressing the imbalances inherent in the dataset, thereby enhancing the reliability of the predictions. The research contributes significantly to the field by not only providing a solution to the problem of geographical origin prediction in imbalanced datasets but also by laying down a methodological framework that can be adapted for similar challenges in the broader area of herbal medicine research. This study serves as a cornerstone in the intersection of traditional Chinese medicine and modern scientific methods, offering a robust, data-driven approach to ensuring the authenticity and quality of vital medicinal herbs like P. notoginseng. Its implications extend beyond this specific application, providing insights and methodologies that could revolutionize quality control and authentication processes in herbal medicine globally.

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

Cited by 12 publications

References 40 publications

NIR spectroscopy as a PAT tool for the extraction process of Gegen Qinlian Decoction: end-point determination and fault detection

NIR spectroscopy as a PAT tool for the extraction process of Gegen Qinlian Decoction: end-point determination and fault detection

Application of Data Fusion in Traditional Chinese Medicine: A Review

Comparative analysis of data preprocessing methods and machine learning models for geographical origin prediction in an imbalanced Panax notoginseng dataset using near-infrared spectroscopy

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