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Dietrich A, Schiemer R, Kurmann J, Zhang S, Hubbuch J. Raman-based PAT for VLP precipitation: systematic data diversification and preprocessing pipeline identification. Front Bioeng Biotechnol 2024; 12:1399938. [PMID: 38882637 PMCID: PMC11177211 DOI: 10.3389/fbioe.2024.1399938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
Virus-like particles (VLPs) are a promising class of biopharmaceuticals for vaccines and targeted delivery. Starting from clarified lysate, VLPs are typically captured by selective precipitation. While VLP precipitation is induced by step-wise or continuous precipitant addition, current monitoring approaches do not support the direct product quantification, and analytical methods usually require various, time-consuming processing and sample preparation steps. Here, the application of Raman spectroscopy combined with chemometric methods may allow the simultaneous quantification of the precipitated VLPs and precipitant owing to its demonstrated advantages in analyzing crude, complex mixtures. In this study, we present a Raman spectroscopy-based Process Analytical Technology (PAT) tool developed on batch and fed-batch precipitation experiments of Hepatitis B core Antigen VLPs. We conducted small-scale precipitation experiments providing a diversified data set with varying precipitation dynamics and backgrounds induced by initial dilution or spiking of clarified Escherichia coli-derived lysates. For the Raman spectroscopy data, various preprocessing operations were systematically combined allowing the identification of a preprocessing pipeline, which proved to effectively eliminate initial lysate composition variations as well as most interferences attributed to precipitates and the precipitant present in solution. The calibrated partial least squares models seamlessly predicted the precipitant concentration with R 2 of 0.98 and 0.97 in batch and fed-batch experiments, respectively, and captured the observed precipitation trends with R 2 of 0.74 and 0.64. Although the resolution of fine differences between experiments was limited due to the observed non-linear relationship between spectral data and the VLP concentration, this study provides a foundation for employing Raman spectroscopy as a PAT sensor for monitoring VLP precipitation processes with the potential to extend its applicability to other phase-behavior dependent processes or molecules.
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Affiliation(s)
- Annabelle Dietrich
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Robin Schiemer
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jasper Kurmann
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Shiqi Zhang
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Jürgen Hubbuch
- Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Wang H, Du Z, Li Y, Zeng F, Qiu X, Li G, Li C. Non-destructive prediction of TVB-N using color-texture features of UV-induced fluorescence image for freeze-thaw treated frozen-whole-round tilapia. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:2574-2586. [PMID: 37851503 DOI: 10.1002/jsfa.13055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/26/2023] [Accepted: 10/18/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND The investigation of UV-induced fluorescence imaging coupled with machine learning was conducted to non-destructively detect the total volatile basic nitrogen (TVB-N) of frozen-whole-round tilapia (FWRT) during freezing and thawing. The UV-induced fluorescence images of FWRT at the wavelength of 365 nm were acquired by self-developed fluorescence image acquisition system. In total, 169 color and texture features based on RGB, hue-saturation-intensity and L*a*b* color spaces and gray level co-occurrence matrix were extracted, respectively. Successive projections algorithm (SPA) was employed to select the optimal 16 features to achieve feature dimension reduction modeling. With full and extracted features as input, the models of partial least squares regression (PLSR), least-squares support vector machine (LSSVM) and convolutional neural network (CNN) were established for TVB-N prediction. RESULTS Results indicated that the full features-based CNN performed better than SPA based prediction models (SPA-PLSR and SPA-LSSVM). The CNN model was determined to be the optimal with an RP2 value of 0.9779, RMSEP value of 1.1502 × 10-2 g N kg-1 and RPD value of 6.721 for TVB-N content predictiin. CONCLUSION The CNN method based on UV fluorescence imaging technology has potential for quality and safety detection of FWRT. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Huihui Wang
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Zhonglin Du
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Yule Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Fanyi Zeng
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Xinjing Qiu
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Gaobin Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
| | - Chunpeng Li
- School of Mechanical Engineering & Automation, Dalian Polytechnic University, Dalian, China
- National Engineering Research Center of Seafood, Dalian Polytechnic University, Dalian, China
- Engineering Research Center of Seafood of Ministry of Education of China, Dalian, China
- Collaborative Innovation Center of Seafood Deep Processing, Dalian, China
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Zhou M, Wang J, Shi J, Zhai G, Zhou X, Ye L, Li L, Hu M, Zhou Y. Prediction model of radiotherapy outcome for Ocular Adnexal Lymphoma using informative features selected by chemometric algorithms. Comput Biol Med 2024; 170:108067. [PMID: 38301513 DOI: 10.1016/j.compbiomed.2024.108067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/28/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Ocular Adnexal Lymphoma (OAL) is a non-Hodgkin's lymphoma that most often appears in the tissues near the eye, and radiotherapy is the currently preferred treatment. There has been a controversy regarding the prognostic factors for systemic failure of OAL radiotherapy, the thorough evaluation prior to receiving radiotherapy is highly recommended to better the patient's prognosis and minimize the likelihood of any adverse effects. PURPOSE To investigate the risk factors that contribute to incomplete remission in OAL radiotherapy and to establish a hybrid model for predicting the radiotherapy outcomes in OAL patients. METHODS A retrospective chart review was performed for 87 consecutive patients with OAL who received radiotherapy between Feb 2011 and August 2022 in our center. Seven image features, derived from MRI sequences, were integrated with 122 clinical features to form comprehensive patient feature sets. Chemometric algorithms were then employed to distill highly informative features from these sets. Based on these refined features, SVM and XGBoost classifiers were performed to classify the effect of radiotherapy. RESULTS The clinical records of from 87 OAL patients (median age: 60 months, IQR: 52-68 months; 62.1% male) treated with radiotherapy were reviewed. Analysis of Lasso (AUC = 0.75, 95% CI: 0.72-0.77) and Random Forest (AUC = 0.67, 95% CI: 0.62-0.70) algorithms revealed four potential features, resulting in an intersection AUC of 0.80 (95% CI: 0.75-0.82). Logistic Regression (AUC = 0.75, 95% CI: 0.72-0.77) identified two features. Furthermore, the integration of chemometric methods such as CARS (AUC = 0.66, 95% CI: 0.62-0.72), UVE (AUC = 0.71, 95% CI: 0.66-0.75), and GA (AUC = 0.65, 95% CI: 0.60-0.69) highlighted six features in total, with an intersection AUC of 0.82 (95% CI: 0.78-0.83). These features included enophthalmos, diplopia, tenderness, elevated ALT count, HBsAg positivity, and CD43 positivity in immunohistochemical tests. CONCLUSION The findings suggest the effectiveness of chemometric algorithms in pinpointing OAL risk factors, and the prediction model we proposed shows promise in helping clinicians identify OAL patients likely to achieve complete remission via radiotherapy. Notably, patients with a history of exophthalmos, diplopia, tenderness, elevated ALT levels, HBsAg positivity, and CD43 positivity are less likely to attain complete remission after radiotherapy. These insights offer more targeted management strategies for OAL patients. The developed model is accessible online at: https://lzz.testop.top/.
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Affiliation(s)
- Min Zhou
- Ophthalmology Department, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai 200011, China.
| | - Jiaqi Wang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China.
| | - Jiahao Shi
- Ophthalmology Department, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai 200011, China.
| | - Guangtao Zhai
- Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
| | - Xiaowen Zhou
- Ophthalmology Department, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai 200011, China.
| | - Lulu Ye
- Department of Oral and Maxillofacial- Head Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China.
| | - Lunhao Li
- Ophthalmology Department, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai 200011, China.
| | - Menghan Hu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China.
| | - Yixiong Zhou
- Ophthalmology Department, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai 200011, China; Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai 200011, China.
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Qi YP, He PJ, Lan DY, Xian HY, Lü F, Zhang H. Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods. WASTE MANAGEMENT (NEW YORK, N.Y.) 2022; 153:20-30. [PMID: 36041267 DOI: 10.1016/j.wasman.2022.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/13/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.
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Affiliation(s)
- Ya-Ping Qi
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Pin-Jing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Dong-Ying Lan
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Hao-Yang Xian
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Shanghai Engineering Research Center of Multi-source Solid Wastes Co-processing and Energy Utilization, Shanghai 200092, China.
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Li K, Zhang C, Du B, Song X, Li Q, Zhang Z. Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline. ACS OMEGA 2022; 7:20291-20297. [PMID: 35721958 PMCID: PMC9202040 DOI: 10.1021/acsomega.2c02282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
Near-infrared (NIR) spectroscopy analysis is one of the most rapid detection methods for determining ethanol content in gasoline. Wavelength selection is a key step in the multivariate calibration analysis of NIR spectroscopy. To improve detection accuracy of ethanol content in gasoline and provide a simpler interpretation, we established NIR spectroscopy, a rapid analysis method based on the effective characteristic spectra. Five effective characteristic spectral bands were used according to the molecular structure of ethanol, followed by the development of four modeling schemes. The four modeling schemes spectra, NIR full spectra, and variable importance projection (VIP) spectra were used for modeling and analysis. The model was established based on the effective characteristic spectra without the interference spectra of aromatic hydrocarbons, achieving the best model performance. In addition, the model was further evaluated by internal cross-validation and external validation. The model's evaluation parameters were as follows: the root mean square error of cross-validation (RMSECV) was 0.6193, the correlation coefficient of internal cross-validation (R CV 2) was 0.9995, the root mean square error of prediction (RMSEP) was 0.5572, and the correlation coefficient of external prediction validation (R P 2) was 0.9995. The effective characteristic spectra model had smaller RMSEP and RMSECV values, and larger R CV 2 and R P 2 values compared to the full spectra and VIP spectra models. In conclusion, the effective characteristic spectra model had the highest accuracy and could provide rapid analysis of the ethanol content in gasoline.
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Affiliation(s)
- Ke Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, P. R. China
| | - Chi Zhang
- Sinochem
Oil Marketing Co., Ltd., Beijing 100069, P. R. China
| | - Biao Du
- Beijing
Yixingyuan Petrochemical Technology Co., Ltd., Beijing 101301, P. R. China
| | - Xiaoping Song
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, P. R. China
| | - Qi Li
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, P. R. China
| | - Zhengdong Zhang
- Center
for Environmental Metrology, National Institute
of Metrology, Beijing 100029, P. R. China
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General model of multi-quality detection for apple from different origins by Vis/NIR transmittance spectroscopy. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01375-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Pauline O, Chang HT, Tsai IL, Lin CH, Chen S, Chuang YK. Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.111524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Huo J, Ma Y, Lu C, Li C, Duan K, Li H. Mahalanobis distance based similarity regression learning of NIRS for quality assurance of tobacco product with different variable selection methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 251:119364. [PMID: 33493932 DOI: 10.1016/j.saa.2020.119364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 12/13/2020] [Accepted: 12/17/2020] [Indexed: 06/12/2023]
Abstract
Quality assurance is one of the key issues in tobacco industry and many efforts have been put on the quality control. This paper introduces a new chemometrics technique to estimate the "quality similarity rate", which is used for quality control. The value of the quality similarity rate represents the similarity degree between the products and the standard reference samples, which is a global parameter that can be generated by either human assessors or machine learning. Supervised similarity regression models are built to automatically estimate the quality similarity rate value from NIRS data of tobacco leaf and smoke. For the similarity regression learning, the metric matrix is generated by a novel method which calculates the Mahalanobis distance from the segmented near infrared spectroscopy (NIRS). The results show the similarity regression learning can predict the quality similarity score well in high speed and can be improved with lasso (least absolute shrinkage and selection operator) related feature selection algorithms such as sRDA (sparse redundancy analysis) and glmnet.
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Affiliation(s)
- Juan Huo
- Zhengzhou University, Henan Province, China.
| | - Yuping Ma
- China Tobacco Henan Industrial Co., Ltd, Zhengzhou 450000, China
| | - Changtong Lu
- China Tobacco Henan Industrial Co., Ltd, Zhengzhou 450000, China
| | - Chenggang Li
- China Tobacco Henan Industrial Co., Ltd, Zhengzhou 450000, China
| | - Kun Duan
- China Tobacco Henan Industrial Co., Ltd, Zhengzhou 450000, China
| | - Huaiqi Li
- China Tobacco Henan Industrial Co., Ltd, Zhengzhou 450000, China.
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Process Analytical Technology for Precipitation Process Integration into Biologics Manufacturing towards Autonomous Operation—mAb Case Study. Processes (Basel) 2021. [DOI: 10.3390/pr9030488] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The integration of real time release testing into an advanced process control (APC) concept in combination with digital twins accelerates the process towards autonomous operation. In order to implement this, on the one hand, measurement technology is required that is capable of measuring relevant process data online, and on the other hand, a suitable model must be available to calculate new process parameters from this data, which are then used for process control. Therefore, the feasibility of online measurement techniques including Raman-spectroscopy, attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR), diode array detector (DAD) and fluorescence is demonstrated within the framework of the process analytical technology (PAT) initiative. The best result is achieved by Raman, which reliably detected mAb concentration (R2 of 0.93) and purity (R2 of 0.85) in real time, followed by DAD. Furthermore, the combination of DAD and Raman has been investigated, which provides a promising extension due to the orthogonal measurement methods and higher process robustness. The combination led to a prediction for concentration with a R2 of 0.90 ± 3.9% and for purity of 0.72 ± 4.9%. These data are used to run simulation studies to show the feasibility of process control with a suitable digital twin within the APC concept.
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Li L, Jin S, Wang Y, Liu Y, Shen S, Li M, Ma Z, Ning J, Zhang Z. Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 247:119096. [PMID: 33166782 DOI: 10.1016/j.saa.2020.119096] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea.
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Affiliation(s)
- Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Shanshan Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Zhiyu Ma
- School of Information & Computer, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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Gao L, Zhong L, Zhang J, Zhang M, Zeng Y, Li L, Zang H. 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. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 244:118854. [PMID: 32920500 DOI: 10.1016/j.saa.2020.118854] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/13/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
- Lele Gao
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Liang Zhong
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Jin Zhang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Mengqi Zhang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Yingzi Zeng
- Shandong Wohua Pharmaceutical Technology Co., Ltd,Weifang 261205, China
| | - Lian Li
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China.
| | - Hengchang Zang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, Shandong, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, China.
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