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Hao N, Ping J, Wang X, Sha X, Wang Y, Miao P, Liu C, Li W. Data fusion of near-infrared and mid-infrared spectroscopy for rapid origin identification and quality evaluation of Lonicerae japonicae flos. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 320:124590. [PMID: 38850827 DOI: 10.1016/j.saa.2024.124590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/09/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
A data fusion strategy based on near-infrared (NIR) and mid-infrared (MIR) spectroscopy techniques were developed for rapid origin identification and quality evaluation of Lonicerae japonicae flos (LJF). A high-level data fusion for origin identification was formed using the soft voting method. This data fusion model achieved accuracy, log-loss value and Kappa value of 95.5%, 0.347 and 0.910 on the prediction set. The spectral data were converted to liquid chromatography data using a data fusion model constructed by the weighted average algorithm. The Euclidean distance and adjusted cosine similarity were used to evaluate the similarity between the converted and the real chromatographic data, with results of 247.990 and 0.996, respectively. The data fusion models all performed better than the models constructed using single data. This indicates that multispectral data fusion techniques have a wide range of application prospects and practical value in the quality control of natural products such as LJF.
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Affiliation(s)
- Nan Hao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Jiacong Ping
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Xi Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China
| | - Xin Sha
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
| | - Yanshuai Wang
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Peiqi Miao
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Changqing Liu
- National and Local Joint Innovation Center for Modern Chinese Medicine, Tianjin 300392, China; Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, China.
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Zhong L, Huang R, Gao L, Yue J, Zhao B, Nie L, Li L, Wu A, Zhang K, Meng Z, Cao G, Zhang H, Zang H. A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration. Molecules 2023; 28:5672. [PMID: 37570642 PMCID: PMC10419756 DOI: 10.3390/molecules28155672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
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.
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Affiliation(s)
- Liang Zhong
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Ruiqi Huang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lele Gao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Jianan Yue
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Bing Zhao
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lei Nie
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Lian Li
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Aoli Wu
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Kefan Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
| | - Zhaoqing Meng
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China; (Z.M.); (G.C.)
| | - Guiyun Cao
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China; (Z.M.); (G.C.)
| | - Hui Zhang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
- National Glycoengineering Research Center, Shandong University, Jinan 250012, China
| | - Hengchang Zang
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; (L.Z.); (R.H.); (L.G.); (J.Y.); (B.Z.); (L.N.); (L.L.); (A.W.); (K.Z.)
- National Glycoengineering Research Center, Shandong University, Jinan 250012, China
- Key Laboratory of Chemical Biology, Ministry of Education, Shandong University, Jinan 250012, China
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Zhang M, Zhao B, Li L, Nie L, Li P, Sun J, Wu A, Zang H. A rapid extraction process monitoring of Swertia mussotii Franch. With near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 295:122609. [PMID: 36921517 DOI: 10.1016/j.saa.2023.122609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/05/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Swertia mussotii Franch. (SMF), a traditional Tibetan medicine, which has miraculous effect on treating hepatitis diseases. However, there is no research on its entire production process, and invisible production process has seriously hindered the SMF modern development. In this study, principal component analysis (PCA), subtractive spectroscopy, and two-dimensional correlation spectroscopy (2D-COS) were used to explain changes of characteristic groups in the extraction process. Four main characteristic peaks at 1884 nm, 1944 nm, 2246 nm and 2308 nm were identified to describe the changes of molecular structure information of total active components in SMF extraction process. In addition, multi critical quality attributes (CQAs) models were established by near-infrared spectroscopy (NIRS) combined with the total quantum statistical moment (TQSM). The coefficients of determination (R2eval and R2ival) were both greater than 0.99. The ratios of the standard deviation of validation to the standard error of the prediction (RPDe and RPDi) were greater than five. The quantitative model of AUCT could save time on primary data measurement by not requiring determination of indicator components compared with others. In conclusion, these results demonstrated that it was feasible to understand the SMF extraction process through AUCT and characteristic groups. These could realize the visual digital characterization and quality stability of the SMF extraction process.
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Affiliation(s)
- Mengqi Zhang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Bing Zhao
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Lian Li
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Lei Nie
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Peipei Li
- Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China
| | - Jing Sun
- Qinghai Provincial Key Laboratory of Qinghai-Tibet Plateau Biological Resources, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China
| | - Aoli Wu
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Hengchang Zang
- National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; National Glycoengineering Research Center, Shandong University, Jinan, Shandong, 250012, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan, Shandong, 250012, China.
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Xia Z, Che X, Ye L, Zhao N, Guo D, Peng Y, Lin Y, Liu X. A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis. Molecules 2023; 28:molecules28041778. [PMID: 36838765 PMCID: PMC9965801 DOI: 10.3390/molecules28041778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.
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Affiliation(s)
- Ziyi Xia
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Xiaoqing Che
- Shandong Runzhong Pharmaceutical Co., Ltd., Yantai 256603, China
| | - Lei Ye
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Na Zhao
- Key Laboratory of Xinjiang Phytomedicine Resources and Utilization in Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832002, China
| | - Dongxiao Guo
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
| | - Yanfang Peng
- Pharmacy Faculty, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Yongqiang Lin
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
- Correspondence: (Y.L.); (X.L.)
| | - Xiaona Liu
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
- Correspondence: (Y.L.); (X.L.)
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Bian X, Ling M, Chu Y, Liu P, Tan X. Spectral denoising based on Hilbert–Huang transform combined with F-test. Front Chem 2022; 10:949461. [PMID: 36110141 PMCID: PMC9469774 DOI: 10.3389/fchem.2022.949461] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert–Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky–Golay (SG) smoothing.
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Affiliation(s)
- Xihui Bian
- Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, China
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Sichuan, China
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, China
- *Correspondence: Xihui Bian,
| | - Mengxuan Ling
- Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, China
- Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Sichuan, China
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, China
| | - Yuanyuan Chu
- Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, China
| | - Peng Liu
- Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, China
| | - Xiaoyao Tan
- Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, China
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Tian Z, Tan Z, Li Y, Yang Z. Rapid monitoring of flavonoid content in sweet tea (Lithocarpus litseifolius (Hance) Chun) leaves using NIR spectroscopy. PLANT METHODS 2022; 18:44. [PMID: 35366929 PMCID: PMC8977023 DOI: 10.1186/s13007-022-00878-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Sweet tea, which functions as tea, sugar and medicine, was listed as a new food resource in 2017. Flavonoids are the main medicinal components in sweet tea and have significant pharmacological activities. Therefore, the quality of sweet tea is related to the content of flavonoids. Flavonoid content in plants is normally determined by time-consuming and expensive chemical analyses. The aim of this study was to develop a methodology to measure three constituents of flavonoids, namely, total flavonoids, phloridin and trilobatin, in sweet tea leaves using near-infrared spectroscopy (NIR). RESULTS In this study, we demonstrated that the combination of principal component analysis (PCA) and NIR spectroscopy can distinguish sweet tea from different locations. In addition, different spectral preprocessing methods are used to establish partial least squares (PLS) models between spectral information and the content of the three constituents. The best total flavonoid prediction model was obtained with NIR spectra preprocessed with Savitzky-Golay combined with second derivatives (SG + D2) (RP2 = 0.893, and RMSEP = 0.131). For trilobatin, the model with the best performance was developed with raw NIR spectra (RP2 = 0.902, and RMSEP = 2.993), and for phloridin, the best model was obtained with NIR spectra preprocessed with standard normal variate (SNV) (RP2 = 0.818, and RMSEP = 1.085). The coefficients of determination for all calibration sets, validation sets and prediction sets of the best PLS models were higher than 0.967, 0.858 and 0.818, respectively. CONCLUSIONS The conclusion indicated that NIR spectroscopy has the ability to determine the flavonoid content of sweet tea quickly and conveniently.
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Affiliation(s)
- Zhaoxia Tian
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang Province, China
- College of Forestry, Nanjing Forestry University, Nanjing, People's Republic of China
| | - Zifeng Tan
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang Province, China
| | - Yanjie Li
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang Province, China
| | - Zhiling Yang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou, 311400, Zhejiang Province, China.
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Dong X, Yan X, Qu H. Advanced process control for salvianolic acid A conversion reaction based on data-driven and mechanism-driven model. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Yang H, Bao L, Liu Y, Luo S, Zhao F, Chen G, Liu F. Identification and quantitative analysis of salt-adulterated honeysuckle using infrared spectroscopy coupled with multi-chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract. SEPARATIONS 2021. [DOI: 10.3390/separations8060074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
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.
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Li P, Zhang X, Zheng Y, Yang F, Jiang L, Liu X, Ding S, Shan Y. A novel method for the nondestructive classification of different-age Citri Reticulatae Pericarpium based on data combination technique. Food Sci Nutr 2021; 9:943-951. [PMID: 33598177 PMCID: PMC7866605 DOI: 10.1002/fsn3.2059] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 11/13/2022] Open
Abstract
The quality of Citri Reticulatae Pericarpium (CRP) is closely correlated with the aging time. However, CRPs in different storage ages are similar in appearance, and the young CRP may be labeled as the aged one to obtain the excess profit by some unscrupulous traders. Most traditional analysis methods are laborious and time-consuming, and they can hardly realize the nondestructive classification. In this paper, a novel method based on near-infrared diffuse reflectance spectroscopy (NIRDRS) and data combination technique for the nondestructive classification of different-age CRPs was proposed. The CRPs in different storage ages (5, 10, 15, 20, and 25 years) were measured. The near-infrared spectra of outer skin and inner capsule were obtained. Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and Fisher's linear discriminant analysis (FLD), with different data pretreatment methods, were used for the classification analysis. Data combination of the outer skin and inner capsule spectra was discussed for further improving the classification results. The results show that multiple sensors provide more useful and complementary information than a single sensor does for improving the prediction accuracy. With the help of data combination strategy, 100% prediction accuracy can be obtained with both second-order derivative-FLD and continuous wavelet transform-multiplicative scatter correction-FLD methods.
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Affiliation(s)
- Pao Li
- Hunan Agricultural Product Processing InstituteHunan Academy of Agricultural SciencesChangshaChina
- Hunan Provincial Key Laboratory of Food Science and BiotechnologyCollege of Food Science and TechnologyHunan Agricultural UniversityChangshaChina
| | - Xinxin Zhang
- Hunan Provincial Key Laboratory of Food Science and BiotechnologyCollege of Food Science and TechnologyHunan Agricultural UniversityChangshaChina
| | - Yu Zheng
- School of MedicineHunan Normal UniversityChangshaChina
| | - Fei Yang
- School of MedicineHunan Normal UniversityChangshaChina
| | - Liwen Jiang
- Hunan Provincial Key Laboratory of Food Science and BiotechnologyCollege of Food Science and TechnologyHunan Agricultural UniversityChangshaChina
| | - Xia Liu
- Hunan Provincial Key Laboratory of Food Science and BiotechnologyCollege of Food Science and TechnologyHunan Agricultural UniversityChangshaChina
| | - Shenghua Ding
- Hunan Agricultural Product Processing InstituteHunan Academy of Agricultural SciencesChangshaChina
| | - Yang Shan
- Hunan Agricultural Product Processing InstituteHunan Academy of Agricultural SciencesChangshaChina
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