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Qian S, Wang Z, Chao H, Xu Y, Wei Y, Gu G, Zhao X, Lu Z, Zhao J, Ren J, Jin S, Li L, Chen K. Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124718. [PMID: 38950481 DOI: 10.1016/j.saa.2024.124718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/08/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024]
Abstract
A new transfer approach was proposed to share calibration models of the hexamethylenetetramine-acetic acid solution for studying hexamethylenetetramine concentration values across different near-infrared (NIR) spectrometers. This approach combines Savitzky-Golay first derivative (S_G_1) and orthogonal signal correction (OSC) preprocessing, along with feature variable optimization using an adaptive chaotic dung beetle optimization (ACDBO) algorithm. The ACDBO algorithm employs tent chaotic mapping and a nonlinear decreasing strategy, enhancing the balance between global and local search capabilities and increasing population diversity to address limitations observed in traditional dung beetle optimization (DBO). Validated using the CEC-2017 benchmark functions, the ACDBO algorithm demonstrated superior convergence speed, accuracy, and stability. In the context of a partial least squares (PLS) regression model for transferring hexamethylenetetramine-acetic acid solutions using NIR spectroscopy, the ACDBO algorithm excelled over alternative methods such as uninformative variable elimination, competitive adaptive reweighted sampling, cuckoo search, grey wolf optimizer, differential evolution, and DBO in efficiency, accuracy of feature variable selection, and enhancement of model predictive performance. The algorithm attained outstanding metrics, including a determination coefficient for the calibration set (Rc2) of 0.99999, a root mean square error for the calibration set (RMSEC) of 0.00195%, a determination coefficient for the validation set (Rv2) of 0.99643, a root mean squared error for the validation set (RMSEV) of 0.03818%, residual predictive deviation (RPD) of 16.72574. Compared to existing OSC, slope and bias correction (S/B), direct standardization (DS), and piecewise direct standardization (PDS) model transfer methods, the novel strategy enhances the accuracy and robustness of model predictions. It eliminates irrelevant background information about the hexamethylenetetramine concentration, thereby minimizing the spectral discrepancies across different instruments. As a result, this approach yields a determination coefficient for the prediction set (Rp2) of 0.96228, a root mean squared error for the prediction set (RMSEP) of 0.12462%, and a relative error rate (RER) of 17.62331, respectively. These figures closely follow those obtained using DS and PDS, which recorded Rp2, RMSEP, and RER values of 0.97505, 0.10135%, 21.67030, and 0.98311, 0.08339%, 26.33552, respectively. Unlike conventional methods such as OSC, S/B, DS, and PDS, this novel approach does not require the analysis of identical samples across different instruments. This characteristic significantly broadens its applicability for model transfer, which is particularly beneficial for transferring specific measurement samples.
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Affiliation(s)
- Shichuan Qian
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhi Wang
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Hui Chao
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Yinguang Xu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Yulin Wei
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Guanghui Gu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Xinping Zhao
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Zhiyan Lu
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Jingru Zhao
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Jianmei Ren
- Gansu Yinguang Chemical Industry Group Co., Ltd, Baiyin 730900, China
| | - Shaohua Jin
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Lijie Li
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Kun Chen
- School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
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Chen S, Du K, Shan B, Xu Q, Zhang F. A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1021-1033. [PMID: 38312025 DOI: 10.1039/d3ay01942j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
In this paper, a novel hybrid variable selection method for model building by near-infrared (NIR) spectroscopy is proposed for composition measurement in industrial processes. A double-layer structure is designed for variable selection by combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS). The Fisher classifier combined with model population analysis is used to select the variable interval wherein the useful variables are roughly located even when strong multicollinearity exists among spectral variables. Opposition-based learning (OBL) and jumping genes (JG) are introduced to improve the binary cuckoo search algorithm for the fine selection of key variables, thus avoiding the loss of excellent solutions due to randomness and the local optimum. Different variable selection methods were used to select variables for beer, corn, and diesel fuel datasets, and the partial least squares (PLS) algorithms were used to build calibration models to predict the original extract concentration of beer, the protein and starch content of corn, and the boiling point of diesel fuel, respectively. The results showed that the proposed PLS modeling method based on FCPA-IBCS has higher fitting accuracy and smaller prediction errors.
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Affiliation(s)
- Shuobo Chen
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Kang Du
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Baoming Shan
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Qilei Xu
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
| | - Fangkun Zhang
- College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, 266061, P. R. China.
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Bian X, Zhao Z, Liu J, Liu P, Shi H, Tan X. Discretized butterfly optimization algorithm for variable selection in the rapid determination of cholesterol by near-infrared spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5190-5198. [PMID: 37779476 DOI: 10.1039/d3ay01636f] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
The blood cholesterol level is strongly associated with cardiovascular disease. It is necessary to develop a rapid method to determine the cholesterol concentration of blood. In this study, a discretized butterfly optimization algorithm-partial least squares (BOA-PLS) method combined with near-infrared (NIR) spectroscopy is firstly proposed for rapid determination of the cholesterol concentration in blood. In discretized BOA, the butterfly vector is described by 1 or 0, which represents whether the variable is selected or not, respectively. In the optimization process, four transfer functions, i.e., arctangent, V-shaped, improved arctangent (I-atan) and improved V-shaped (I-V), are introduced and compared for discretization of the butterfly position. The partial least squares (PLS) model is established between the selected NIR variables and cholesterol concentrations. The iteration number, transfer functions and the performance of butterflies are investigated. The proposed method is compared with full-spectrum PLS, multiplicative scatter correction-PLS (MSC-PLS), max-min scaling-PLS (MMS-PLS), MSC-MMS-PLS, uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Results show that the I-V function is the best transfer function for discretization. Both preprocessing and variable selection can improve the prediction performance of PLS. Variable selection methods based on BOA are better than those based on statistics. Furthermore, I-V-BOA-PLS has the highest predictive accuracy among the seven variable selection methods. MSC-MMS can further improve the prediction ability of I-V-BOA-PLS. Therefore, BOA-PLS combined with NIR spectroscopy is promising for the rapid determination of cholesterol concentration in blood.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co. Ltd., Binzhou 256500, China
- NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Shandong University, Jinan, 250012, China
| | - Zizhen Zhao
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Jianwen Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
| | - Huibing Shi
- Shandong Provincial Key Laboratory of Olefin Catalysis and Polymerization, Shandong Chambroad Holding Group Co. Ltd., Binzhou 256500, China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin, 300387, China.
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Bian X, Zhang R, Liu P, Xiang Y, Wang S, Tan X. Near infrared spectroscopic variable selection by a novel swarm intelligence algorithm for rapid quantification of high order edible blend oil. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 284:121788. [PMID: 36058170 DOI: 10.1016/j.saa.2022.121788] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
The quantification of single oil in high order edible blend oil is a challenging task. In this research, a novel swarm intelligence algorithm, discretized whale optimization algorithm (WOA), was first developed for reducing irrelevant variables and improving prediction accuracy of hexanary edible blend oil samples. The WOA is inspired by hunting strategy of humpback whales, which mainly includes three behaviors, i.e., encircling prey, bubble-net attacking and searching for prey. In discretized WOA, positions of whales were updated and then discretized by arctangent function. The whale population performance, iteration number and whale number of WOA were investigated. To validate the performance of selected variables, partial least squares (PLS) was used to build model and predict single oil contents in hexanary blend oil. Results show that WOA-PLS can provide the best prediction accuracy compared with full-spectrum PLS, continuous wavelet transform-PLS (CWT-PLS), uninformative variable elimination-PLS (UVE-PLS), Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). Furthermore, CWT-WOA-PLS can further produce better results with fewer variables compared with WOA-PLS.
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Affiliation(s)
- Xihui Bian
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Sichuan 644000, China; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China.
| | - Rongling Zhang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Peng Liu
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Yang Xiang
- State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
| | - Shuyu Wang
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
| | - Xiaoyao Tan
- State Key Laboratory of Separation Membranes and Membrane Processes, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China
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Zhang F, Zhang Y, Shi L, Li L, Cui X, Gao Y. Application of portable near‐infrared spectroscopy technology for grade identification of Panax notoginseng slices. J Food Saf 2023. [DOI: 10.1111/jfs.13033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Fujie Zhang
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Yu Zhang
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Lei Shi
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Lixia Li
- Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China
| | - Xiuming Cui
- Yunnan Key Laboratory of Sustainable Utilization of Panax Notoginseng Kunming University of Science and Technology Kunming China
| | - Yongping Gao
- Yixintang Pharmaceutical Group Ltd. Kunming China
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Tao Y, Bao J, Liu Q, Liu L, Zhu J. Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets. Molecules 2022; 27:molecules27206969. [PMID: 36296563 PMCID: PMC9609342 DOI: 10.3390/molecules27206969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/20/2022] Open
Abstract
Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established. Raman spectra of 330 real samples were collected by a portable Raman spectrometer. The contents of danshensu, ferulic acid, rosmarinic acid, and salvianolic acid B were determined with high-performance liquid chromatography-diode array detection (HPLC-DAD), while the content of soluble solid was determined by using an oven-drying method. In the establishing of the CNN calibration model, the spectral characteristic bands were screened out by a competitive adaptive reweighted sampling (CARS) algorithm. The performance of the CNN model is evaluated by root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), coefficient of determination of calibration (Rc2), coefficient of determination of cross-validation (Rcv2), and coefficient of determination of validation (Rp2). The Rp2 values for soluble solid, salvianolic acid B, danshensu, ferulic acid, and rosmarinic acid are 0.9415, 0.9246, 0.8458, 0.8667, and 0.8491, respectively. The established model was used for the analysis of three batches of unknown samples from the manufacturing process of Guanxinning tablets. As the results show, Raman spectroscopy is faster and more convenient than that of conventional methods, which is helpful for the implementation of process analysis technology (PAT) in the manufacturing process of Guanxinning tablets.
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Affiliation(s)
- Yi Tao
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- Correspondence: (Y.T.); (J.Z.)
| | - Jiaqi Bao
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Qing Liu
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Li Liu
- Chiatai Qingchunbao Pharmaceutical Co., Ltd., Hangzhou 310023, China
| | - Jieqiang Zhu
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- Correspondence: (Y.T.); (J.Z.)
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