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Wang M, Hu T, Li Y, Wang R, Xu Y, Shi Y, Tong H, Yu M, Qin Y, Mei X, Su L, Mao C, Lu T, Li L, Ji D, Jiang C. An integrated and rapid evaluation of Curcumae Radix from different botanical origins based on chemical components, antiplatelet aggregation effect and Fourier transform near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025; 324:124992. [PMID: 39163771 DOI: 10.1016/j.saa.2024.124992] [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: 06/07/2024] [Revised: 07/12/2024] [Accepted: 08/14/2024] [Indexed: 08/22/2024]
Abstract
Curcumae Radix (CR) is a widely used traditional Chinese medicine with significant pharmaceutical importance, including enhancing blood circulation and addressing blood stasis. This study aims to establish an integrated and rapid quality assessment method for CR from various botanical origins, based on chemical components, antiplatelet aggregation effects, and Fourier transform near-infrared (FT-NIR) spectroscopy combined with multivariate algorithms. Firstly, ultra-performance liquid chromatography-photodiode array (UPLC-PDA) combined with chemometric analyses was used to examine variations in the chemical profiles of CR. Secondly, the activation effect on blood circulation of CR was assessed using an in vitro antiplatelet aggregation assay. The studies revealed significant variations in chemical profiles and antiplatelet aggregation effects among CR samples from different botanical origins, with constituents such as germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin, and curcumin showing a positive correlation with antiplatelet aggregation biopotency. Thirdly, FT-NIR spectroscopy was integrated with various machine learning algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Subspace K-Nearest Neighbors (Subspace KNN), to classify CR samples from four distinct sources. The result showed that FT-NIR combined with KNN and SVM classification algorithms after SNV and MSC preprocessing successfully distinguished CR samples from four plant sources with an accuracy of 100%. Finally, Quantitative models for active constituents and antiplatelet aggregation bioactivity were developed by optimizing the partial least squares (PLS) model with interval combination optimization (ICO) and competitive adaptive reweighted sampling (CARS) techniques. The CARS-PLS model achieved the best predictive performance across all five components. The coefficient of determination (R2p) and root mean square error (RMSEP) in the independent test sets were 0.9708 and 0.2098, 0.8744 and 0.2065, 0.9511 and 0.0034, 0.9803 and 0.0066, 0.9567 and 0.0172 for germacrone, β-elemene, bisdemethoxycurcumin, demethoxycurcumin and curcumin, respectively. The ICO-PLS model demonstrated superior predictive capabilities for antiplatelet aggregation biotency, achieving an R2p of 0.9010, and an RMSEP of 0.5370. This study provides a valuable reference for the quality evaluation of CR in a more rapid and comprehensive manner.
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Affiliation(s)
- Meng Wang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China; School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou 325035, China.
| | - Tingting Hu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Yuhang Li
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Rui Wang
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Yudie Xu
- School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou 325035, China.
| | - Yabo Shi
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Huangjin Tong
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing 210028, China.
| | - Mengting Yu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Yuwen Qin
- School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou 325035, China.
| | - Xi Mei
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Lianlin Su
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Chunqin Mao
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Tulin Lu
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Lin Li
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - De Ji
- School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Chengxi Jiang
- School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou 325035, China.
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Chen Y, Li S, Jia J, Sun C, Cui E, Xu Y, Shi F, Tang A. FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae ( chenpi) and predict the adulteration concentration. Food Chem X 2024; 24:101798. [PMID: 39296477 PMCID: PMC11408387 DOI: 10.1016/j.fochx.2024.101798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Revised: 08/30/2024] [Accepted: 08/30/2024] [Indexed: 09/21/2024] Open
Abstract
Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2 P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.
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Affiliation(s)
- Ying Chen
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Si Li
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Jia Jia
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Chuanduo Sun
- Central Medical Branch of PLA General Hospital, PR China
| | - Enzhong Cui
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Yunyan Xu
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Fangchao Shi
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Anfu Tang
- Department of Pharmacy, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
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Yang X, Zhuang X, Shen R, Sang M, Meng Z, Cao G, Zang H, Nie L. In situ rapid evaluation method of quality of peach kernels based on near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124108. [PMID: 38447442 DOI: 10.1016/j.saa.2024.124108] [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: 12/26/2023] [Revised: 02/24/2024] [Accepted: 03/02/2024] [Indexed: 03/08/2024]
Abstract
This study aimed to perform a rapid in situ assessment of the quality of peach kernels using near infrared (NIR) spectroscopy, which included identifications of authenticity, species, and origins, and amygdalin quantitation. The in situ samples without any pretreatment were scanned by a portable MicroNIR spectrometer, while their powder samples were scanned by a benchtop Fourier transform NIR (FT-NIR) spectrometer. To improve the performance of the in situ determination model of the portable NIR spectrometer, the two spectrometers were first compared in identification and content models of peach kernels for both in situ and powder samples. Then, the in situ sample spectra were transferred by using the improved principal component analysis (IPCA) method to enhance the performance of the in situ model. After model transfer, the prediction performance of the in situ sample model was significantly improved, as shown by the correlation coefficient in the prediction set (Rp), root means square error of prediction (RMSEP), and residual prediction deviation (RPD) of the in situ model reached 0.9533, 0.0911, and 3.23, respectively, and correlation coefficient in the test set (Rt) and root means square error of test (RMSET) reached 0.9701 and 0.1619, respectively, suggesting that model transfer could be a viable solution to improve the model performance of portable spectrometers.
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Affiliation(s)
- Xinya Yang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China
| | - Xiaoqi Zhuang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China
| | - Rongjing Shen
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China
| | - Mengjiao Sang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China
| | - Zhaoqing Meng
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China
| | - Guiyun Cao
- Shandong Hongjitang Pharmaceutical Group Co. Ltd., Jinan 250103, China
| | - Hengchang Zang
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China; Key Laboratory of Chemical Biology (Ministry of Education), Shandong University, Jinan 250012, Shandong, China; National Glycoengineering Research Center, Shandong University, Jinan 250012, Shandong, China.
| | - Lei Nie
- School of Pharmaceutical Sciences, Cheeloo College of Medicine, NMPA Key Laboratory for Technology Research and Evaluation of Drug Products, Institute of Biochemical and Biotechnological Drug, Shandong University, Jinan 250012, Shandong, China.
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Zhang JB, Wang B, Zhang YF, Wu Y, Li MX, Gao T, Lu TL, Bian ZH, Su LL. E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process. Food Chem 2024; 439:138148. [PMID: 38064826 DOI: 10.1016/j.foodchem.2023.138148] [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: 09/21/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 01/10/2024]
Abstract
Gastrodia elata (GE) is traditionally subjected to steaming, and steaming duration plays a crucially important role in determining GE quality. This study examined the variations in bioactive components during the steaming process and proposed the utilization of electronic eye and Fourier Transform near-infrared (FT-NIR) spectroscopy for quality assessment. The findings revealed that the levels of parishin E parishin B, parishin A, and gastrodin initially rose and subsequently declined, while 4-Hydroxybenzyl alcohol exhibited a rapid decrease followed by stabilization. With prolonged steaming, the brightness of GE decreased, while the red and yellow tones became more pronounced and the color saturation increased. FT-NIR divided the steaming process into three stages: 0 min (raw GE), 0-9 min (partially steamed GE), and 9-30 min (fully steamed GE), and the partial least squares regression models effectively predicted the levels of five components. Overall, this study provided valuable insights into quality control in food processing.
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Affiliation(s)
- Jiu-Ba Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Bin Wang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yun-Fei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yi Wu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ming-Xuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Ting Gao
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Tu-Lin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China.
| | - Zhen-Hua Bian
- Department of Pharmacy, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China; Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China.
| | - Lian-Lin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu Provincial Technology Engineering Research Center of TCM Health Preservation, Nanjing 210023, China.
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Qin Y, Zhao Q, Zhou D, Shi Y, Shou H, Li M, Zhang W, Jiang C. Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae. Food Chem X 2024; 21:101220. [PMID: 38384686 PMCID: PMC10879671 DOI: 10.1016/j.fochx.2024.101220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024] Open
Abstract
Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.
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Affiliation(s)
- Yuwen Qin
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Qi Zhao
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Dan Zhou
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Haiyan Shou
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
| | - Mingxuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Wei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China
- College of Pharmacy, Anhui University of Chinese Medicine, Anhui 230012, China
- Anhui Province Key Laboratory of Traditional Chinese Medicine Decoction Pieces of New Manufacturing Technology, China
| | - Chengxi Jiang
- College of Pharmacy, Wenzhou Medical University, Wenzhou 325035, China
- Jiuhuashan Polygonati Rhizoma Research Institute, Chizhou 247100, China
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Shi Y, He T, Zhong J, Mei X, Li Y, Li M, Zhang W, Ji D, Su L, Lu T, Zhao X. Classification and rapid non-destructive quality evaluation of different processed products of Cyperus rotundus based on near-infrared spectroscopy combined with deep learning. Talanta 2024; 268:125266. [PMID: 37832457 DOI: 10.1016/j.talanta.2023.125266] [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: 05/31/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 10/15/2023]
Abstract
The quality of traditional Chinese medicine is very important for human health, but the traditional quality control method is very tedious, which leads to the substandard quality of many traditional Chinese medicine. In order to solve the problem of time-consuming and laborious traditional quality control methods, this study takes traditional Chinese medicine Cyperus rotundus as an example, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with One-dimensional convolutional neural network (1D-CNN) and chaotic map dung beetle optimization (CDBO) algorithm combined with BP neural network (BPNN) is proposed. This strategy has the advantages of fast and non-destructive. It can not only qualitatively distinguish Cyperus rotundus and various processed products, but also quantitatively predict two bioactive components. In classification, 1D-CNN successfully distinguished four kinds of processed products of Cyperus rotundus with 100 % accuracy. Quantitatively, a CDBO algorithm is proposed to optimize the performance of the BPNN quantitative model of two terpenoids, and compared with the BP, whale optimization algorithm (WOA)-BP, sparrow optimization algorithm (SSA)-BP, grey wolf optimization (GWO)-BP and particle swarm optimization (PSO)-BP models. The results show that the CDBO-BPNN model has the smallest error and has a significant advantage in predicting the content of active components in different processed products. To sum up, it is feasible to use near infrared spectroscopy to quickly evaluate the effect of processing methods on the quality of Cyperus rotundus, which provides a meaningful reference for the quality control of traditional Chinese medicine with many other processing methods.
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Affiliation(s)
- Yabo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Tianyu He
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Jiajing Zhong
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Xi Mei
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
| | - Yu Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Mingxuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Wei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - De Ji
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Lianlin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China
| | - Tulin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
| | - Xiaoli Zhao
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, PR China.
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Li MX, Shi YB, Zhang JB, Wan X, Fang J, Wu Y, Fu R, Li Y, Li L, Su LL, Ji D, Lu TL, Bian ZH. Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms. Food Chem X 2023; 20:101022. [PMID: 38144802 PMCID: PMC10740088 DOI: 10.1016/j.fochx.2023.101022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/09/2023] [Accepted: 11/19/2023] [Indexed: 12/26/2023] Open
Abstract
Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.
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Affiliation(s)
- Ming-xuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Ya-bo Shi
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Jiu-ba Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Xin Wan
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Jun Fang
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Yi Wu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Rao Fu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Yu Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Lin Li
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Lian-lin Su
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - De Ji
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Tu-lin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Zhen-hua Bian
- Department of Pharmacy, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, 214071, China
- Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, 214071, China
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