| 光谱学与光谱分析 |
|
|
|
|
|
| Application of Near Infrared Spectroscopy in Rapid and Simultaneous Determination of Essential Components in Five Varieties of Anti-Tuberculosis Tablets |
| TENG Le-sheng,WANG Di,SONG Jia,ZHANG Yi-bo,GUO Wei-liang,TENG Li-rong* |
| College of Life Science, Jilin University, Changchun 130012, China |
|
|
|
|
Abstract Since 1980s, tuberculosis has become increasingly serious.Rifampicin tablets, isoniazide tablets, pyrazinamide tablets, rifampicin and isoniazide tablets and rifampicin isoniazide and pyrazinamide tablets are currently relatively efficacious anti-tuberculosis drugs.In the present paper, near infrared spectroscopy (NIRS) with partial least squares (PLS) was applied to the simultaneous determination of rifampicin (RMP), isoniazide (INH) and pyrazinamide (PZA) contents in 5 varieties of anti-tuberculosis tablets.As the results showed, all of the models for the determination of RMP, INH and PZA contents applied the original NIR spectra.The most efficacious wavelength range for the determination of RMP contents was 1 981-2 195 nm, it was 1 540-1 717 nm and 2 086-2 197 nm for the determination of INH contents, and it was 1 460-1 537 nm, 1 956-2 022 nm and 2 268-2 393 nm for determination of PZA contents.The root mean square error of the calibration set obtained by cross-validation (RMSECV) of the optimum models for the quantitative analysis of RMP, INH and PZA contents was 0.049 4, 0.025 7 and 0.030 7, respectively.Using these optimum models for the determination of RMP, INH and PZA contents in prediction set, the root mean square error of prediction set (RMSEP) was 0.018 2, 0.016 6 and 0.013 4, respectively.The correlation coefficient (rp) between the predicted values and actual values was 0.986 4, 0.998 9 and 0.999 3, respectively.These results demonstrated that this method was precise and reliable, and is significative for in situ measurement and the on-line quality control for anti-tuberculosis tablets production.
|
|
Received: 2007-05-06
Accepted: 2007-08-12
|
|
|
|
Corresponding Authors:
TENG Li-rong
E-mail: tenglr@jlu.edu.cn
|
|
[1] Phillip O, Graham A, Rook W.Nature Reviews, 2004, 2：930.
[2] Panchagnula R, Agrawal S.International Journal of Pharmaceutics, 2004, 271：1.
[3] Agrawal S, Kaur K J, Singh I, et al.International Journal of Pharmaceutics, 2002, 233：169.
[4] Panchagnula R, Sancheti P, Rungta S, et al.Pharmacological Research, 2003, 48：383.
[5] Espinosa-Mansilla A, Acedo-Valenzuela M I, Pena A M, et al.Talanta, 2002, 58：273.
[6] Calleri E, Lorenzi E D, Furlanetto S, et al.Journal of Pharmaceutical and Biomedical Analysis, 2002, 29(6)：1089.
[7] Argekar A P, Kunjir S S, Purandare K S.Journal of Pharmaceutical and Biomedical Analysis, 1996, 14(11)：1645.
[8] Espinosa-Mansilla A, Acedo-Valenzuela M I, Munoz A., et al.Analytica Chimica Acta, 2001, 427：129.
[9] McClure W F.Journal of Near Infrared Spectroscopy, 2003, 11(6)：487.
[10] GUO Wei-liang, MENG Qing-fan, LU Jia-hui, et al.The 2006 International Conference on Computational Science and Its Applications, Lecture Notes in Computer Science 3980, 2006.938.
[11] TENG Le-sheng, NIAN Zong-qian, LU Jia-hui, et al(滕乐生, 年综潜, 逯家辉, 等).Journal of Jilin University(Engineering and Technology Edition)(吉林大学学报, 工学版), 2006, 36(3)：443.
[12] MENG Qing-fan, ZHANG Yi-bo, LU Jia-hui, et al(孟庆繁, 张益波, 逯家辉, 等).Chinese Pharmaceutical Journal(中国药学杂志), 2007, 42(4)：304.
[13] Blanco M, Coello J, Iturriaga H.Chemometrics and Intelligent Laboratory Systems, 2000, 50：75.
[14] NI Li-jun, ZHU Jing, ZHANG Li-guo(倪力军, 朱 静, 张立国).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(1)：67.
[15] LIU Li-ying, CHEN Hong-zhang(刘丽英, 陈洪章).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(2)：275.
[16] Eriksson L, Johansson E, Muller M, et al.Chemometrics, 2000, 14：599.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
| [3] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [4] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [5] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [6] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [7] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [8] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [9] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [10] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [15] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
|
|
|
|
