Teicoplanin (TCP) is a multiple drug-resistant lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. In this study, a mixture of TCP with the Tris-HCl buffer (TCP-Tris- HCl) was used to simulate TCP fermentation broth. The reagent-free, rapid and simultaneous quantitative analysis models for TCP and Tris in the TCP-Tris-HCl mixtures were established by near-infrared (NIR) spectroscopy. The equidistant combination partial least squares (EC-PLS) method and the equivalent model sets were proposed, the simplest equivalent model with the smallest number of wavelengths were further selected. The initial wavelength, number of wavelengths, number of wavelength gaps, number of PLS factors were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplifie d. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were 0.043 mg·mL-1 and 0.9998 for TCP, and 0.222 mg·mL-1 and 0.9989 for Tris, respectively. The results indicate that NIR method can be applied to highly accurate quantitative analysis for TCP and provide valuable references for further application to TCP fermentation broth.
Teicoplanin (TCP) is a multiple drug-resistant novel lipoglycopeptide antibiotic produced by the fermenting of Actinoplanes teichomyceticus [
As a rapid, non-destructive, eco-friendly and cost-effective analytical technique, near-infrared (NIR) spectroscopy has been extensively used in agriculture [
The Tris-HCl buffer, which is often used in biochemistry and molecular biology experiments for its stable nature, is suitable for use in simulating the physiological environment of a living body, such as the enzyme reaction in cell sap [
NIR spectra are generally composed of relatively week and highly overlapping bands. A multivariate calibration method must be used for quantitative analysis of NIR spectra. In parallel with chemometric developments, the reagent-free NIR analysis method shows substantial potential in drug monitoring. Partial least squares (PLS) has been proven an effective method to extract information and overcome spectral colinearity. However, the prediction effect of PLS is difficult to improve when the signal-to-noise ratio of a waveband is not adequately high [
Moving window PLS (MW-PLS) is a well-performed and PLS-based method with wavelength selection in the study of many objects [
TCP standard products were purchased from National Institutes for Food and Drug Control (Beijing, China). Tris was analytical reagent. TCP-Tris-HCl mixtures were prepared to simulate TCP fermentation broth.
Given that the concentration of TCP can reach to 3.2 mg∙mL−1 with the pH values of approximately 7.0 to 7.5 [
A uniform statistic distribution of the concentrations of TCP and Tris in the 72 mixture samples was found. The concentrations for 72 mixture samples ranged from 0.338 mg∙L−1 to 9.805 mg∙L−1 for TCP, 6.272 mg∙L−1 to 20.561 mg∙L−1 for Tris, and the mean value and standard deviation were 4.114 and 2.225 mg∙L−1 for TCP, 13.438 and 4.505 mg∙L−1 for Tris, respectively, which were used as the reference values for the calibration modeling of NIR spectroscopic analysis.
All samples were used for spectrometry measurement. Spectra were collected using an XDS Rapid Content TM Liquid Grating Spectrometer (FOSS, Denmark) equipped with transmission accessory and a 2 mm cuvette. The scanning range spanned from 400 - 2498 nm with a 2 nm wavelength interval, including the entire NIR region and a large part of the visible region. Wavebands of 400 - 1100 and 1100 - 2498 nm were used for Si and PbS detection, respectively. Each sample was scanned in triplicate, and the mean value of the three measurements was used for modeling. Spectra were recorded at 25˚C ± 1˚C and 46% ± 1% relative humidity.
A calibration and prediction process was performed to achieve the goal of modeling optimization. All samples were divided into the calibration (40 samples) and prediction (32 samples) sets. In order to ensure modeling representativeness and integrity, the calibration and prediction sets must cover the concentration ranges of the two indicators, and the distribution must be uniform. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were calculated, respectively. Calculation formulas are as follows:
where n was number of prediction samples;
EC-PLS method employed moving-window mode to select an appropriate combination of equidistant wavelengths to establish PLS model, the search parameters were set as the follows: 1) initial wavelength (I), 2) number of wavelengths (N), 3) number of wavelength gaps (G), and 4) number of PLS factors (F). The search range covered the entire scanning region, but it can also be reduced according to the actual conditions. The total number of wavelengths for the search range was set as N*. Therefore, N can be set as
The combination of parameters (I, N, G) corresponded to a continuous waveband when G = 1, which corresponded to MW-PLS method. Therefore, EC-PLS is the promotion of MW-PLS in term of the algorithm.
In this study, the parameters I, N, G, and F were set as
The number of PLS factor (F) is an important parameter that corresponds to the number of integrated spectral variables. The selection of a reasonable F is necessary but difficult [
On the other hand, due to the cost and material properties, the instrument design typically involves certain limitations of the position and number of wavelengths. At some instances, the demand of actual conditions is not met by the global optimal waveband. Therefore, local optimal wavebands that correspond to different positions and numbers of wavelengths are significant. For any fixed I = I0, the local optimal model was selected according to the follows:
For any fixed N = N0, the local optimal model was selected according to the follows:
And for any fixed G = G0, the local optimal model was selected by:
As mentioned, the global optimal wavelength combination can be selected according to min SEP. However, the models with insignificantly fluctuating prediction accuracy are statistically equivalent because the samples are random and limited. Therefore, the optimal SEP value can slowly increase. The equivalence model set that corresponds to a certain percentage (α) was expressed as the follows:
The α value was set through simulation experiments based on the actual data. From the obtained equivalent model set
The computer platform in this study was developed with Matlab 7.6 software.
The NIR spectra of 72 samples of TCP-Tris-HCl mixture in the entire scanning region (400 - 2498 nm) are illustrated in
Based on the entire scanning region, the full PLS models for the analyses of TCP and Tris were first established. The model parameters and prediction effects (SEP, RP) are summarized in
Methods | Indicator | I | N | G | F | SEP (mg∙mL−1) | RP |
---|---|---|---|---|---|---|---|
Full PLS | TCP | 400 | 1050 | 1 | 14 | 0.506 | 0.9723 |
Tris | 7 | 0.372 | 0.9970 | ||||
Optimal EC-PLS | TCP | 1508 | 62 | 3 | 7 | 0.041 | 0.9998 |
Tris | 1106 | 27 | 4 | 6 | 0.206 | 0.9990 | |
Simplest equivalent models | TCP | 1520 | 28 | 5 | 5 | 0.043 | 0.9998 |
Tris | 1084 | 13 | 6 | 4 | 0.222 | 0.9989 |
EC-PLS was performed to improve prediction effect and reduce model complexity. The obtained optimal parameters I, N, G, and F were 1508 nm, 62, 3, 7 for TCP and 1106 nm, 27, 4, 6 for Tris, respectively. The parameters and prediction effects are also summarized in
The SEP values of the local optimal models for each I, N, and G are shown in
The method mentioned above was performed to select the equivalence model sets for the two indicators. The global optimal SEP values (SEP*) were 0.041 and 0.206 mg∙mL−1 for TCP and Tris, respectively. The values of α for the two indicators were both set as 0.08 through several simulation experiments, and the corresponding equivalence model sets were expressed as the follows:
where, the equivalence model set (10) included 21 models for TCP, the wavelength combination ranged from 1470 to 1890 nm; while the equivalence model set (11) included 27 models for Tris, the wavelength combination ranged from 988 to 1338 nm. Positions of the wavelength combinations are shown in
The simplest equivalent models were further selected. The parameters I, N, G and F were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. The corresponding wavelengths combinations were 1520, 1530, 1540, 1550, 1560, 1570, 1580, 1590, 1600, 1610, 1620, 1630, 1640, 1650, 1660, 1670, 1680, 1690, 1700, 1710, 1720, 1730, 1740, 1750, 1760, 1770, 1780, 1790 (nm) for TCP and 1084, 1096, 1108, 1120, 1132, 1144, 1156, 1168, 1180, 1192, 1204, 1216, 1228 (nm) for Tris, respectively. The parameters and prediction effects are also summarized in
Take the simplest equivalent models for the examples; the relationships between the predicted and actual values are illustrated in
A rapid measurement method for TCP has a great significance and applied value in drug production monitoring and pharmacokinetics measurement. In this study, a simultaneous quantitative analysis method of TCP and Tris in the TCP-Tris-HCl mixture was established with reagent-free NIR spectroscopy.
The EC-PLS method and the equivalence model sets were proposed to select appropriate wavelength combinations, the simplest equivalent models were further obtained for NIR analysis of TCP and Tris. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplified. The results showed that the predicted values were high correlated and in good agreement to the actual values. NIR spectroscopy combined with the proposed wavelength selection method was successfully applied for reagent-free, accurate and simultaneous quantitative analysis of TCP and Tris. The wavelength selection could provide a valuable reference for further application to TCP fermentation broth. We believe that the methodological framework has such applicability and can be applied to other spectroscopic analysis fields.
This work was supported by the Science and Technology Project of Guangdong Province of China (No. 2014A020213016, No. 2014A020212445) and the Science and Technology Project of Guangzhou of China (No. 2011Y5-00002).
Jing Zhang,Tian Ai,Jiemei Chen,Tao Pan, (2016) Wavelength Selection for Near-Infrared Spectroscopic Analysis of Teicoplanin. American Journal of Analytical Chemistry,07,460-468. doi: 10.4236/ajac.2016.75043