Journal of Spectroscopy

Journal of Spectroscopy / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 1521035 |

Jin-Fang Ma, Tian-Ling Chen, Xiang-Dong Zhang, Xue Xiao, Fa-Huan Ge, "An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling", Journal of Spectroscopy, vol. 2019, Article ID 1521035, 8 pages, 2019.

An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling

Academic Editor: Jose S. Camara
Received18 May 2019
Accepted22 Jul 2019
Published22 Aug 2019


This study established an approach to rapidly determine the Tween-80 in traditional Chinese medicine (TCM) injection by using near-infrared (NIR) spectroscopy. Totally 133 standard solutions of Tween-80 were prepared and randomly divided into calibration set and validation set, containing 109 and 24 samples, respectively. Spectral data were preprocessed and then subjected to establish a predictive model using partial least-squares (PLS). The standard error of cross validation (SECV), standard deviation of calibration (SEC), and the determination coefficient (R) of the established model were 0.0561, 0.0526, and 0.9986, respectively. The model was successfully applied to determine Tween-80 contents in 25 XBJ Injections and 40 FFSX Injections, and it produced satisfactory quantitative analysis results with average relative deviations 0.60% and 0.16%, respectively, for 25 XBJ Injections and 40 FFSX Injections, and the maximum relative deviations 8.57% and 7.60%, respectively. This work shows that NIR model displayed quite good predictive ability for Tween-80 quantitative analysis, which could potentially be applied to rapid determination of Tween-80 in the production process of TCM injections and other TCM products.

1. Introduction

Traditional Chinese medicine (TCM) injection is a new dosage form developed in the modernization of TCM. It displays exact efficacy in clinical practice but raises a controversy either due to its possible adverse reactions [13]. TCM injections are generally produced from medicinal materials with sources of plants or animals, and both the complicated ingredients thereof and the production process may result in uncontrollable and random factors affecting the quality of TCM injections. Moreover, TCM injections are typically added with excipients, such as various solubilizers, which may also affect the quality of the products. Therefore, TCM injection is facing high-quality risks, and accordingly, more perfect means of quality control are urgently required [46].

However, the current quality control method of TCM injections is still at infancy. According to Chinese Pharmacopoeia (2015), quality control of TCM injection is mainly conducted via determining some active ingredients, while nonbioactive additives and excipients thereof were often ignored. A representative pharmaceutical excipient is Tween-80 (see its chemical structure in Figure 1).

As a common solubilizer, emulsifier, or stabilizer, Tween-80 is often used in a number of TCM injections, such as XBJ Injection and FFSX Injection. In general, nearly 2.5% Tween-80 [7] is commonly added to TCM injection, particularly in TCM injection with volatile components as the main active ingredients. Tween-80 is a nonbioactive adjuvant, but recent studies have found that drugs containing Tween-80 exhibit sensitization, hemolysis, hepatotoxicity, or peripheral neurotoxicity activities [810] and may even correlate with anaphylactic reaction [13]. Therefore, the FDA has issued a security warning for Tween-80 [11]. So far, adverse reactions of TCM injections have been frequently reported [13], and there are evidences indicating that Tween-80 might be, at least in part, responsible for those issues [12, 13]. So, Tween-80 should attract our more attention in the quality control of TCM injections.

Some routine analytical methods, such as gas chromatography and ultraviolet spectrophotometry, were widely used to determine Tween-80 content [1417]. However, these methods are mostly cumbersome and time-consuming, and the determination process may suffer from slight deviations; as a result, sample quality cannot be ascertained in a timely manner. Thus, a rapid, efficient, and accurate quality control method is needed.

Near-infrared (NIR) spectroscopy has been ever increasingly accepted as a fast and green process analysis technology in chemical industry, agriculture, food, medicine, tobacco, and some other fields [1822]. The frequency doubling of the stretching vibration of compounds X-H (X = C, O, N, etc.) causes absorption in NIR spectroscopy. NIR combined with chemometrics algorithms can qualitatively and quantitatively determine unknown samples [23, 24], thereby achieving the purpose of quality control. Researchers have used different concentrations of ethanol solution to quickly determine the alcoholicity of liquor and have found good application prospect [25].

This work intends to use the NIR spectrum of different concentrations of Tween-80 standard solutions to establish an optimized PLS model. By using the proposed model, Tween-80 contents can be predicted in TCM injections (see Figure 2). This work is expected to provide a promising method for the quality control of TCM injection and gain further insight into the preparation of medicines containing Tween-80.

2. Materials and Methods

2.1. Materials

Tween-80 (batch number 20120201) was obtained from Nanjing Well Chemical Co., Ltd. (Nanjing, China). 25.0 g Tween-80 was accurately weighted and completely dissolved in pure water to a final volume of 500 mL. Then, 133 Tween-80 standard solutions were then prepared by stock solution dilution, with concentrations uniformly distributed at the range of 0.1∼5% (g/g).

25 XBJ Injection samples (Tween-80 labeled amount 0.14%, g/g) were provided by the manufacturer A (Tianjin, China). These samples involve nine batches, with batch numbers as follows: 1212051, 1212151, 1212211, 1212231, 1301061, 1301121, 1301151, 1301161, and 1303171. 40 FFSX Injection samples (Tween-80 labeled amount 2.00%, g/g) were provided by the manufacturer B (Shijiazhuang, China). These samples involve eight batches, with batch numbers as follows: 12021031, 12021131, 12021141, 12022041, 12022441, 12022941, 12022942, and 12022942.

2.2. NIR Apparatus and Software

A Bruker spectrometer (Bruker Optics, Germany) was used for spectral acquisition. Transmission spectra of samples were scanned in a 2 mm light path. Each spectrum was acquired as the average of 32 scans on the same sample in the wavelength range of 12,000 to 4000 cm−1. The spectral resolution was kept to 8.0 cm−1.

Processes of spectral wavebands selection, spectral pretreatment, and PLS modeling were performed using THUNIR software (Tsinghua University version 3.0, Beijing) [2629].

2.3. Spectral Data Pretreatment

In order to obtain reliable, accurate, and stable calibration models, it is necessary to preprocess the spectral data before modeling. Spectral pretreatment was performed using Savitsky–Golay (SG) smoothing, first-order and second-order convolutional derivation, multiplicative scatter correction (MSC), standard normal variant (SNV), and normalization. The effects of different pretreatment methods were compared, and the optimized combination was selected.

2.4. Calibration of Models

The predictive model was constructed based on NIR data of 133 Tween-80 standard solution samples. Partial least squares (PLS) combines data matrix decomposition with regression interaction. The PLS model is robust and reduces the interference caused by light scattering and other components. PLS can eliminate noises to establish a well-stabilized model with prediction ability, thereby solving the model’s overfitting and variable collinearity. In NIR-based modeling, PLS is the most commonly used linear regression method [3032]. In this study, the PLS method was thus used to extract valid information from the NIR spectrum and a quantitative calibration model was established. The performance of the established model was evaluated via indicators of standard error of cross validation (SECV), the standard error of calibration (SEC), the primary factor (LV), and the determination coefficient (R) [28, 3335].

The established quantitative calibration model was used to predict the Tween-80 content in 25 XBJ Injection samples and 40 FFSX Injection samples.

3. Results and Discussion

3.1. NIR Spectral Features

The selected spectral scanning range was 12000–4000 cm−1, and the measured characteristics were the fundamental frequencies of molecular vibration, particularly the frequency doubling and combined frequency absorption bands of the stretching and bending vibration [3639]. The original and pretreated spectra of Tween-80 are shown in Figure 3. In the original spectra, 5500–5800 cm−1 and 6500–7300 cm−1 had significant responses, which were related to the second-order frequency doubling of -CH2 and -CH and the three-stage frequency doubling of C=O [4043]. Figure 4(a) summarizes the shifts in the NIR spectrum baseline. These three bonds are widespread in the target molecules, so their concentration information can be reflected in the NIR spectra and this can be viewed as the foundation of NIR quantitative analysis of the Tween-80.

3.2. Calibration of the Model
3.2.1. Calibration and Validation Sets

133 samples were randomly divided into calibration and validation sets, which contained, respectively, 109 and 24 samples. The calibration set was used to construct a predictive model for quantitative determination of Tween-80 in TCM injections, and the validation set was applied as blind samples to investigate the accuracy of the proposed method.

3.2.2. Data Pretreatment

NIR spectroscopy inevitably contains noises that are independent of the nature of the sample. Pretreatment of the spectral data can effectively filter the noise of the NIR spectrum, thereby ensuring a good correlation between the spectral data and the components, reducing the complexity of the quantitative model, and improving the accuracy of the model [22]. We investigated different spectral preprocessing methods and compared their SEC, SECV, and R values, as listed in Table 1. After SG smoothing on spectra, the SEC and SECV values obviously decreased and R values were close to 1, which suggested pretreatment of convolution smoothing allows a superior model performance.

No.Pretreatment methodsLVsCalibrationCross validation

BSG smoothing50.99860.05260.99840.0561
COne DC50.99850.05230.99800.0635
DTwo DC50.99850.05230.99840.0561
HOne DC + MSC40.99640.08340.99580.8840
ITwo DC + MSC40.99640.8340.99810.8840

LV: latent variable number; SEC: standard error of calibration; SECV: standard error of cross validation; DC: dimension convolution; MSC: multiple scatter correction; SNV: standard normal variate.
3.2.3. Waveband Selection

The band of near-infrared spectrum was 14286–4000 cm−1, which was divided into short-wave and long-wave near-infrared regions. The short-wave near-infrared region was a high-frequency absorption band, and the long-wave near-infrared region was a first- and second-order frequency doubling absorption band [44]. To obtain more information, the available area was covered up to the entire near-infrared band, which had a wider spectral range than that of the short-wave or long-wave near-infrared alone. As shown in Figure 3, most of the spectral regions were smooth. However, noise was detected in the spectra of 5300–4800 and 420–4000 cm−1, which were discarded during the calibration process. In this research, the selected wavelength range was 5780–5870.7 cm−1. To verify the correctness of the band selection, the relevant coefficients of the spectrum in this band were studied. As shown in Figure 4(b), the variables with higher coefficients (>0.4) were mostly distributed in the selected region. Therefore, the selected band (5780–5870.7 cm−1) was the best modeling band.

3.2.4. Optimum LVs

Selection of the primary factors directly affects the prediction ability of calibration model. For instance, if the selected primary factor is excessively small, the useful information of the original spectrum will be lost and may result in insufficient fitting, while the primary factor is excessively large, it will include excessive measured noise, leading to an overfitting phenomenon and a significant increase in the prediction error of the built model. To avoid “under-fitting” and “overfitting,” the optimal LV values must be used to establish the appropriate model [4548]. In this study, we acquire optimum LVs by comparing the correlation diagram of the SECV and LV values. The SECV value is observed decreasing as the LV increasing, and LV reaches the optimal value when the SECV approached a constant value, as shown in Figure 4(c).

3.2.5. Model Establishment

The PLS method has been most commonly used in NIR spectroscopy-based quantitative modeling. Data of Tween-80 NIR spectrum were pretreated and then used to establish a calibration model. The SECV, SEC, LV, and R values were investigated to evaluate the model performance, and the result showed that the models had low SEC and SECV values and R value close to 1. The best modeling band (5780–5870.7 cm−1) was selected, which allowed acceptable model performance of the Tween-80 calibration model, with parameters as follows: LVs 5, SECV values 0.0561, SEP values of 0.0526, and the R value 0.9984.

3.2.6. Model Validation

The established calibration model should take an external validation to verify its accuracy. Samples of validation set were tested as blind samples using the established model, and the result indicates that the correlation of the prediction values and factual values R is most close to 1 (). Figure 5 indicates that the calibration model was acceptable.

3.2.7. Model Application

The Tween-80 labeled amounts in XBJ Injection and FFSX Injection were 0.14% and 2.00%, respectively. The established Tween-80 calibration model was used to predict the Tween-80 content in 25 samples of XBJ Injection and 40 samples of FFSX Injection. Table 2 lists the predicted value and the labeled value. The results showed that in samples of both XBJ Injection and FFSX Injection, the predicted values of the Tween-80 calibration model strongly matched with the true value. For XBJ Injection and FFSX Injection, the average relative deviations were 0.60% and 0.16%, respectively, and the maximum relative deviations were 8.57% and 7.60%, respectively.


Mean (no. 1–25, XBJ Injection)0.1400.1410.0010.600Mean (no. 26–65, FFSX Injection)2.0001.997−0.003−0.164
SEP (no. 1–25, XBJ Injection)0.006SEP (no. 26–65, FFSX Injection)0.059

PV: predicted values; Ea: absolute error; SEP: standard error of prediction; RD: relative deviation.

4. Discussion

In the past decade, process analysis technology (PAT) gradually became an important technology for production process controlling in chemical engineering, environmental, agricultural, food, and other fields, along with the acceptance of concept of “quality by design.” As one of the most common PAT technologies, NIR is now widely applied in these fields. However, either for TCM products or production process, the mainstream quality control method is still an offline analysis approach. In recent years, people come to recognize the advantages of the NIR-based method, i.e., fast, real-time, in situ, nondestructive, and accurate, and NIR shows great expectation in the quality control for TCM injections. Moreover, presently most quality control methods focus on the so-called bioactive components of TCM, while nearly take no consideration on other additive nonactive ingredients. For instance, Tween-80, as one of the most commonly used additives in TCM injection, might lead to potential quality risks of products, whereas it has not been paid much attention to the quality control. This work established a NIR-based quality control approach for Tween-80 in TCM injection, which can be used not only for the fast quality detection of Tween-80 in the terminal product of TCM injection but for the real-time detection of Tween-80 in the production process, due to its characteristics of rapid, in situ, and nondestructive. Moreover, the proposed method also can be used PAT technology with good adaptability in other TCM products and produce process.

For NIR modeling, the commonly used algorisms include multivariate linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLSR). MLR often produces unsatisfactory prediction performance due to the problem of collinearity of spectral variables; PCR only decomposes the spectral array to eliminate the noise while PLSR decomposes both the spectral array and the concentration array simultaneously, which not only overcomes the disadvantage of the PCR with only data of spectra decomposed but resolves problems of variable collinearity and model overfitting. Therefore, the PLS modeling method is used in this study to construct calibration model.

This work concerned the quality control of Tween-80 in TCM injection but did not promote the method in the application for other TCM dosage forms containing Tween-80. In our further study, the proposed NIR-based Tween-80 quality control method would be improved and developed to be applied in other TCM, and even in food, cosmetics, and other industries.

5. Conclusions

In this study, a method for rapidly determining the Tween-80 in TCM injection by NIR spectroscopy was established for the first time. The proposed method is simple and accurate, which may be suitable for application in the inspection in production processes and in final product quality assessment for TCM injection, thereby may promote the quality control of TCM injection and even expand the strategy for the quality control of the production process in the pharmaceutical industry.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Additional Points

Highlights. Rapid determination of Tween-80 in TCM injection by using near-infrared spectroscopy was established. This work could promote the quality control of TCM injection and expand the strategies for the quality control of the production process in the pharmaceutical industry.


Jin-Fang Ma and Tian-Ling Chen are the co-first authors.

Conflicts of Interest

The authors declare there are no conflicts of interest.


This work was funded by S&T Plan Project of Guangzhou (201803010104), the National Key R&D Program of China (2017YFC1703102), and Pearl River S&T Nova Program of Guangzhou (201610010113).


  1. Y. Xu, C. Liu, D. Dou, and Q. Wang, “Evaluation of anaphylactoid constituents in vitro and in vivo,” International Immunopharmacology, vol. 43, pp. 79–84, 2017. View at: Publisher Site | Google Scholar
  2. Y. B. Xu, T. G. Kang, D. Q. Dou, and H. X. Kuang, “The evaluation and optimization of animal model for anaphylactoid reaction induced by injections,” Asian Pacific Journal of Allergy and Immunology, vol. 33, no. 4, pp. 330–338, 2015. View at: Publisher Site | Google Scholar
  3. E. A. Coors, H. Seybold, H. F. Merk, and V. Mahler, “Polysorbate 80 in medical products and nonimmunologic anaphylactoid reactions,” Annals of Allergy, Asthma & Immunology, vol. 95, no. 6, pp. 593–599, 2005. View at: Publisher Site | Google Scholar
  4. H. Li, J. Deng, L. Deng, X. Ren, and J. Xia, “Safety profile of traditional Chinese herbal injection: an analysis of a spontaneous reporting system in China,” Pharmacoepidemiology and Drug Safety, vol. 28, no. 7, pp. 1002–1013, 2019. View at: Publisher Site | Google Scholar
  5. H. Li, S. Wang, Z. Yue, X. Ren, and J. Xia, “Traditional Chinese herbal injection: current status and future perspectives,” Fitoterapia, vol. 129, pp. 249–256, 2018. View at: Publisher Site | Google Scholar
  6. C. Wang, Q.-P. Shi, F. Ding et al., “Reevaluation of the post-marketing safety of Shuxuening injection based on real-world and evidence-based evaluations,” Drug Design, Development and Therapy, vol. 12, pp. 757–767, 2018. View at: Publisher Site | Google Scholar
  7. H. Y. Zhang, W. K. Wu, Y. C. Wei et al., “Safety status of solubilizing auxiliary materials commonly used for injection,” Shaanxi Journal of Traditional Chinese Medicine, vol. 32, no. 4, pp. 503–505, 2011. View at: Google Scholar
  8. B. J. Zhu, T. T. Li, Z. L. Zhou, and D. Luo, “The associativity study of polysorbate 80 in shenmai injection and haemolysis rate,” Tech Square Magazine, vol. 10, pp. 71–73, 2010. View at: Google Scholar
  9. X. Luo, Q. Wang, L. Zhou, Y. Dong, and Y. P. Jiang, “Study on relationship between tween-80 contained in injections of Chinese herbal medicine and anaphylactic reactions,” Australasian Dispute Resolution Journal, vol. 12, no. 3, pp. 160–166, 2010. View at: Google Scholar
  10. Y. T. Wang, C. Y. Li, Y. Yi et al., “Comparative study on pseudoanaphylactoid reactions induced by medicinal tween 80 and injectable tween80,” China Journal of Chinese Materia Medica, vol. 37, no. 13, pp. 1890–1898, 2012. View at: Publisher Site | Google Scholar
  11. FDA, Inactive Ingredient Search for Approved Drug Products, 2018,
  12. S. Q. Chen, Study on Clinical Application Risk Assessment of Xiyanping Injection and Revision Procedure of Its Specification, Beijing University of Chinese Medicine, Beijing, China, 2019.
  13. R. Yang, Q.-C. Lao, H.-P. Yu et al., “Tween-80 and impurity induce anaphylactoid reaction in zebrafish,” Journal of Applied Toxicology, vol. 35, no. 3, pp. 295–301, 2015. View at: Publisher Site | Google Scholar
  14. J. Puschmann, D.-H. Evers, C. C. Müller-Goymann, and M. E. Herbig, “Development of a design of experiments optimized method for quantification of polysorbate 80 based on oleic acid using UHPLC-MS,” Journal of Chromatography A, vol. 1599, pp. 136–143, 2019. View at: Publisher Site | Google Scholar
  15. L. X. Nie, Y. Chang, Z. Dai et al., “A new method for determination of polysorbate 80 in shengmai injection based on absorption coefficient,” Spectroscopy and Spectral Analysis, vol. 39, no. 1, pp. 199–203, 2019. View at: Google Scholar
  16. W. Li, L. Li, and Z. J. Chen, “Determination of CMC of tween-80 in Chinese medicine injection,” Journal of Liaoning University of TCM, vol. 14, no. 2, pp. 181–183, 2012. View at: Google Scholar
  17. Y. Wu, X. D. Li, R. C. Lin, and S. H. Jin, “Development of a size-exclusion HPLC method with evaporative light-scattering detection for the quantitation of polysorbate 80 in Houttuynia cordata injection,” Journal of AOAC International, vol. 93, no. 3, pp. 917–921, 2010. View at: Google Scholar
  18. W. Li, H. Han, L. Zhang, Y. Zhang, and H. Qu, “A feasibility study on the non-invasive analysis of bottled Compound E Jiao oral liquid using near infrared spectroscopy,” Sensors and Actuators B: Chemical, vol. 211, pp. 131–137, 2015. View at: Publisher Site | Google Scholar
  19. Z.-S. Wu, L.-W. Zhou, S.-Y. Dai, X.-Y. Shi, and Y.-J. Qiao, “Evaluation of the value of near infrared (NIR) spectromicroscopy for the analysis of glycyrrizhic acid in licorice,” Chinese Journal of Natural Medicines, vol. 13, no. 4, pp. 316–320, 2015. View at: Publisher Site | Google Scholar
  20. C. Y. Hou, L. Y. Duan, Y. L. Li et al., “Study on comprehensive evaluation of Ligustrum lucidum based on near infrared spectroscopy,” Journal of Hanan Institute of Science and Technology (Natural Science Edition), vol. 47, no. 1, pp. 47–52, 2019. View at: Google Scholar
  21. L. Yin, J. Zhou, D. Chen et al., “A review of the application of near-infrared spectroscopy to rare traditional Chinese medicine,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 221, no. 5, Article ID 117208, 2019. View at: Publisher Site | Google Scholar
  22. R. Peng, Z. He, T. Gou, J. Du, and H. Li, “Detection of parameters in solid state fermentation of Monascus by near infrared spectroscopy,” Infrared Physics & Technology, vol. 96, pp. 244–250, 2019. View at: Publisher Site | Google Scholar
  23. X. Q. Cao, C. Jia, L. F. Huang, X. L. Liu, and D. Wang, “Rapid determination of α-cyperone in cyperi rhizoma stir-fried with vinegar by near-infrared spectroscopy,” Chinese Journal of Experimental Traditional Medical Formulae, vol. 24, no. 7, pp. 27–32, 2018. View at: Google Scholar
  24. W. Nie, P. L. Zhu, H. L. Fang, B. Gong, and Y. C. He, “Quality evaluation of Callicarpa kwangtungensis from different origins by HPLC coupled with chemometrics,” Chinese Traditional and Herbal Drugs, vol. 48, no. 1, pp. 185–191, 2017. View at: Google Scholar
  25. F. Qu, D. Ren, J. Wang, Z. Zhang, N. Lu, and L. Meng, “An ensemble successive project algorithm for liquor detection using near infrared sensor,” Sensors, vol. 16, no. 1, p. 89, 2016. View at: Publisher Site | Google Scholar
  26. W. Pan, J. Ma, X. Xiao et al., “Near-infrared spectroscopy assay of key quality-indicative ingredients of Tongkang tablets,” AAPS PharmSciTech, vol. 18, no. 3, pp. 913–919, 2016. View at: Publisher Site | Google Scholar
  27. Z. L. Zhou, J. Li, S. Q. Huang et al., “Development of chemometric modelling in the application of NIR to the quality control of Chinese herbal medicine: literature review and future perspectives,” Chemical Industry and Engineering Progres, vol. 35, no. 6, pp. 1627–1645, 2016. View at: Google Scholar
  28. F. Allegrini, J. A. Fernández Pierna, W. D. Fragoso, A. C. Olivieri, V. Baeten, and P. Dardenne, “Regression models based on new local strategies for near infrared spectroscopic data,” Analytica Chimica Acta, vol. 933, pp. 50–58, 2016. View at: Publisher Site | Google Scholar
  29. X. Xiao, J. Ma, F. Ge et al., “Application of near-infrared spectroscopy for the rapid analysis of Lonicerae japonicae flos solution extracted by water,” Journal of Innovative Optical Health Sciences, vol. 7, no. 4, Article ID 1350063, 2014. View at: Publisher Site | Google Scholar
  30. J. A. Cayuela and J. F. García, “Sorting olive oil based on alpha-tocopherol and total tocopherol content using near-infra-red spectroscopy (NIRS) analysis,” Journal of Food Engineering, vol. 202, pp. 79–88, 2017. View at: Publisher Site | Google Scholar
  31. J. Hu, X. Ma, L. Liu, Y. Wu, and J. Ouyang, “Rapid evaluation of the quality of chestnuts using near-infrared reflectance spectroscopy,” Food Chemistry, vol. 231, pp. 141–147, 2017. View at: Publisher Site | Google Scholar
  32. F. Patrizia, D. L. Silvia, B. Remo et al., “Near infrared (NIR) spectroscopy-based classification for the authentication of Darjeeling black tea,” Food Control, vol. 100, pp. 292–299, 2019. View at: Publisher Site | Google Scholar
  33. L. F. Liu, J. Liang, J. F. Ma, S. S. Li, X. Xiao, and F. H. Ge, “Rapid determination of calycosin-7-glucoside content in extraction process of Astragalus by near infrared spectroscopy,” Jiangxi Journal of Traditional Chinese Medicine, vol. 397, no. 47, pp. 68–70, 2016. View at: Google Scholar
  34. C. Schaefer, D. Clicq, C. Lecomte, A. Merschaert, E. Norrant, and F. Fotiadu, “A process analytical technology (PAT) approach to control a new API manufacturing process: development, validation and implementation,” Talanta, vol. 120, no. 5, pp. 114–125, 2014. View at: Publisher Site | Google Scholar
  35. Y. Wu, Y. Jin, Y. Li, D. Sun, X. Liu, and Y. Chen, “NIR spectroscopy as a process analytical technology (PAT) tool for on-line and real-time monitoring of an extraction process,” Vibrational Spectroscopy, vol. 58, no. 1, pp. 109–118, 2012. View at: Publisher Site | Google Scholar
  36. A. Q. Vo, H. He, J. Zhang, S. Martin, R. Chen, and M. A. Repka, “Application of FT-NIR analysis for in-line and real-time monitoring of pharmaceutical hot melt extrusion: a technical note,” AAPS Pharmscitech, vol. 19, no. 8, pp. 3425–3429, 2018. View at: Publisher Site | Google Scholar
  37. L. X. Sun, J. P. Chen, G. M. Zhao, and M. Y. Li, “Research progress in application of chemometrics in food analysis,” Science and Technology of Food Industry, vol. 33, no. 7, pp. 444–452, 2012. View at: Google Scholar
  38. A. Kuriyama, J. Osuga, Y. Hattori, and M. Otsuka, “In-line monitoring of a high-shear granulation process using the baseline shift of near infrared spectra,” AAPS Pharmscitech, vol. 19, no. 2, pp. 710–718, 2017. View at: Publisher Site | Google Scholar
  39. V. R. Naidu, R. S. Deshpande, M. R. Syed, P. Deoghare, D. Singh, and P. S. Wakte, “PAT-based control of fluid bed coating process using NIR spectroscopy to monitor the cellulose coating on pharmaceutical pellets,” AAPS Pharmscitech, vol. 18, no. 6, pp. 2045–2054, 2017. View at: Publisher Site | Google Scholar
  40. J. Workman and L. Weyer, Practical Guide to Interpretive Near-Infrared Spectroscopy, CRC Press, Boca Raton, FL, USA, 2007.
  41. P. M. Santos and L. A. Colnago, “Comparison among MIR, NIR, and LF-NMR techniques for quality control of jam using chemometrics,” Food Analytical Methods, vol. 11, no. 7, pp. 2029–2034, 2018. View at: Publisher Site | Google Scholar
  42. J. Wang, X. Zhang, S. Sun, X. Sun, Q. Li, and Z. Zhang, “Online determination of quality parameters of dried soybean protein-lipid films (Fuzhu) by NIR spectroscopy combined with chemometrics,” Journal of Food Measurement and Characterization, vol. 12, no. 3, pp. 1473–1484, 2018. View at: Publisher Site | Google Scholar
  43. G. F. d. Lima, S. A. C. Andrade, V. H. d. Silva, and F. A. Honorato, “Multivariate classification of UHT milk as to the presence of lactose using benchtop and portable NIR spectrometers,” Food Analytical Methods, vol. 11, no. 10, pp. 2699–2706, 2018. View at: Publisher Site | Google Scholar
  44. W. Li, X. Yan, J. Pan, S. Liu, D. Xue, and H. Qu, “Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 218, pp. 271–280, 2019. View at: Publisher Site | Google Scholar
  45. C. Y. Xu, Z. L. Ye, D. S. Li, D. Z. Zho, and J. D. Liu, “Rapid analysis of naringin and neohesperidin by near infrared transmittance spectroscopy in extract of aurantii fructus,” Chinese Journal of Experimental Traditional Medical Formulae, vol. 11, no. 6, pp. 86–90, 2014. View at: Google Scholar
  46. O. A. Alrezj, K. Patchava, M. Benaissa, and S. A. Alshebeili, “Pre-processing to enhance the quantitative analysis of glucose from NIR and MIR spectra,” in Proceedings of the EMBEC & NBC, Springer, Tamper, Finland, June 2017. View at: Google Scholar
  47. J. T. Xue, Y. F. Liu, L. M. Ye et al., “Rapid and simultaneous analysis of five alkaloids in four parts of coptidis rhizoma by near-infrared spectroscopy,” Spectrochimica Acta A Molecular Biomolecular Spectroscopy, vol. 188, pp. 611–618, 2018. View at: Publisher Site | Google Scholar
  48. X. Zhuang, L. Wang, Q. Chen, X. Wu, and J. Fang, “Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools,” Science China Technological Sciences, vol. 60, no. 1, pp. 84–90, 2017. View at: Publisher Site | Google Scholar

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