Rapid detection method for content of surfactant lauryl sodium sulfate in toothpaste

Abstract

The invention provides a rapid detection method for the content of a surfactant lauryl sodium sulfate in toothpaste, and belongs to the technical field of daily chemical product detection. The method comprises the steps of sample preparation, reference value determination, near infrared spectrum acquisition, spectrum pretreatment, model establishment, model verification, content determination of a to-be-detected sample and the like. The method comprises the following steps: carrying out pretreatment-free scanning on opaque and transparent toothpaste samples by adopting a universal near-infrared spectrometer, storing an original near-infrared spectrum, preprocessing the spectrum, constructing a partial least square regression model, and obtaining a reference value through a titration method so as to train and verify the model. The model selects wave bands of 1192-1298 nm and 1640-1770 nm, and the number of factors is determined through the minimum PRESS value. Experiments prove that the method is high in prediction precision, the relative deviation is controlled within + / -3%, and the method has good fitting degree and popularization capability, is suitable for rapid detection and quality control of the K12 content in toothpaste products, and has the advantages of high efficiency, greenness and real-time performance.

Description

Technical Field

[0001] The invention belongs to the technical field of daily chemical product detection, and particularly relates to a method for quickly detecting the content of sodium lauryl sulfate, a surfactant, in toothpaste. Background Art

[0002] Toothpaste, a typical daily cosmetic, provides multiple functions, including oral cleansing, antibacterial deodorization, and teeth whitening. Its quality and safety have always been a key concern for regulators and consumers. Since the implementation of the Regulations on the Supervision and Administration of Cosmetics in 2021, toothpaste has been included in the scope of general cosmetics regulation, and the ingredient control, quality standards, and safety testing requirements for related products have become more stringent and standardized. Surfactants, one of the main functional ingredients in toothpaste, particularly sodium lauryl sulfate (K12), have become a core monitoring indicator due to their direct impact on foaming ability and cleaning effectiveness.

[0003] K12 is an anionic surfactant commonly used in toothpaste to provide foaming, cleaning, and emulsification functions. However, improperly controlling its addition can not only affect product performance but also irritate the oral mucosa, necessitating accurate control of its content. In particular, during actual production, K12 addition often fluctuates. Therefore, rapid and accurate testing of K12 content in different toothpaste batches on the production line is crucial for ensuring product quality and improving production efficiency.

[0004] Currently, the most commonly used method for K12 content testing is the two-phase titration method, which has been incorporated into national standards such as GB / T15963-2008 and GB / T 5173-2018 and has a certain degree of reliability and applicability. However, this method has significant limitations: First, the operation steps are cumbersome, requiring multiple solvent extractions and phase separations, which requires a high level of operator skill. Second, the detection cycle is long, making it difficult to meet the real-time and rapid feedback requirements of production sites. Furthermore, the method requires the use of toxic organic solvents such as chloroform, which not only poses a potential threat to the health of the operator but also imposes high ventilation and protective requirements on the experimental environment, clearly inconsistent with the current development trend of green testing.

[0005] As the daily chemical industry transitions toward automation, intelligence, and green processes, the demand for rapid, non-destructive testing of key components in raw materials and finished products is growing stronger. While traditional titration methods offer precise measurements, their time-consuming, highly polluting, and costly nature make them difficult to adapt to the urgent demands of modern production workshops for online analysis, rapid feedback, and the absence of chemical reagents.

[0006] In recent years, near-infrared spectroscopy (NIRS), a rapid, non-destructive, reagent-free analytical method, has been widely used in the pharmaceutical, food, and cosmetics industries. By collecting the near-infrared absorption spectrum of a sample and combining it with chemometric methods to develop models, NIRS enables simultaneous quantification of multiple components in complex systems. It offers significant advantages, including rapid detection, ease of operation, the need for sample pretreatment, and the ability to perform real-time online monitoring.

[0007] However, there are currently no established methods for the quantitative analysis of sodium lauryl sulfate (SLS) content in toothpaste using near-infrared spectroscopy (NIR) in the public literature or industry practice. NIR analysis in toothpaste, a multi-component, semi-solid matrix with complex optical properties, faces challenges such as poor model stability and difficulty in modeling. Therefore, developing a rapid, efficient, and environmentally friendly method for K12 content determination in toothpaste is not only of great practical significance, but also fills a gap in the application of NIR spectroscopy in this field. Summary of the Invention

[0008] In response to the problems existing in the prior art, the present invention provides a method for rapid detection of the content of sodium lauryl sulfate, a surfactant, in toothpaste. The method can realize efficient, accurate and green detection of the K12 content in toothpaste, and is suitable for on-site rapid analysis and production process quality control.

[0009] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for rapidly detecting the content of sodium lauryl sulfate, a surfactant, in toothpaste comprises the following steps: (1) Sample preparation: Prepare simulated toothpaste samples and collect actual samples of opaque and transparent toothpaste from different batches, and store them in sealed containers at room temperature; (2) Determination of reference value by titration: The sodium lauryl sulfate content of the standard toothpaste sample prepared in step (1) and the actual toothpaste sample is determined by titration as a reference value; (3) Near-infrared spectrum acquisition: The opaque toothpaste sample was directly placed in the sample cup for spectrum acquisition, and the transparent toothpaste sample was placed in a gold-plated reflector for spectrum acquisition. The spectrum acquisition used a Wantong near-infrared spectrometer to obtain the original spectrum of the toothpaste. The original spectrum of the opaque toothpaste and transparent toothpaste were then preprocessed to obtain the preprocessed spectra of the opaque toothpaste and transparent toothpaste samples; (4) Model establishment: The wavelengths of 1192-1298 nm and 1640-1770 nm were selected as the wavelength bands related to the characteristics of sodium dodecyl sulfate. The toothpaste simulation samples and some actual toothpaste samples were used as the calibration set, and the other samples not involved in the modeling were used as the validation set. The partial least squares method was used for modeling. The number of principal factors was determined by combining cross-validation, and independent quantitative models for opaque and transparent toothpaste were established respectively. (5) Model validation: Use the validation set to perform external validation of the model and calculate the relative deviation between the predicted value and the titration reference value; (6) Determination of the content of the sample to be tested: The content of sodium lauryl sulfate in the toothpaste sample to be tested was determined using the validated model.

[0010] In this invention, a transparent toothpaste sample is placed in a gold-plated reflector before near-infrared spectrum acquisition. This is mainly because transparent samples have high light transmittance. If the spectrum is collected directly, some near-infrared light may penetrate the sample and not be effectively reflected back to the detector, resulting in signal weakening, unstable optical path, and unclear spectral characteristics, affecting model accuracy. The gold-plated reflector has an extremely high infrared reflectivity and can efficiently reflect light that passes through the sample back, allowing the light to penetrate the sample multiple times, effectively extending the optical path, enhancing spectral absorption characteristics, and improving the signal-to-noise ratio and spectral reproducibility, thereby significantly improving the spectral acquisition quality and modeling stability of transparent samples.

[0011] Furthermore, the amount of sodium lauryl sulfate added to the toothpaste simulation sample in step (1) is between 50% and 150%.

[0012] Furthermore, the instrument parameters of the near-infrared spectrometer in step (3) are set as follows: the spectral scanning range is 400 to 2500 nm, and the resolution is set to 8 cm -1 , scan times 32 times.

[0013] Furthermore, the preprocessing method in step (3) is second-order derivative and standard normal variable correction.

[0014] Furthermore, the number of principal factors for the partial least squares modeling in step (4) is determined by minimizing the prediction error sum of squares PRESS.

[0015] Furthermore, the relative deviation between the calculated predicted value and the titration reference value in step (5) is controlled within ±3%.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. To address the technical difficulties of transparent toothpaste samples, such as weak near-infrared spectral signals and unstable optical paths due to their high light transmittance, this invention innovatively introduces a gold-plated reflector as an auxiliary optical component. This reflector has extremely high infrared reflectivity and can effectively reflect near-infrared light that penetrates the sample back to the sample, improving the repeatability of the optical path and the consistency of the absorption path, thereby significantly enhancing the intensity and stability of the spectral characteristic signal. This design not only improves the model's adaptability to transparent systems, but also broadens the application boundaries of near-infrared spectroscopy technology in complex, semi-transparent daily chemical products, resolving the industry's long-standing technical bottleneck of being unable to stably model transparent samples.

[0017] 2. This invention employs a cross-formulation modeling strategy. This involves not only using standard laboratory-prepared simulated samples for calibration modeling, but also incorporating real-world samples from different batches and with varying physical properties (transparent and opaque), thereby constructing a comprehensive dataset encompassing both formulation variations and process fluctuations. Based on this, independent regression models were developed for transparent and opaque toothpastes. After extensive external testing with validation samples, the deviations between the predicted values and the titration reference values were kept within ±3%. This strategy significantly improves the model's stability and generalizability, ensuring rapid and accurate determination of K12 content across different products and batches in actual industrial production, effectively meeting the rapid production needs of daily chemical companies.

[0018] 3. The present method achieves "zero sample pretreatment," meaning toothpaste samples can be directly placed in a cup and tested on the instrument without extraction, dilution, or other complex processing steps. Especially when combined with the in-situ scanning capabilities of the near-infrared instrument and an automated data processing model, the entire testing process can be completed within minutes. Compared to the complex chemical treatment process of traditional titration methods, this method significantly improves on-site testing efficiency, providing a practical and feasible technical means for online monitoring and closed-loop quality control of K12 content, and possessing strong engineering feasibility and production adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the original near-infrared spectrum of the opaque toothpaste sample of Example 1.

[0020] Figure 2 This is the original near-infrared spectrum of the transparent toothpaste sample of Example 1.

[0021] Figure 3 This is the spectrum of the opaque toothpaste sample after pretreatment of Example 1.

[0022] Figure 4 This is the spectrum of the transparent toothpaste sample after pretreatment in Example 1.

[0023] Figure 5 This is the relationship diagram between the PLS main factors and PRESS of the opaque toothpaste in Example 1.

[0024] Figure 6 This is a relationship diagram between the second-order derivative of the opaque toothpaste in Example 1 and the calculated value of the calibration set under standard normal variable correction preprocessing and the reference value.

[0025] Figure 7 This is the relationship diagram between the PLS main factors and PRESS of the transparent toothpaste in Example 1.

[0026] Figure 8 This is a relationship diagram between the second-order derivative of the transparent toothpaste in Example 1 and the calculated value of the calibration set under standard normal variable correction preprocessing and the reference value.

[0027] Figure 9 This is a relative deviation distribution diagram of the near infrared method and the potentiometric titration results in the opaque toothpaste sample of Example 1.

[0028] Figure 10 This is a relative deviation distribution diagram of the near-infrared method and the potentiometric titration results in the transparent toothpaste sample of Example 1. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work premise belong to the scope of protection of the present invention. In addition, it is worth noting that the raw materials involved in the present invention are all common commercially available products unless otherwise specified. Example

[0030] This embodiment provides a method for rapid detection of the content of sodium lauryl sulfate, a surfactant, in toothpaste, and the steps are as follows: (1) Sample preparation We prepared simulated toothpaste samples prepared in the laboratory and actual samples from different batches of toothpaste produced in the workshop. The simulated toothpaste samples prepared in the laboratory covered a range of K12 addition levels from 50% to 150%, ensuring that the model encompassed different sample concentrations. The workshop samples included both opaque toothpaste and transparent gel-type toothpaste.

[0031] (2) Titration reference value determination To obtain reference values for modeling, chemical determination of K12 content was performed on all simulated samples and workshop samples. The titration method according to standard QB / T 4970-2016 was used as the determination method for K12 reference values in this example.

[0032] (3) Near-infrared spectrum acquisition ① Sample processing: Opaque toothpaste does not require any pre-treatment. It is directly squeezed into a glass sample cup with a thickness of 1 / 2 of the sample cup. It must be uniform and free of bubbles. Then it is placed in the sample chamber of the near-infrared spectrometer for scanning. Transparent toothpaste does not require any pretreatment. The toothpaste sample is placed in a sample cup and placed in a gold-plated reflector with a thickness that covers the reflector. It is then placed in the sample chamber of the near-infrared spectrometer for scanning.

[0033] ② Spectral acquisition: Spectral acquisition was performed using a Wantong near-infrared spectrometer with the following instrument parameters: the spectral scanning band was 400–2500 nm and the resolution was set to 8 cm -1 , scan times 32 times. Figure 1 and Figure 2 These are the original spectra collected for opaque toothpaste and transparent toothpaste under the above conditions.

[0034] ③ Spectral preprocessing: The original spectra of opaque toothpaste and transparent toothpaste were preprocessed using the second derivative (SD) + standard normal variate (SNV) method. Figure 3 and Figure 4 These are the spectra of opaque toothpaste and transparent toothpaste samples after pretreatment.

[0035] (4) Model establishment ① Selection of modeling bands: Based on the absorption characteristics of the K12 molecular structure and the response rules of the near-infrared spectral bands, two bands closely related to K12 were selected for model variable selection. The two ranges of 1192-1298 nm and 1640-1770 nm were selected for model establishment, corresponding to the second-order and first-order frequency harmonics of the C-H bond in long-chain alkyl groups in the near-infrared, respectively.

[0036] ②Calibration set and validation set The prepared toothpaste simulation samples plus different batches of toothpaste samples from the workshop were used as the calibration set to establish a quantitative calibration model for sodium lauryl sulfate in toothpaste. In addition, different batches of toothpaste samples from the workshop that did not participate in the modeling were used as the validation set to verify the predictive ability of the established calibration model.

[0037] ③Establishment of quantitative model Partial least squares (PLS) was used to model and analyze the spectral data and K12 reference values, with separate regression models established for opaque and transparent toothpastes. Prior to modeling, the spectral data of all calibration samples underwent a combined preprocessing using second-order derivatives and standard normal variate correction to enhance spectral peak characteristics and reduce background drift and scattering interference, ultimately improving modeling quality.

[0038] During the PLS modeling process, cross-validation is used to determine the optimal number of modeling factors. As the number of PLS factors increases, the model's predicted residual error sum of squares (PRESS) decreases and then increases. The number of factors that minimizes the PRESS value is generally considered the optimal number of principal components for the model.

[0039] For the opaque toothpaste sample, the model selected 12 main factors, and the calibration set fitting correlation coefficient (R 2 ) is 0.9957, indicating that the model fits well. Figure 5 As shown in the figure, the relationship curve between PRESS value and the number of factors shows that PRESS reaches the lowest when the number of factors is 12; Figure 6 The figure shows the fitting relationship between the calculated values of the calibration set samples and the titration reference values under the conditions of second-order derivative and SNV pretreatment. The scatter points are densely distributed near the diagonal, indicating that the model has high prediction accuracy and excellent fitting degree.

[0040] For the transparent toothpaste sample, the model selected 11 main factors, and the calibration set correlation coefficient (R 2 ) is 0.9958, which also has good fitting performance. Figure 7 The figure below shows the change of PLS factor number and PRESS value in the transparent toothpaste model. It can be seen that when the number of factors is 11, PRESS is the smallest and the model is optimal. Figure 8 The figure is a comparison chart of the corresponding calibration set predicted value and reference value, with regular scatter distribution and high degree of fit.

[0041] The above modeling results show that the SD+SNV preprocessing and PLS algorithm can effectively extract the K12 feature information in the toothpaste sample spectrum, and establish a near-infrared prediction model with high accuracy and strong stability, which is suitable for opaque and transparent toothpaste systems respectively.

[0042] (5) Model validation To verify the predictive power and practical applicability of the established PLS model, spectra were collected from different batches of toothpaste samples from the workshop as a validation set. The results were analyzed using the established model and compared with the measured values obtained by titration. Independent validation was performed on both an opaque toothpaste model and a transparent toothpaste model.

[0043] A total of 30 opaque toothpaste samples were validated. The model's predicted values were compared with reference values measured by titration, and relative deviations were calculated. The validation results showed that the relative deviations between the predicted and measured values for all samples were within ±3%, demonstrating the model's high prediction accuracy. Table 1 lists the measured values (potentiometric titration values), predicted values (near-infrared measurement values), and their absolute and relative deviations for the opaque toothpaste samples.

[0044] Table 1 Comparison of near infrared test values and potentiometric titration values of opaque toothpaste model

[0045] Figure 9 The distribution of relative deviations between the near-infrared method's predicted values and the potentiometric titration method's measured values for opaque toothpaste samples is shown. The figure shows that the relative deviations for all samples are concentrated within a ±3% range and exhibit a nearly symmetrical bell-shaped trend, indicating that the model's errors are highly random and lack systematic bias.

[0046] Similarly, the transparent toothpaste validation set consisted of 30 samples, using the same testing procedure as the opaque samples. The validation results showed that the relative deviation between the predicted and measured values for the transparent samples was also within ±3%. Table 2 shows the measured values (potentiometric titration values), predicted values (near-infrared test values), and their absolute and relative deviations for the transparent toothpaste samples.

[0047] Table 2 Comparison of near infrared test values and potentiometric titration values of transparent toothpaste model

[0048] Figure 10 The relative deviation distribution of transparent toothpaste samples is shown in the figure. The figure shows that the relative deviations of all samples are concentrated within the range of ±3% and exhibit a nearly symmetrical bell-shaped trend, indicating that the model error is highly random and lacks systematic bias.

[0049] Judging from the above statistical results, the overall error distribution of the verification samples of the two models is stable, with no obvious systematic deviations or outliers, indicating that the established models not only fit well on the training set, but also have strong predictive capabilities in samples that are not involved in modeling, and have good practical application value.

[0050] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for rapidly detecting the content of sodium lauryl sulfate, a surfactant, in toothpaste, comprising the following steps: (1) Sample preparation: Prepare simulated toothpaste samples and collect actual samples of opaque and transparent toothpaste from different batches, and store them in sealed containers at room temperature; (2) Titration reference value determination: titration method is used to determine the sodium lauryl sulfate content of the toothpaste simulation sample and the actual toothpaste sample prepared in step (1) as the reference value; (3) Near-infrared spectrum acquisition: The opaque toothpaste sample was directly placed in the sample cup for spectrum acquisition, and the transparent toothpaste sample was placed in a gold-plated reflector for spectrum acquisition. The spectrum acquisition used a Wantong near-infrared spectrometer to obtain the original spectrum of the toothpaste. The original spectrum of the opaque toothpaste and transparent toothpaste were then preprocessed to obtain the preprocessed spectra of the opaque toothpaste and transparent toothpaste samples; (4) Model establishment: The wavelengths of 1192-1298 nm and 1640-1770 nm were selected as the wavelength bands related to the characteristics of sodium dodecyl sulfate. The toothpaste simulation samples and some actual toothpaste samples were used as the calibration set, and the other samples not involved in the modeling were used as the validation set. The partial least squares method was used for modeling. The number of principal factors was determined by combining cross-validation, and independent quantitative models for opaque and transparent toothpaste were established respectively. (5) Model validation: Use the validation set to perform external validation of the model and calculate the relative deviation between the predicted value and the titration reference value; (6) Determination of the content of the sample to be tested: The content of sodium lauryl sulfate in the toothpaste sample to be tested was determined using the validated model.

2. The method according to claim 1, wherein: The amount of sodium lauryl sulfate added to the toothpaste simulation sample in step (1) is between 50% and 150%.

3. The method according to claim 1, wherein: The instrument parameters of the near-infrared spectrometer in step (3) are set as follows: the spectral scanning range is 400 to 2500 nm, and the resolution is set to 8 cm -1 , scan times 32 times.

4. The method according to claim 1, wherein: The preprocessing methods described in step (3) are second-order derivative and standard normal variate correction.

5. The method according to claim 1, wherein: The number of principal factors in the partial least squares modeling described in step (4) is determined by minimizing the prediction error sum of squares PRESS.

6. The method according to claim 1, wherein: The relative deviation between the calculated predicted value and the titration reference value described in step (5) is controlled within ±3%.

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