Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice

Surface-enhanced Raman spectroscopy (SERS), coupled with characteristic peak screening methods, was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. Magnesium sulfate (MgSO₄), primary secondary amine (PSA), and C₁₈ were used to purify the rice extraction. A successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six Raman peaks such as 671, 834, 1016, 1114, 1436, and 1444 cm^-1 were selected by the SPA and SVM models and had better performance using six peaks (only 0.92% of the full spectra variables) with R²_p = 0.97, RMSEP = 2.89 and RPD = 4.26, and the experiment time for a sample was accomplished within 10 min. Recovery for five unknown concentration samples was 97.45-103.96%, and T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that SERS, combined with characteristic peak screening methods, can be applied to rapidly monitor the chlorpyrifos residue in rice.

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

FT-IR combined with chemometrics in the quality evaluation of Nongxiangxing baijiu

Recently, there has been an increasing demand for developing a reliable method to assess the quality of liquor in the baijiu industry quickly and accurately. The present study sought to establish a strategy for rapid quantitative analysis of the primary flavor components in Nongxiangxing baijiu. Under the experimental conditions, 7 of the 10 major flavor components in Nongxiangxing baijiu could be quantified effectively, such as ethyl butyrate (R²p = 0.9942), ethyl lactate (R²p = 0.9438), n-butanol (R²p = 0.9048), isobutanol (R²p = 0.9696), acetic acid (R²p = 0.9600), butyric acid (R²p = 0.8448), caproic acid (R²p = 0.9971). This result indicates that FT-IR combined with quantitative chemometric modeling could be a potential approach for rapid quality assessment of Nongxiangxing baijiu. Overall, this study provides a theoretical basis for subsequent related studies on Nongxiangxing baijiu.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500-4,250 cm^-1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R ²) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g^-1, R ² of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g^-1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

Rapid Identification of Easily-Confused Mineral Traditional Chinese Medicine (TCM) Based on Low-Wavenumber Raman and Terahertz Spectroscopy

With the unique advantages of mineral TCMs gradually emerging in clinical treatment, health care, and precaution, it has played an important role in the international medical market. Commonly, mineral TCMs with similar appearance and different processing methods have different effects, but they are easy to be confused in preparation, storage, transportation, and other links, which affects the use and causes related problems. In this paper, six kinds of easily confused mineral TCMs, including coral skeleton, ophicalcitum, calamine, matrii sulfas exsiccatus, gypsum, and alumen, are rapidly characterized using Raman spectroscopy, which can be distinguished with different Raman peaks at 0–300 cm−1 due to the different lattice structure. The THz spectra of these mineral TCMs show that different mineral TCMs have different THz absorption coefficients at 0.3–2.0 THz. Furthermore, compared with the ineffectiveness of the Raman spectrum for differentiating mineral TCMs prepared with disparate processing methods, the terahertz absorption spectrum plays an active role in making up the limitation of low-wavenumber Raman spectroscopy. The combination of low-wavenumber Raman and THz spectroscopy provides a simple and feasible scheme for the identification of mineral TCMs, which could play an important role in the quality control of mineral TCMs.

Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy

In this study, a classification model was established based on near-infrared spectroscopy and random forest method to accurately distinguish three samples of Schisandra chinensis from different habitats. At the same time, the feasibility of fast and effective prediction of polysaccharide contents in Schisandra chinensis by near-infrared spectroscopy combined with chemometrics was evaluated. In this paper, phenol sulfuric acid method was used to determine the content of total polysaccharides in samples, and partial least squares regression algorithm was used to link the spectral information with the reference value. Different spectral pretreatment methods were used to optimize the model to improve its predictability and stability. The results showed that random forest could distinguish these samples accurately, with an accuracy of 97.47%. In the established prediction model, the RMSEC of the optimal model calibration set is 0.0012, and the coefficient of determination R is 0.9976. The RMSEP of prediction set is 0.0024, the coefficient of determination R is 0.9922, and the RPD is 11.36. In general, the method has good stability and applicability, which provides a new analytical method for the identification of Schisandra chinensis origin and quality evaluation.

Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

Surface enhanced Raman spectroscopy based on rapid pretreatment combined with Chemometrics was used to determine chlorpyrifos residue in tea. Au nanoparticles were used to as enhance substrate. Different dosages of PSA and NBC were investigated to eliminate the tea substrate influence. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic peaks, and compared to full spectra variables and the experiment selected variables. The results showed that PSA of 80 mg and NBC of 20 mg was an excellent approach for rapid detecting. CARS - PLS had better accuracy and stability using only 1.7% of full spectra variables. SVM model achieved better performance with R²_p = 0.981, RMSEP = 1.42 and RPD = 6.78. Recoveries for five unknown concentration samples were 98.47 ~ 105.18% with RSD - 1.53% ~ 5.18%. T-test results showed that t value was 0.720, less than t0.05,4 = 2.776, demonstrating that no clear difference between the real value and predicted value. The detection time of a single sample is completed within 15 min. This study demonstrated that SERS coupled with Chemometrics and QuEChERS may be employed to rapidly examine the chlorpyrifos residue in tea towards its quality and safety monitoring.

Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.