Quality Evaluation of Oviductus Ranae Based on PUFAs Using HPLC Fingerprint Techniques Combined with Chemometric Methods

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Establishment of Male and Female Eucommia Fingerprints by UPLC Combined with OPLS-DA Model and Its Application

Eucommia ulmoides Oliver is a dioecious plant, which plays an important role in traditional Chinese medicine. However, there has not yet been any research on male and female E. ulmoides. The UPLC fingerprints and OPLS-DA approach were able to quickly and easily identify and quantify E. ulmoides and differentiate between the male and female fingerprints. In this study, we optimized the UPLC conditions and analyzed them to investigate fingerprints of twenty-four extracts of Eucommiae Cortex (EC) and twenty-four extracts of Eucommiae Folium (EF) under optimal conditions. It was demonstrated that thirteen and twelve substances were possible chemical markers for EC and EF male and female discrimination and that the level of these markers - chlorogenic acid and protocatechuic acid - was many times higher in male than in female. This approach offered a reference for quality control and precise treatment of male and female E. ulmoides in the clinic.

Unsaturated fatty-acid based HPLC fingerprints in combination with quantitative analysis of multi-components by single-marker for the classification of Rana chensinensis ovum

Comprehensive quality evaluation strategy was established for Rana chensinensis ovum based on analytical chemistry and chemometrics.

Determination and Chemometrics-Assisted Comparative Analysis of Active Components in Different Tissue of Rana chensinensis

In this study, the chemical composition of different tissues of Rana temporaria chensinensis David derived from the same individual was analyzed by comparative approach. First, pre-column derivatization combined with high performance liquid chromatography (HPLC) was established to determine the content of 1-methyl hydantoin in samples, which used S1–S5 samples. The results indicated that 1-methyl hydantoin was determined in Oviductus Ranae (OR), Rana chensinensis ovum (RCO), Rana chensinensis meat (RCM), and Rana chensinensis skin (RCS), except for Rana chensinensis bone (RCB). Moreover, the content of it in RCS was the highest. In addition, the contents of six polyunsaturated fatty acids (PUFAs) in different tissues of Rana chensinensis were measured by HPLC, including eicosapentaenoic acid (EPA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA) and oleic acid (OA). The results indicated that OR, RCO, RCM, RCS, and RCB all contained the above six PUFAs. With the aid of chemometrics methods, the results of principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) combined with the sequencing results of the total PUFAs content of each sample, showed that different tissues of Rana chensinensis could be divided into four categories, and the RCO sample was divided into one category because of the highest PUFAs content, which was a good source of PUFA. For comparison, OR and other tissue from the perspective of PUFAs, we also established OPLS-DA models of them. It could be found that the RCM was the most similar to the OR in the diversity and content of PUFAs. This study provided a theoretical basis for the further development and utilization of RCO, RCM, RCS, and RCB as by-products of OR.

Antioxidant Activity Evaluation of Oviductus Ranae Protein Hydrolyzed by Different Proteases

As nutrition and a health tonic for both medicine and food, the protein content of Oviductus Ranae is more than 40%, making it an ideal source to produce antioxidant peptides. This work evaluated the effects of six different proteases (pepsin, trypsin, papain, flavourzyme, neutral protease and alcalase) on the antioxidant activity of Oviductus Ranae protein, and analyzed the relationship between the hydrolysis time, the degree of hydrolysis (DH) and the antioxidant activity of the enzymatic hydrolysates. The results showed that the antioxidant activity of Oviductus Ranae protein was significantly improved and the optimal hydrolysis time was maintained between 3-4 h under the action of different proteases. Among them, the protein hydrolysate which was hydrolyzed by pepsin for 180 min had the strongest comprehensive antioxidant activity and was most suitable for the production of antioxidant peptides. At this time, the DH, the DPPH radical scavenging activity, the absorbance value of reducing power determination and the hydroxyl radical scavenging activity corresponding to the enzymatic hydrolysate were 13.32 ± 0.24%, 70.63 ± 1.53%, 0.376 ± 0.009 and 31.96 ± 0.78%, respectively. Correlation analysis showed that there was a significant positive correlation between the hydrolysis time, the DH and the antioxidant activity of the enzymatic hydrolysates, further indicating that the hydrolysates of Oviductus Ranae protein had great antioxidant potential. The traditional anti-aging efficacy of Oviductus Ranae is closely related to the scavenging of reactive oxygen species, and its hydrolysates have better antioxidant capacity, which also provides support for further development of its traditional anti-aging efficacy.

Exploitation of HPLC Analytical Method for Simultaneous Determination of Six Principal Unsaturated Fatty Acids in Oviductus Ranae Based on Quantitative Analysis of Multi-Components by Single-Marker (QAMS)

As one of the featured products in northeast China, Oviductus Ranae has been widely used as a nutritious food, which contains a variety of bioactive unsaturated fatty acids (UFAs). It is necessary to establish a scientific and reliable determination method of UFA contents in Oviductus Ranae. In this work, six principal UFAs in Oviductus Ranae, namely eicosapentaenoic acid (EPA), linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA) and oleic acid (OA), were identified using UPLC-MS/MS. The UFAs identified in Oviductus Ranae were further separated based on the optimized RP-HPLC conditions. Quantitative analysis of multi-components by single-marker (QAMS) method was implemented in content determination of EPA, ALA, DHA, ARA and OA, where LA was used as the internal standard. The experiments based on Taguchi design verified the robustness of the QAMS method on different HPLC instruments and chromatographic columns. The QAMS and external standard method (ESM) were used to calculate the UFA content of 15 batches of Oviductus Ranae samples from different regions. The relative error (r < 0.73%) and cosine coefficient showed that the two methods obtained similar contents, and the method validations met the requirements. The results showed that QAMS can comprehensively and effectively control the quality of UFAs in Oviductus Ranae which provides new ideas and solutions for studying the active components in Oviductus Ranae.

Study on the Fingerprints and Quality Evaluation of Angelica Sinensis Radix by HPLC Coupled With Chemometrics Based on Traditional Decoction Process of ACPTCM

Objective

To establish a HPLC fingerprints evaluation method for Angelica Sinensis Radix (ASR) based on traditional decoction process of Ancient Classical Prescriptions of Traditional Chinese Medicine (ACPTCM).

Methods

The fingerprints of 10 batches of ASR were further evaluated by chemometrics methods. The similarity analyzed with "Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine 2004A," and hierarchical clustering analysis (HCA) and principal component analysis (PCA) were performed by SPSS (version 22.0, SPSS Inc., Chicago, IL, USA).

Results

There were 12 common peaks, and the similarity degrees of 10 batches of samples were more than 0.923 and showed that all the samples from different origins were of good consistency. The samples were divided into 4 clusters by HCA. The results of PCA showed that the 3 factors were chosen, the quality of samples could be evaluated basically. The comprehensive score results show that the ASR with Lot.Nos.DG-18007, DG-18008 in Weiyuan County, Gansu and DG-18009 produced in Minle County, Gansu Province rank among the top 3 in all samples.

Conclusions

These results demonstrated that the combination of HPLC chromatographic fingerprint and chemometrics offers an efficient and reliable approach for quality evaluation of ASR from different sources as Ancient Classical Prescriptions ingredients.

Rana chensinensis Ovum Oil Based on CO2 Supercritical Fluid Extraction: Response Surface Methodology Optimization and Unsaturated Fatty Acid Ingredient Analysis

Rana chensinensis ovum oil (RCOO) is an emerging source of unsaturated fatty acids (UFAs), but it is lacking in green and efficient extraction methods. In this work, using the response surface strategy, we developed a green and efficient CO2 supercritical fluid extraction (CO2-SFE) technology for RCOO. The response surface methodology (RSM), based on the Box-Behnken Design (BBD), was used to investigate the influence of four independent factors (pressure, flow, temperature, and time) on the yield of RCOO in the CO2-SFE process, and UPLC-ESI-Q-TOP-MS and HPLC were used to identify and analyze the principal UFA components of RCOO. According to the BBD response surface model, the optimal CO2-SFE condition of RCOO was pressure 29 MPa, flow 82 L/h, temperature 50 °C, and time 132 min, and the corresponding predicted optimal yield was 13.61%. The actual optimal yield obtained from the model verification was 13.29 ± 0.37%, and the average error with the predicted value was 0.38 ± 0.27%. The six principal UFAs identified in RCOO included eicosapentaenoic acid (EPA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), arachidonic acid (ARA), linoleic acid (LA), and oleic acid (OA), which were important biologically active ingredients in RCOO. Pearson correlation analysis showed that the yield of these UFAs was closely related to the yield of RCOO (the correlation coefficients were greater than 0.9). Therefore, under optimal conditions, the yield of RCOO and principal UFAs always reached the optimal value at the same time. Based on the above results, this work realized the optimization of CO2-SFE green extraction process and the confirmation of principal bioactive ingredients of the extract, which laid a foundation for the green production of RCOO.

High-performance liquid chromatography-based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr

A simple and efficient high-performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.