Application of near infrared spectroscopy for rapid determination the geographical regions and polysaccharides contents of Lentinula edodes

Effect of white jade snail secretion on antioxidant capacity and intestinal microbial diversity in mouse model of acute gastric ulcer

BACKGROUND

In the present work, acute gastric ulcer models were constructed by administering hydrochloric acid/ethanol. The mice ingested white jade snail secretion (WJSS) through gastric infusion. Ulcer areas in gastric tissue were recorded, and malondialdehyde (MDA) and superoxide dismutase (SOD) were also measured. Notably, high-throughput 16S rDNA analysis of intestinal flora and determination of amino acid composition in feces were performed to understand the effect of WJSS on model mice.

RESULTS

Compared with the control group, the ulcer area in the WJSS low-, medium- and high-concentration groups declined by 28.02%, 39.57% and 77.85%, respectively. MDA content decreased by 24.71%, 49.58% and 64.25%, and SOD relative enzyme activity fell by 28.19%, 43.37% and 9.60%, respectively. The amounts of amino acids in the low-, medium- and high-concentration groups were slightly lower, and probiotic bacteria such as Bacteroidetes and Lactobacillales increased in different-concentration WJSS groups. Adding WJSS contributes to the establishment of beneficial intestinal flora and the absorption of amino acids.

CONCLUSION

Our results showed that WJSS has a beneficial effect on inhibiting hydrochloric acid-ethanolic gastric ulcers, suggesting that WJSS has excellent potential as a novel anti-ulcer agent. Combined with ulcer area, MDA content, SOD content, gut probiotics and other indicators, a high concentration of WJSS had the best protective effect on acute gastric ulcer. © 2023 Society of Chemical Industry.

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning

Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L. edodes from very limited sources, and thus could not achieve high accuracy for large samples from more varied sources. Strictly, there is a nonlinear relationship between NIR spectral data and chemical label values, and traditional modeling methods for NIR data analysis have problems such as insufficient feature learning ability and difficulty in training. The deep learning model has excellent nonlinear modeling ability and generalization capacity, which is very suitable for analyzing larger samples. In this study, we constructed a novel framework with deep learning techniques on the NIR analysis of the content of polysaccharide in L. edodes. The siPLS model was established based on the combination of the bands 4797-3995 cm-1 and 6401-5600 cm-1, while the one-dimensional convolutional neural network (1D-CNN) model was established with improved feature in the treatment of the spectral data. The comparative experimental results showed that the 1D-CNN model (R2pre = 95.50%; RMSEP =0.1875) outperformed the siPLS model (R2pre = 87.89%, RMSEP = 0.6221). As such, this work has demonstrated that NIR spectroscopy with the integration of deep learning can provide more accurate quantification of polysaccharide in L. edodes. Such method can be very useful for nutritional grading and quality control of diverse L. edodes in the market.

Characterization of Novel Exopolysaccharides from Enterococcus hirae WEHI01 and Its Immunomodulatory Activity

Exopolysaccharide (EPS) from probiotic Enterococcus hirae WEHI01 was isolated and purified by anion exchange chromatography and gel chromatography, the results of which show that the EPS consists of four fractions, namely I01-1, I01-2, I01-3, and I01-4. As the main purification components, I01-2 and I01-4 were preliminarily characterized for their structure and their immunomodulatory activity was explored. The molecular weight of I01-2 was 2.28 × 104 Da, which consists mainly of galactose, and a few other sugars including glucose, arabinose, mannose, xylose, fucose, and rhamnose, while the I01-4 was composed of galactose only and has a molecular weight of 2.59 × 104 Da. Furthermore, the results of an evaluation of immunomodulatory activity revealed that I01-2 and I01-4 could improve the viability of macrophage cells, improve phagocytosis, boost NO generation, and encourage the release of cytokines including TNF-α and IL-6 in RAW 264.7 macrophages. These results imply that I01-2 and I01-4 could improve macrophage-mediated immune responses and might be useful in the production of functional food and medications.

Rapid Determination of Pachymic Acid Content by Near-Infrared Spectroscopy

In this paper, a rapid model for the determination of pachymic acid content in Poria was established by partial least squares (PLS) regression and near-infrared spectroscopy (NIR). During the research, a total of 108 batches of Poria samples from different producing regions were used, while their corresponding pachymic acid contents by high-performance liquid chromatography (HPLC) were adopted as reference. These samples were divided randomly into calibration sets for model establishment and validation sets for model validation. The test results from the calibration set showed that the best preprocessing method of the NIR spectra model was the standard normal variate (SNV) + second derivatives (SD), and the most suitable number of principal factors was 9. In this model, the coefficient of determination of the calibration set ( $r_c^2$ ) and validation set ( $r_v^2$ ) was 0.915 and 0.917, respectively. Meanwhile, the root mean square error of calibration (RMSEC) and the root mean square error of validation (RMSEP) with the calibration set were 0.051 mg/g and 0.054 mg/g, respectively. These results indicated this model could rapidly and reliably predict the pachymic acid content in Poria and increase the determination efficiency of pachymic acid in Poria. This is conducive to promote the development of industry.

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.

Hypoglycemic effect of Taraxacum officinale root extract and its synergism with Radix Astragali extract

Taraxacum officinale (dandelion) and Radix Astragali are traditional medicinal and edible plants with high nutritional value. In this study, the synergistic hypoglycemic effect of DRE and Radix Astragali extract (RAE) was evaluated. Our results showed that water extract of dandelion (DRE-w), mainly containing polysaccharides (63.92 ± 1.82 mg/g), total flavonoid (2.57 ± 0.06 mg/g), total phenolic compounds (8.93 ± 0.34 mg/g), and saponins (0.54 ± 0.05 mg/g), exhibited significantly inhibitory effect on α-glucosidase and α-amylase. DRE-w and RAE had synergistic hypoglycemic effect; we found that DRE-w and its combination with RAE could relieve the state of insulin resistance in IR-HepG2 cells. The combination could more significantly increase the glucose consumption and intracellular glycogen content, and improve the activity of hexokinase and pyruvate kinase in IR-HepG2 cells. In summary, DRE and its combination with RAE can be developed as the drugs or functional foods for diabetes prevention and treatment.

A Raman spectroscopy analysis method for rapidly determining saccharides and its application to monitoring the extraction process of Wenxin granule manufacturing procedure

Saccharides are the major constituents of many herbs, and they are often utilized as quality indicators of many botanical drugs, such as Chinese medicines. A method for the rapid determination of saccharides in the in-process extract solutions is beneficial for process monitoring and ensuring consistency in the quality of the end-products during the manufacturing of Chinese medicines. In this work, a method based on Raman spectroscopy and a competitive adaptive reweighted sampling-partial least squares (CARS-PLS) model was established for the rapid quantification of saccharides. The accuracy and precision of this method were confirmed by employing one monosaccharide (glucose), one oligosaccharide (maltotriose), and two polysaccharides (Codonopsis radix polysaccharides and Polygonati rhizome polysaccharides) as reference substances. The determined results correlated well with the reference values of the four substances with the coefficient of determination of prediction (R_p²) ≥ 0.9939 and the root-mean-square error of prediction (RMSEP) ≤ 1.1052 mg/mL. Then, the method was applied to monitoring the simulated extraction process for Wenxin granule manufacture using total saccharides as a quality indicator. The CARS-PLS model exhibited satisfactory fitting and predictive capability, with R_p² and RMSEP values of 0.9743 and 1.4931 mg/mL, respectively. Our work demonstrated that Raman spectroscopy coupled with chemometrics can offer a reliable and nondestructive alternative for the determination of different types of saccharides, in addition to being useful for real-time monitoring of the extraction process during the manufacturing of Wenxin granules. The presented approach is expected to be applicable to other Chinese medicines.

Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method

Polysaccharide is the main active compound of Lilium, and showed many activities, such as hypoglycemic, antioxidant, immune-modulatory. There are three types' Lilium in China market, i.e. Lilium lancifolium Thunb (JD), Lilium davidiivar. Unicolor Salisb (L. davidii var)(LZBH), and Lilium brownii F.E. Brown var. viridulum Baker (BH). Near infrared spectroscopy (NIR) technique has become popular in the fields of quality control, due to its advantages, such as fast, non-destructive, and can detect several ingredients, simultaneously. In this study, a classification model was established based on NIR technique and random forest method to accurately distinguish three types' Lilium species, and the classification accuracy reached 94.37%. Furthermore, taking the effects of neighbor wavelength into account, a new weighted partial least square algorithm was proposed to establish an accurate and quantitative model for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R²) and low root mean square errors of prediction (RMSEP) were, 0.9455 and 0.9098, respectively. The obtained results showed that combined NIR technique with chemometrics was an effective and green method for quality control.