Evaluate how steaming and sulfur fumigation change the microstructure, physicochemical properties and in vitro digestibility of Gastrodia elata Bl. starch

E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process

Gastrodia elata (GE) is traditionally subjected to steaming, and steaming duration plays a crucially important role in determining GE quality. This study examined the variations in bioactive components during the steaming process and proposed the utilization of electronic eye and Fourier Transform near-infrared (FT-NIR) spectroscopy for quality assessment. The findings revealed that the levels of parishin E parishin B, parishin A, and gastrodin initially rose and subsequently declined, while 4-Hydroxybenzyl alcohol exhibited a rapid decrease followed by stabilization. With prolonged steaming, the brightness of GE decreased, while the red and yellow tones became more pronounced and the color saturation increased. FT-NIR divided the steaming process into three stages: 0 min (raw GE), 0-9 min (partially steamed GE), and 9-30 min (fully steamed GE), and the partial least squares regression models effectively predicted the levels of five components. Overall, this study provided valuable insights into quality control in food processing.

Selection and application of aptamers for p-hydroxybenzyl hydrogen sulfite after Gastrodia elata Bl. fumigated with sulfur

Gastrodia elata Bl. is a widely used traditional Chinese medicine known for its medicinal properties. However, during the drying process, G. elata is often fumigated with sulfur to prevent corrosion and improve its appearance. Sulfur-fumigation can result in a reduction in the effective components of the herb and can also be hazardous to human health due to the remaining sulfur dioxide. Sulfur-fumigation of G. elata poses a significant challenge to both end-users and researchers. The detection of p-hydroxybenzyl hydrogen sulfite (p-HS) is a useful tool in determining whether G. elata has been fumigated with sulfur. Unfortunately, the current method for detecting p-HS is costly and requires sophisticated instruments. Therefore, there is a need to develop a more cost-effective and user-friendly method for the detection of p-HS. This study utilized the Capture-SELEX technique to screen high-affinity aptamers for p-HS, which were subsequently characterized by isothermal titration calorimetry (ITC). An aptamer sequence (seq 6) with a high affinity of Kd = 26.5 μM was obtained following 8 rounds of selection against p-HS. With the aptamer serving as the recognition element and gold nanoparticles as the colorimetric indicator, a simple and efficient colorimetric sensor was developed for the specific detection of p-HS. This detection method exhibited a limit of detection of 1 μg/ml, while the p-HS recoveries demonstrated a range of between 88.5 % and 105 % for samples of G. elata obtained in the market. In summary, the aptamer exhibited a high affinity for p-HS, and the sensor developed through the use of a colloidal gold detector based on nucleic acid aptamer can be utilized for rapid detection of sulfur-fumigated G. elata. With these findings, this research paper provides valuable scientific insights and highlights significant potential for future studies in this area.

Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.

Melatonin Treatment Maintains the Quality of Fresh-Cut Gastrodia elata under Low-Temperature Conditions by Regulating Reactive Oxygen Species Metabolism and Phenylpropanoid Pathway

The application of melatonin (MT) has been shown to improve the quality during the storage of fruits and vegetables. The primary objective of this study is to investigate the effects of MT on the quality of fresh-cut Gastrodia elata during low-temperature (4 °C) storage. The results indicated that MT treatment not only suppressed the respiratory rate and malondialdehyde content but also slowed down the decline in total acidity and total soluble solids, effectively inhibiting microbial growth and enhancing the product safety of fresh-cut G. elata. The treatment with MT reduced the superoxide anions and hydrogen peroxide production, as well as inhibiting the activity and expression of peroxidase and polyphenol oxidase. Additionally, it led to increased activity and the expression of antioxidant-related enzymes, including superoxide dismutase, catalase, ascorbate peroxidase, glutathione reductase, monodehydroascorbate reductase, and dehydroascorbate reductase, while also resulting in elevated levels of ascorbic acid and glutathione. Furthermore, the treatment with MT induced an increase in the total phenolic and flavonoid content of fresh-cut G. elata and enhanced the activity and expression of key enzymes involved in the phenylpropanoid pathway (phenylalanine ammonia-lyase, cinnamate-4-hydroxylase, 4-coumarate: CoA ligase). In summary, MT enhances the antioxidant capacity by activating both the ROS metabolism and phenylpropanoid pathway, thus maintaining the quality of fresh-cut G. elata.

Effects of Steam and Water Blanching on Drying Characteristics, Water Distribution, Microstructure, and Bioactive Components of Gastrodia Elata

In the current work, the effects of steam and boiling water blanching on the drying characteristics, water distribution, microstructure, and contents of bioactive substances of Gastrodia elata (G. elata) were explored. Results showed that the degree of steaming and blanching was related to the core temperature of G. elata. The steaming and blanching pretreatment increased the drying time of the samples by more than 50%. The low-field nuclear magnetic resonance (LF-NMR) of treated samples showed that the relaxation time corresponded to water molecule states (bound, immobilized, and free) and G. elata became shorter, which indicated a reduction in free moisture and increased resistance of water diffusion in the solid structure during drying. Hydrolysis of polysaccharides and gelatinization of starch granules was observed in the microstructure of treated samples, which was consistent with changes in water status and drying rates. Steaming and blanching increased gastrodin and crude polysaccharide contents and decreased p-hydroxybenzyl alcohol content. These findings will contribute to a better understanding of the effect of steaming and blanching on the drying behavior and quality attributes of G. elata.