In the realm of agritech and crop processing, a groundbreaking study has emerged that could redefine quality control for one of China’s most prized medicinal plants, Gastrodia elata Blume (G. elata). Published in *Industrial Crops and Products*, the research led by Yingfeng Zhong from the College of Agronomy and Biotechnology at Yunnan Agricultural University, alongside the Medicinal Plants Research Institute at Yunnan Academy of Agricultural Sciences, delves into the intricate effects of different processing methods on the bioactive components of G. elata.
The study highlights how post-harvest processing, particularly steaming, significantly influences the quality of this medicinal and edible crop. By employing a combination of near-infrared (NIR) and Fourier-transform infrared (FTIR) spectroscopy alongside advanced chemometrics, the researchers have uncovered nuanced insights into the molecular dynamics of G. elata under various processing conditions.
One of the most compelling findings is the identification of a clear trend: different processing methods lead to an increase in the levels of parishin (A, B, C, E) and gastrodin, while 4-hydroxybenzyl alcohol shows a decrease. Among the preparation methods, stir-frying (LP) stands out, boasting the highest content of total characteristic components, reaching an impressive 11.93 mg/g.
“What sets this study apart is our use of two-dimensional correlation spectroscopy (2DCOS) to visualize the sequential changes in molecular dynamics,” explains Zhong. “This approach allows us to see how functional groups like O-H and C-H respond to thermal agitation, providing a deeper understanding of how processing alters the product’s quality.”
The implications for the agriculture sector are profound. The NIR-CARS-2DCOS-ResNet model developed in this study achieved 100% accurate identification across six different processing methods, outperforming the full-spectrum ResNet model in both accuracy and efficiency. For quantitative analysis, partial least squares regression (PLSR) models successfully predicted the content of individual characteristic components, with the NIR strategy proving more robust than the FTIR strategy.
“This research provides a rapid, non-destructive, and effective strategy for both authenticating processing methods and predicting the content of key active constituents in G. elata,” Zhong adds. “It offers a powerful tool for post-harvest quality control, which is crucial for maintaining the integrity and value of this important crop.”
The study’s findings could revolutionize the way G. elata and similar crops are processed and quality-controlled, ensuring higher standards and greater commercial value. As the agriculture sector continues to embrace advanced technologies, this research paves the way for more precise and efficient processing methods, ultimately benefiting producers, processors, and consumers alike.
With the growing demand for high-quality medicinal and edible crops, the insights gained from this study are timely and relevant. By leveraging cutting-edge technologies like NIR, FTIR, and chemometrics, the agriculture sector can enhance its capabilities and meet the evolving needs of the market. This research not only advances our understanding of G. elata but also sets a precedent for future developments in the field of crop processing and quality control.