Ginseng is a traditional medical herb used for more than 2,000 years to treat various conditions, including cancer, diabetes, and cardiovascular diseases, promote immune and central nervous system (CNS) function, relieve stress, and for its antioxidant activities.
Ginsenosides are considered the main bioactive components and essential indicators for the quality evaluation of ginseng. Among them, ginsenoside Rg1, ginsenoside Re, and ginsenoside Rb1 are the most bioactive components in ginseng.
The level and composition of these ginsenosides vary significantly depending on the species, growth years, part of the plant, cultivation methods, climate, and preservation methods; therefore, it is important to determine the content of various ginsenosides in ginseng for quality control.
In a new study published in the journal Food Chemistry, researchers at Tianjin University of Traditional Chinese Medicine used the SOC710-VP hyperspectral imaging system and X-ray imaging to establish how these techniques, combined with data fusion technology, can be used for successful quality evaluation of ginseng, paving the way for a more efficient and effective future in ginseng quality control.
Currently, the standard method for determining ginsenoside content relies on wet chemical analysis methods, including the high-performance liquid chromatography (HPLC)technique adopted in the study to establish benchmark ginsenoside Rg1, ginsenoside Re, and ginsenoside Rb1 levels. While these methods are accurate, they are time-intensive, making real-time analysis of samples difficult. moreover, samples used in wet chemical analysis methods cannot typically be recovered afterward, highlighting the urgent need for more efficient, non-destructive methods.
The global demand for ginseng, one of the most widely consumed alternative medicines, is expected to reach $17.9 billion by 2030. The authors note that the ginsenoside content in individual ginseng samples can vary significantly, underscoring the importance of implementing quality control at the individual unit level. As a result, the need for non-destructive, automated methods for quality grading is becoming increasingly evident.
Unlike traditional imaging, which captures color in broad bands like red, green, and blue, spectral imaging gathers data across numerous narrow spectral bands, enabling the detection of specific materials, chemicals, or substances by their distinct spectral signatures. By integrating spatial and spectral information, spectral imaging allows for in-depth analysis of agricultural products’ composition, properties, and condition in a manner that preserves the product.
For more information on how researchers at Tianjin University combined hyperspectral images taken with the SOC710-VP hyperspectral camera and density data obtained from X-ray images to predict ginsenoside levels in ginseng, see the full published article.