Researchers from Zhejiang University in China have revealed the transformative potential of infrared (IR) and Raman spectroscopy techniques for rapidly and accurately assessing herb quality and safety, offering a promising path toward intelligent and eco-friendly herb industry development.
In a recent study published in Frontiers in Plant Science, researchers from the College of Biosystems Engineering and Food Science at Zhejiang University in Hangzhou, China, described the potential of infrared (IR) and Raman spectroscopy techniques for the assessment of herb quality and safety. Led by Rongqin Chen, Fei Liu, and Jing Huang, this research highlights the urgent need for innovative, rapid, and environmentally friendly methods to ensure the quality and safety of herbs in the modern herbal industry.
Herbs have long been utilized for their natural healing properties and culinary applications. As interest in their benefits continues to rise globally, the need for rigorous quality control and safety inspection has become paramount. Unlike synthetic drugs with well-defined ingredients, herbs are influenced by various factors, such as habitat, maturity, and processing methods, throughout the entire production process, from raw materials to patented herbal products.
Traditional methods of quality control, relying on subjective human knowledge or experience, are time-consuming and often lack quick responsiveness, hindering the digital transformation of the herbal industry. To address these challenges, the researchers explored the potential of IR and Raman spectroscopy, vibrational spectroscopy techniques capable of providing comprehensive chemical profiles of multiple compounds without causing damage. These techniques offer objective, high-speed, non-destructive analysis, making them invaluable tools in herb quality control and safety inspection.
The study showcases the application of IR and Raman spectroscopy techniques across the entire herb production process. This includes the analysis of herbal raw materials, quality control during processing, and the evaluation of patented herbal products. By providing a non-invasive and quick-response approach to characterizing the composition and content of herbal ingredients, these techniques promise to enhance the efficiency and accuracy of digital herb detection.
In addition to the advantages, the research also addresses the limitations of IR and Raman spectroscopy techniques. It offers valuable insights into improving digital detection methods for herb quality and safety, paving the way for intelligent and eco-friendly development within the herbal industry.
As herbs continue to play a vital role in healthcare and as functional food additives, ensuring their quality and safety is of paramount importance. The team not only highlights the potential of advanced spectroscopy techniques but also underscores the need for innovation in herb quality control to meet the growing demands of a global market.
(1) Chen, R.; Liu, F.; Zhang, C.; Wang, W.; Yang, R.; Zhao, Y.; Peng, J.; Kong, W.; Huang, J. Trends in Digital Detection for the Quality and Safety Assessment of Herbs Through Infrared and Raman Spectroscopy. Frontiers in Plant Science 2023, 14, 1128300. DOI: 10.3389/fpls.2023.1128300
This article was written with the help of artificial intelligence and has been edited to ensure accuracy and clarity. You can read more about our policy for using AI here.
New Spectroscopic Techniques Offer Breakthrough in Analyzing Ancient Chinese Wall Paintings
October 29th 2024This new study examines how spectroscopic techniques, such as attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), ultraviolet–visible–near-infrared (UV-Vis-NIR) spectroscopy, and Raman spectroscopy, were used to analyze the pigments in ancient Chinese wall paintings.
Geographical Traceability of Millet by Mid-Infrared Spectroscopy and Feature Extraction
October 18th 2024The study developed an effective mid-infrared spectroscopic identification model, combining principal component analysis (PCA) and support vector machine (SVM), to accurately determine the geographical origin of five types of millet with a recognition accuracy of up to 99.2% for the training set and 98.3% for the prediction set.