Unveiling the Origin and Quality of Rosa roxburghii Tratt: Fourier Transform Infrared Spectroscopy Sheds Light

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A new study leverages Fourier transform infrared spectroscopy (FT-IR) to determine the geographical origin and assess the quality of Rosa roxburghii Tratt (RRT), providing valuable insights for functional food production.

In a quest to unlock valuable insights into Rosa roxburghii Tratt (RRT), a study conducted by a team of researchers from Qingdao Agricultural University and China Agricultural University has delved into the analysis of its quality and identification of geographical origin. Published in the journal Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, the study employed Fourier transform infrared spectroscopy (FT-IR) to unravel the nutritional components and distinguish the origins of RRT (1).

Led by Shuqin Li, Yuemeng Lv, Qingli Yang, Juan Tang, Yue Huang, Haiyan Zhao, and Fangyuan Zhao, the study aimed to provide valuable information for the production of functional foods using RRT while also enabling the differentiation of RRT based on its geographical origin.

The team analyzed the nutritional components of RRT obtained from three different regions in China. They focused on key components such as vitamin C, polysaccharides, total flavonoids, and total phenolics, and evaluated their antioxidant activities using one-way analysis of variance (ANOVA). The results revealed significant variations in nutrient contents and antioxidant activities among RRT samples obtained from different regions.

FT-IR spectroscopy, combined with advanced statistical techniques including principal component analysis (PCA), stepwise linear discriminant analysis (SLDA), k-nearest neighbor (k-NN), and support vector machine (SVM), played a pivotal role in establishing discriminant models to identify the geographical origin of RRT. The researchers identified characteristic fingerprint bands within the FT-IR spectra of RRT (specifically, in the ranges of 1679–1618 cm-1 and 1520–900 cm−1) that were closely correlated with its geographical origins.

By utilizing SLDA, the team successfully developed a discriminant model that achieved high accuracy (reported as 100%) in classifying and identifying RRT samples from different regions. This breakthrough model demonstrated its effectiveness in determining the geographical origin of RRT, allowing for quicker and more accurate identification.


SLDA is a statistical technique used for feature selection and classification purposes. It is particularly effective when dealing with datasets containing a large number of variables or predictors. SLDA sequentially selects a subset of variables based on their discriminatory power, gradually building a discriminant model that optimally separates different classes or groups. The algorithm starts with an empty model and iteratively adds or removes variables based on their impact on the classification accuracy. This stepwise procedure continues until the desired number of variables, or a stopping criterion is reached. SLDA is widely employed machine learning to identify the most informative features and improve classification performance.

The study shed light on the prominent impact of geographical factors on the nutritional components and antioxidant activities found within RRT. The distinct variations observed among RRT samples from different regions highlight the influence of geographic factors on the plant's composition.

The findings also emphasized the potential of FT-IR spectroscopy as a rapid and precise tool for identifying the geographical origins of RRT. By utilizing characteristic fingerprint bands obtained through FT-IR analysis, researchers can effectively discern the origin of RRT samples, providing valuable information for quality control and traceability.

This study not only contributes to our understanding of RRT but also paves the way for improved quality analysis and geographical origin identification of this valuable botanical resource. The research demonstrates the power of advanced spectroscopic techniques in enhancing our knowledge of natural products and their origins.


(1) Li, S.; Lv, Y.; Yang, Q.; Tang, J.; Huang, Y.; Zhao, H.; Zhao, F. Quality analysis and geographical origin identification of Rosa roxburghii Tratt from three regions based on Fourier transform infrared spectroscopy. Spectrochimica Acta Part A: Mol. Biomol. Spectrosc. 2023, 297, 122689. DOI:10.1016/j.saa.2023.122689