College of Food Science and Engineering, Qingdao Agricultural University, No. 700, Changcheng Road, Qingdao 266109, China.
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Sep 5;297:122689. doi: 10.1016/j.saa.2023.122689. Epub 2023 Apr 1.
The study aimed to provide new information of Rosa roxburghii Tratt (RRT) for the production of functional foods and distinguish the geographical origins of RRT. The nutritional components of RRT from three regions in China, such as vitamin C, polysaccharides, total flavonoids, and total phenolics, and their antioxidant activities were analyzed by one-way ANOVA. The results of Fourier transform infrared spectroscopy (FT-IR) combined with principal component analysis (PCA), stepwise linear discriminant analysis (SLDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were used to establish discriminant models to identify the geographical origin of RRT. The results of one-way ANOVA showed that the contents of some nutrients and antioxidant activity were significantly different among RRT from different regions and their FT-IR spectra also showed significant differences. The characteristic fingerprint bands of FT-IR (1679-1618 cmand 1520-900 cm) closely related to the geographical origins of RRT were screened out. Based on SLDA, a discriminant model was established to realize the classification and identification of RRT from different regions and the correct discrimination rate of the testing sample set obtained with the established model reached 100 %. Geographical factors caused the obvious differences in nutritional components and antioxidant activity in RRT. The characteristic fingerprint bands of RRT obtained with FT-IR could be used to identify the geographical origins of RRT more quickly and accurately.
本研究旨在为功能性食品的生产提供新的刺梨（Rosa roxburghii Tratt）信息，并区分刺梨的地理起源。采用单因素方差分析对来自中国三个地区的刺梨的营养成分（如维生素 C、多糖、总黄酮和总酚）及其抗氧化活性进行了分析。傅里叶变换红外光谱（FT-IR）结合主成分分析（PCA）、逐步线性判别分析（SLDA）、k-最近邻（k-NN）和支持向量机（SVM）的结果用于建立判别模型，以识别刺梨的地理起源。单因素方差分析的结果表明，不同地区刺梨的一些营养成分含量和抗氧化活性存在显著差异，其 FT-IR 光谱也存在显著差异。筛选出与刺梨地理起源密切相关的 FT-IR 特征指纹带（1679-1618 cm 和 1520-900 cm）。基于 SLDA，建立了判别模型，实现了不同地区刺梨的分类和识别，所建立模型的测试样本集的正确判别率达到 100%。地理因素导致了刺梨营养成分和抗氧化活性的明显差异。FT-IR 获得的刺梨特征指纹带可用于更快速、准确地识别刺梨的地理起源。
