Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Youyou Wang; Yue Zhang; Yuwei Yuan; Yuyang Zhao; Jing Nie; Tiegui Nan; Luqi Huang; Jian Yang

doi:10.3389/fnut.2022.980095

Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics

Front Nutr. 2022 Oct 17:9:980095. doi: 10.3389/fnut.2022.980095. eCollection 2022.

Authors

Youyou Wang¹, Yue Zhang^{1

2}, Yuwei Yuan³, Yuyang Zhao¹, Jing Nie³, Tiegui Nan¹, Luqi Huang¹, Jian Yang¹

Affiliations

¹ State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
² School of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China.
³ Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou, China.

Abstract

The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.

Keywords: chemometrics; geographic origin; hyperspectral imaging; nutrients content; prediction; red raspberry.