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

文献类型： 外文期刊

作者： Wang, Youyou 1 ; Zhang, Yue 1 ; Yuan, Yuwei 3 ; Zhao, Yuyang 1 ; Nie, Jing 3 ; Nan, Tiegui 1 ; Huang, Luqi 1 ; Yang, Jian 1 ;

作者机构： 1.China Acad Chinese Med Sci, Natl Resource Ctr Chinese Mat Med, State Key Lab Breeding Base Dao di Herbs, Beijing, Peoples R China

2.Yunnan Univ Chinese Med, Sch Tradit Chinese Med, Kunming, Peoples R China

3.Inst Agroprod Safety & Nutr, Zhejiang Acad Agr Sci, Key Lab Informat Traceabil Agr Prod, Minist Agr & Rural Affairs China, Hangzhou, Peoples R China

关键词： red raspberry; hyperspectral imaging; chemometrics; nutrients content; geographic origin; prediction

期刊名称：FRONTIERS IN NUTRITION （ 影响因子：6.59； 五年影响因子：6.873 ）

ISSN： 2296-861X

年卷期： 2022 年 9 卷

页码：

收录情况： SCI

摘要： 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.

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