Yahui, L, Xiaobo, Z, Tingting, S et al. (3 more authors) (2017) Determination of Geographical Origin and Anthocyanin Content of Black Goji Berry (Lycium ruthenicum Murr.) Using Near-Infrared Spectroscopy and Chemometrics. Food Analytical Methods, 10 (4). pp. 1034-1044. ISSN 1936-9751
Abstract
In order to rapidly and efficiently determine geographical origin and characterization categories in five varieties of black Goji berry (Lycium ruthenicum Murr.), near-infrared (NIR) spectroscopy and chemometrics were utilized for data acquisition. Using this data, synergy interval partial least squares (Si-PLS), linear discriminant analysis (LDA), K-nearest neighbors (KNN), back propagation artificial neural network (BP-ANN) and least-squares support vector machine (LS-SVM) regression were systematically evaluated and compared during model development. LS-SVM was initially performed to calibrate the discrimination model to identify the geographical origins and categories of the black Goji berry samples. Compared with other models, the recognition rate of LS-SVM was more than 98.18 %, which showed excellent generalization for identification results. Total anthocyanin content was closely related with the quality of black Goji berry. Synergy interval partial least squares (Si-PLS) was applied to develop the prediction model of total anthocyanin content. The model was optimized by a leave-one-out cross-validation, and model performance was evaluated by assessing the root mean square error of the prediction (RMSEP) and correlation coefficient (R t ) in the prediction set. Experimental results showed that the optimum results of the Si-PLS model were achieved as follows: RMSEP = 0.602 mg/g and R t = 0.899 in the prediction set. The overall results sufficiently demonstrate that spectroscopy coupled with the Si-PLS regression tool has the potential to successfully discriminate black Goji berry varieties.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, Springer Science Business Media New York. This is an author produced version of an article published in Food Analytical Methods. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Black Goji berry; Near-infrared (NIR) spectroscopy; Least-squares support vector machine (LS-SVM); Total anthocyanin content; Synergy interval partial least squares (Si-PLS) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Oct 2021 13:03 |
Last Modified: | 25 Jun 2023 22:47 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s12161-016-0666-4 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179323 |