@article{SUN2025, 
author = {Xinyue SUN and Yanlong LI and Mingming CHEN and Yan SONG and Lili QIAN and Feng ZUO and Hai’ou GUAN and Tao ZHANG and Xingquan LIU and Guoxin ZHOU},
title = {Rapid and Non-destructive Identification of Wuchang Daohuaxiang Rice Using Near-Infrared Spectroscopy and t-Distributed Stochastic Neighbor Embedding},
year = {2025},
journal = {Food Science},
volume = {46},
number = {20},
pages = {318-326},
keywords = {Wuchang Daohuaxiang rice, near-infrared spectroscopy, random forest, discrimination of similar rice varieties, t-distributed stochastic neighbor embedding},
url = {https://www.sciopen.com/article/10.7506/spkx1002-6630-20250430-261},
doi = {10.7506/spkx1002-6630-20250430-261},
abstract = {This study proposed a rapid, non-destructive method for identifying Wuchang Daohuaxiang rice based on near-infrared (NIR) spectroscopy combined with machine learning algorithms. NIR spectra of different varieties of rice were collected. First-order derivative was determined as the best spectral preprocessing method using partial least squares regression (PLSR). Two dimensionality reduction methods, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), were compared, and five machine learning models including artificial neural network (ANN), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and Naive Bayes (NB) were constructed for variety classification and comparison. The results showed that t-SNE improved the Calinski-Harabasz index by 1078.0051, demonstrating better clustering performance. After t-SNE dimensionality reduction, the performance of all five models was superior to that without dimensionality reduction. The average classification accuracy was 95.78%. The accuracy of the NB model was improved most effectively (by 18.89%). The random forest model showed the best classification performance, with prediction accuracy and precision of 98.89% and 98.96%, respectively. This method provides a rapid, non-destructive solution for identifying Wuchang Daohuaxiang rice, which will contribute to brand protection and safeguarding consumer rights, and also offers a new approach for the identification of other geographical indication agricultural products.}
}