文献类型：期刊文献

英文题名：Quality detection and variety classification of pecan seeds using hyperspectral imaging technology combined with machine learning

作者：Chen, Bingyu[1] Shi, Baolong[2] Gong, Jiarun[1] Shi, Guangzu[1] Jin, Hongmiao[1] Qin, Tao[1] Yang, Zhengfu[1] Lim, Kean-Jin[1] Liu, Wei[2] Zhang, Junpei[3] Wang, Zhengjia[1]

第一作者：Chen, Bingyu

通信作者：Chen, BY[1]

机构：[1]Zhejiang A&F Univ, Coll Forestry & Biotechnol, State Key Lab Subtrop Silviculture, Hangzhou 311300, Peoples R China;[2]Zhejiang A&F Univ, Coll Opt Mech & Elect Engn, Hangzhou 311300, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat State Forestry &, Beijing 100091, Peoples R China

年份：2024

卷号：131

外文期刊名：JOURNAL OF FOOD COMPOSITION AND ANALYSIS

收录：;Scopus(收录号：2-s2.0-85190093762);WOS:【SCI-EXPANDED(收录号:WOS:001229646800001)】；

基金：This work is supported by National Key Research and Development Program of China (2022YFD2200402) ; the Key research and development project of Zhejiang Province (2021C02054) ; Yunnan Province Academician Expert Workstation (202105AF150042) ; Zhejiang Provincial Academy Cooperation Forestry Science and Technology Project (2023SY14) ; and the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02066-12) . We wish to thank all the editors and anonymous reviewers for their constructive advice.

语种：英文

外文关键词：Pecan seed; Quality detection; Variety classification; Hyperspectral imaging technique; Machine learning; Feature extraction method

摘要：Pecan (Carya illinoinensis K.), a well-known dried seed and woody oil tree, faces challenges in its industry due to complex quality assessment methods and confusing varieties. These challenges have seriously hampered the development of a large-scale pecan deep processing industry. This work aimed to apply hyperspectral imaging technology (HSI) combined with machine learning to evaluate the quality of pecan seeds and perform variety classification. The samples of this work were composed of 19 varieties of pecan seeds, with 30 seeds per variety. After spectral preprocessing, spectral features were extracted from the spectral profiles using feature extraction methods. Back-propagation neural network models and partial least squares models were established to predict the contents of crude fat and moisture in pecan seeds. Predictions of the best models gave good results with R2score of 0.887 for the crude fat model and 0.950 for the moisture model. Additionally, support vector machine models were developed to identify pecan varieties. The model achieved good results in 19 pecan varieties identification with accuracy of 0.965. In conclusion, the combination of HSI and machine learning could be an effective tool in improving the pecan industry and providing sustainable and efficient methods in the production of pecan seeds.