Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics.

Journal: Food chemistry
Published Date: Feb 1, 2025

Abstract

Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared [MIR] spectroscopy) and analytical models (Discriminant Analysis [DA], Partial Least Squares [PLS], and Artificial Neural Networks [ANN]), seeking to classify Camellia seed varieties and estimate oil and principal FAs composition. The PCA analysis effectively discriminated among various Camellia seed varieties, likely due to variations in their oil and principal FAs compositions. Significantly, the NIR-based DA model significantly outperformed MIR, achieving 100 % accuracy in distinguishing Camellia seed varieties. In terms of predicting the oil and principal FAs compositions in Camellia seeds, NIR-based predictions models outperformed those derived from MIR, with PLS models surpassing ANN models. This study validated the potential of NIR technology combined with chemometrics for rapid, high-throughput, non-destructive identification of Camellia seeds.

Authors

  • Tuo Leng
    State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, PR China.
  • Yuting Wang
    Respiratory Department, Dongzhimen Hospital Affiliated to BUCM, Beijing, China.
  • Zhijun Wang
    Center for Advancement of Drug Research and Evaluation, College of Pharmacy, Western University of Health Sciences, Pomona, CA 91766, USA.
  • Xiaoyi Hu
    State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, PR China.
  • Tongji Yuan
    State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, PR China.
  • Qiang Yu
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: yuq@nwsuaf.edu.cn.
  • Jianhua Xie
    State Key Laboratory of Food Science and Resources, Nanchang University, Nanchang 330047, PR China.
  • Yi Chen
    Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.

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