Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy

Xin Zou, Qiaoyun Wang*, Yinji Chen, Jilong Wang, Shunyuan Xu, Ziheng Zhu, Chongyue Yan, Peng Shan, Shuyu Wang, Yong Qing Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra to improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an eXtreme Gradient Boosting (XGBoost) feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn on test set were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications.

Original languageEnglish
Article number141053
Pages (from-to)1-10
Number of pages10
JournalFood Chemistry
Volume463
Issue numberPart 1
Early online date31 Aug 2024
DOIs
Publication statusE-pub ahead of print - 31 Aug 2024

Keywords

  • Activation function
  • Convolutional neural network
  • Elastic net
  • Near-infrared spectroscopy
  • XGBoost feature extraction

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