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| High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum |
| ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2* |
1. School of Electronic Engineering，Xi’an University of Posts and Telecommunications，Xi’an 710121，China
2. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics，Chinese Academy of Sciences，Xi’an 710119，China
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Abstract In view of the low efficiency and accuracy of the traditional spectral method for egg freshness testing, we propose and demonstrate the study of egg freshness by using the VIS-NIR spectroscopy testing method combined with XGBoost and other algorithms. In our experiments, eggs were under different storage conditions as samples were divided into the training set and testing set for model building and evaluation. The harmonic weighted average (F-measure) and Accuracy were used as the performance evaluation indexes of the classification model. A VIS-NIR spectroscopy system collected the reflection spectra of eggs. The obtained spectral data werethen preprocessed and used to build different models for egg freshness evaluation. Various classification algorithms,including random forest (RF), least square regression (PLS), support vector machine (SVM), Multi-layer Perceptual Model (MLP) and XGBoost algorithm, were used. The performance of each modelwas evaluated in detail. The analysis shows that better training results are obtained in the RF, SVM and XGBoost models with data preprocessed by Savitzky Golay first-derivative (SG-1st-Der) and the PLS and MLP models with data preprocessed by standard normal variables (SNV).The interval partial least squares (IPLS) method was used to select a working waveband for data dimension reduction for models with the raw spectral data preprocessed by SG-1st-Der combing with the RF, SVM and XGBoost algorithms and models with the raw spectral data preprocessed by SNV combining with PLS and MLP algorithms, respectively. Based on the verification using the test set, it can be seen that the IPLS-XGBoost classification model after SG-1st-Der pretreatment performs best.For the conditions of room temperature storage and cold storage, the F-measure reached 92.33% and 90% respectively, and the Accuracy reached 94.44% and 91.67% respectively. Moreover, the computing time of the model for the prediction of test set samples takes only 0.6 s. The results show that the visible-near infrared spectroscopy method combined with the IPLS-XGBoost classification algorithm can be applied in egg freshness evaluation. Compared with traditional methods, this method has advantages in model classification performance, evaluation accuracy and running speed.
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Received: 2022-01-27
Accepted: 2022-06-16
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Corresponding Authors:
YU Wei-xing
E-mail: yuwx@opt.ac.cn
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