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Soft Independent Modeling of Class Analogy for Classifying Lumber Species Using Their Near-infrared Spectra

  • Yang, Sang-Yun (Department of Forest Sciences, Seoul National University) ;
  • Park, Yonggun (Department of Forest Sciences, Seoul National University) ;
  • Chung, Hyunwoo (Department of Forest Sciences, Seoul National University) ;
  • Kim, Hyunbin (Department of Forest Sciences, Seoul National University) ;
  • Park, Se-Yeong (Department of Forest Sciences, Seoul National University) ;
  • Choi, In-Gyu (Department of Forest Sciences, Seoul National University) ;
  • Kwon, Ohkyung (National Instrumentation Center for Environmental Management, Seoul National University) ;
  • Yeo, Hwanmyeong (Department of Forest Sciences, Seoul National University)
  • 투고 : 2018.12.21
  • 심사 : 2019.01.14
  • 발행 : 2019.01.25

초록

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

키워드

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Fig. 1. Raw average NIR absorbance spectra for each species.

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Fig. 2. SNV preprocessed average NIR absorbance spectra for each species.

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Fig. 3. Savitzky-Golay 2nd derivative preprocessed average NIR spectra for each species.

Table 1. The number of lumber samples collected fromseveral National Forestry Cooperative Federations

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Table 2. Optimal number of principal components and explained total variance of principal component analysis model

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Table 3. Confusion matrix in the case of binaryclassification

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Table 4. Confusion matrix of SIMCA based on each species PCA models using raw spectra

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Table 5. Confusion matrix of SIMCA based on each species PCA models using Standard normal variate preprocessed spectra.

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Table 6. Confusion matrix of SIMCA based on each species PCA models using Savitzky-Golay 2nd derivative preprocessed spectra.

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