Spectroscopy and Spectral Analysis, Volume. 42, Issue 2, 549(2022)
Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm
Figures & Tables(6)
Fig. 1. Boletus samples
(a):
Fig. 2. Average spectra of 7 species of Boletus
(a): Mid-infrared spectrum; (b): UV-Vis spectrum
Fig. 3. Ntree selection diagram and Correct rate matrix
(a): Mid-level (LVs) Ntree best choice map; (b): Mid-level (CPA) Ntree best choice map;(c): Mid-level (LVs) Training set correct rate matrix; (d): Mid-level (CPA) Training set correct rate matrix;(e): Mid-level (LVs) Validation set correct rate matrix; (f): Mid-level (CPA) Validation set correct rate matrix
Table 1. Boletus samples information
View tableView in ArticleTable 1. Boletus samples information
种类 编号 产地 数量 灰褐牛肝菌 a 保山市隆阳区、 玉溪市江川区、
大理市弥渡县、 昆明市晋宁区、
曲靖市马龙区、 昆明市安宁区98 美味牛肝菌 b 楚雄市南华县、 迪庆州香格里拉市、
大理市弥渡县、 迪庆州维西县、
保山市隆阳区、 昆明市安宁区、
玉溪市红塔区、 大理市鹤庆县、
文山市东山乡、 昆明市石林县221 栗色牛肝菌 c 昆明市石林县、 曲靖市马龙区、
保山市隆阳区、 大理市弥渡县、
红河州石屏县、楚雄市元谋县、
玉溪市红塔区、红河州个旧市110 小美牛肝菌 d 楚雄市南华县、玉溪市红塔区、
保山市隆阳区32 皱盖疣柄牛肝菌 e 曲靖市麒麟区、 红河州石屏县、
大理市弥渡县、 昆明市安宁区、
迪庆州维西县、 保山市隆阳区、
楚雄市元谋县、 玉溪市红塔区132 双色牛肝菌 f 楚雄市南华县、 大理市苍山、
曲靖市麒麟区30 绒柄牛肝菌 g 红河州个旧市、 迪庆州香格里拉市、
红河州石屏县、 大理市鹤庆县60
Table 2. The main parameters and accuracy of the discriminant analysis model of partial least squares
View tableView in ArticleTable 2. The main parameters and accuracy of the discriminant analysis model of partial least squares
光谱类型 预处理方法 主成分数 RMSEC RMSECV 训练集/% 验证集/% 中红外光谱 SNV+SG 24 0.693 0.555 0.172 449 0.211 937 97.59 98.24 2D+MSC+SNV 18 0.778 0.651 0.149 082 0.193 025 99.78 99.12 1D+MSC+SNV+SG 17 0.673 0.575 0.178 289 0.210 069 97.37 97.80 紫外光谱 MSC+2D 7 0.038 2 0.024 7 0.320 725 0.329 83 34.65 32.16 SNV+SG 16 0.426 0.281 0.247 129 0.282 096 80.70 80.18 2D+MSC+SNV 10 0.351 0.262 0.265 682 0.284 48 67.32 65.64 1D+MSC+SNV+SG 16 0.364 0.257 0.262 953 0.284 499 76.10 73.13 低级融合 2D+MSC+SNV,
SNV+SG19 0.8 0.676 0.140 484 0.185 499 100 99.12 中级融合(LVs) 6 0.834 0.812 0.131 507 0.148 948 100 99.56 中级融合(CPA) 11 0.927 0.882 0.086 528 0.152 926 100 100
Table 3. The main parameters and accuracy of the random forest model
View tableView in ArticleTable 3. The main parameters and accuracy of the random forest model
光谱类型 预处理方法 Ntree Mtry 训练集/% 验证集/% 中红外光谱 SNV+SG 2 000, 1 412 40, 3 76.75 85 2D+MSC+SNV 1 800, 1 149 50, 5 93.20 99 1D+MSC+SNV+SG 2 100, 1 537 42, 5 87.72 96.29 紫外光谱 2D+MSC 2 000, 1 821 40, 4 62.06 64.30 SNV+SG 1 700, 1 327 35, 2 56.80 70.71 2D+MSC+SNV 2 000, 1 549 40, 5 64.47 73.10 1D+MSC+SNV+SG 1 800, 1 562 30, 4 62.28 76.14 低级融合 2D+MSC+SNV,
1D+MSC+SNV+SG2 000, 1 184 40, 5 92.32 99.14 中级融合(LVs) 2 000, 865 40, 5 92.76 96.04 中级融合(CPA) 2 000, 1 249 40, 5 97.15 100
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Feng-xia CHEN, Tian-wei YANG, Jie-qing LI, Hong-gao LIU, Mao-pan FAN, Yuan-zhong WANG. Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm[J]. Spectroscopy and Spectral Analysis, 2022, 42(2): 549
Paper Information
Category: Research Articles
Received: Jan. 4, 2021
Accepted: Feb. 1, 2021
Published Online: Apr. 2, 2022
The Author Email: CHEN Feng-xia (cfx0909@126.com)
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