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Table 1 Modeling results based on spectral features

From: Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

Storage time (h)

Evaluation index

Classification model

RF

SVM (c = 13.9288, g = 0.1649)

XGBoost (eta = 1, max_depth = 1)

2

Number of misjudgments

0

1

0

Accuracy (%)

100.00

95.00

100.00

8

Number of misjudgments

3

4

1

Accuracy (%)

85.00

80.00

95.00

24

Number of misjudgments

10

16

10

Accuracy (%)

50.00

20.00

50.00

48

Number of misjudgments

8

6

7

Accuracy (%)

60.00

70.00

65.00

Total number of misjudgments

21

27

18

 

Overall accuracy (%)

73.75

66.25

77.50

 
  1. c” is the penalty coefficient, “g” is the radius of the kernel function, optimize “c” and “g” by Gridsearch
  2. “eta” is the learning rate, “max_depth” is the maximum depth of each tree