optimised to improve predictive performance, as discussed in Sec. 3.3. All analyses were based on the 36 samples used were
those listed in Table 1, with 510 data points per sample.
% Cocaine % Caffeine % Glucose % Cocaine % Caffeine % Glucose
54.24
23.14
22.62
29.47
9.43
61.1
80.58
8.86
10.56
33.46
20.89
45.65
70.35
17.24
12.41
28.06
8.52
63.42
71.10
9.15
19.75
13.39
15.45
71.16
56.26
10.62
33.12
20.0
10.34
69.66
61.92
19.23
18.85
29.96
29.32
40.72
74.25
19.37
6.38
25.60
18.40
56.0
61.48
29.11
9.41
21.98
20.32
57.70
49.96
10.74
39.30
22.32
28.46
49.22
50.57
19.39
30.04
11.75
30.53
57.72
48.34
41.65
10.01
31.11
38.93
29.96
42.88
10.02
47.10
19.46
39.80
40.74
40.30
19.36
37.34
29.90
50.33
19.77
39.33
30.46
30.21
21.19
29.0
49.81
40.11
39.74
20.15
9.92
38.64
51.44
40.74
49.34
9.92
9.84
50.49
39.67
10.85
10.95
78.20
100.00
0.00
0.00
0.00
100.00
0.00
0.00
0.00
100.00
Table 1: Chemical composition of samples used in the study.
3.2
Prediction Methods
Two regression methods have been evaluated in this study:
1
k-Nearest Neighbours
2
Feed-Forward Neural Networks
Neural Networks are a popular ML technique for non-linear mapping of inputs to outputs. Based on highly simplified
models of the operation of the brain, each neuron in the network has a set of inputs that are weighted and summed, and a
non-linear threshold function is applied to the result to produce an output value. In a feed-forward network, the neurons are
arranged in layers, with each layer’s outputs providing the inputs for the following layer. In this study, the inputs to the first
layer are the Raman spectral data points and the final output is an estimate of cocaine concentration. The number of hidden
(i.e. intermediate) layers and number of neurons per hidden layer have been varied in experiments. Training a neural
network involves adjusting the weights on each neuron’s inputs until the outputs are as close as possible to their expected
values. In this work, the Resilient Backpropogation (RProp) algorithm
13 has been used for training the network, as
preliminary experiments indicated that it achieved relatively fast convergence and was not overly sensitive to parameter
settings. The Stuttgart Neural Network Simulator
14 was used for this work.
those listed in Table 1, with 510 data points per sample.
% Cocaine % Caffeine % Glucose % Cocaine % Caffeine % Glucose
54.24
23.14
22.62
29.47
9.43
61.1
80.58
8.86
10.56
33.46
20.89
45.65
70.35
17.24
12.41
28.06
8.52
63.42
71.10
9.15
19.75
13.39
15.45
71.16
56.26
10.62
33.12
20.0
10.34
69.66
61.92
19.23
18.85
29.96
29.32
40.72
74.25
19.37
6.38
25.60
18.40
56.0
61.48
29.11
9.41
21.98
20.32
57.70
49.96
10.74
39.30
22.32
28.46
49.22
50.57
19.39
30.04
11.75
30.53
57.72
48.34
41.65
10.01
31.11
38.93
29.96
42.88
10.02
47.10
19.46
39.80
40.74
40.30
19.36
37.34
29.90
50.33
19.77
39.33
30.46
30.21
21.19
29.0
49.81
40.11
39.74
20.15
9.92
38.64
51.44
40.74
49.34
9.92
9.84
50.49
39.67
10.85
10.95
78.20
100.00
0.00
0.00
0.00
100.00
0.00
0.00
0.00
100.00
Table 1: Chemical composition of samples used in the study.
3.2
Prediction Methods
Two regression methods have been evaluated in this study:
1
k-Nearest Neighbours
2
Feed-Forward Neural Networks
Neural Networks are a popular ML technique for non-linear mapping of inputs to outputs. Based on highly simplified
models of the operation of the brain, each neuron in the network has a set of inputs that are weighted and summed, and a
non-linear threshold function is applied to the result to produce an output value. In a feed-forward network, the neurons are
arranged in layers, with each layer’s outputs providing the inputs for the following layer. In this study, the inputs to the first
layer are the Raman spectral data points and the final output is an estimate of cocaine concentration. The number of hidden
(i.e. intermediate) layers and number of neurons per hidden layer have been varied in experiments. Training a neural
network involves adjusting the weights on each neuron’s inputs until the outputs are as close as possible to their expected
values. In this work, the Resilient Backpropogation (RProp) algorithm
13 has been used for training the network, as
preliminary experiments indicated that it achieved relatively fast convergence and was not overly sensitive to parameter
settings. The Stuttgart Neural Network Simulator
14 was used for this work.