Development of An Intelligent System to Determine Sour Cherry's Antioxidant Activity and Anthocyanin Content During Ripening

Figures & data

Figure 1 The machine vision system.

Table 1  Selected features of cherries by using sensitivity analysis

Modeling	Selected features
Antioxidant	pixel no.	r_mean	v_g_b	v_r_b	skew_g_b	g_mean
	m_g_b	m_r_b	kor_g_b	v_r	v_g	a*
Anthocyanin	pixel no.	g_mean	v_r_g	v_r_b	skew_ b	v_b
	m_r_b	skew_r	m_r_g	v_r	kor_r_b	v_g_b

Table 2  Performance of neural networks results related to antioxidant activity

Model	NH1	NH2	MSE	MAE	MAPE (%)	R
1	6	15	0.199	0.343	28.0	0.895
2	7	11	0.190	0.335	5.9	0.900
3	8	7	0.216	0.348	247.1	0.886
4	8	13	0.196	0.338	40.9	0.897
5 ^a	15	19	0.186	0.328	3.4	0.904
6	19	4	0.206	0.334	23.5	0.893
^aBold line indicates the best topology, i.e., 12-15-19-1 for modeling antioxidant.

Figure 2 Architecture of the used artificial neural networks for (a) anthocyanin content and (b) antioxidant activity.

Figure 3 Correlation between (a) anthocyanin content and (b) antioxidant activity achieved by laboratory methods and predicted values.

Table 3  Performance of neural networks results related to anthocyanin content

Model	NH1	NH2	MSE	MAE	MAPE (%)	R
1	7	7	0.033	0.089	7.1	0.984
2	15	13	0.247	0.211	20.6	0.877
3	17	5	0.031	0.092	15.3	0.985
4	17	12	0.032	0.096	34.3	0.984
5	17	15	0.040	0.095	6.2	0.980
6 ^a	20	6	0.027	0.082	3.5	0.987
^aBold line indicates the best topology, i.e., 12-20-6-1 for modeling anthocyanin.

Figure 4 (a) Anthocyanin content and (b) antioxidant activity changes during ripeness stages.