Identification of Repeatedly Frozen Meat Based on Near-Infrared Spectroscopy Combined with Self-Organizing Competitive Neural Networks

Figures & data

FIGURE 1 Schematic illustration of the structure of the self-organizing neural network.

FIGURE 2 Near-infrared spectra of the various pork samples.

TABLE 1 Accumulative contribution rate of the first four principal components

Principal component	PC1	PC2	PC3	PC4
Contribution rate (%)	43.42	36.74	13.11	1.89
Cumulative contribution rate (%)	43.42	80.16	93.27	95.16

FIGURE 3 A: Multiplicative scatter correction principal component score plot; B: Standard normal variables principal component score plot; C: First differential derivative principal component score plot; D: Second differential principal component score plot.

TABLE 2 Calibration models results

True value	Predictive value				Total sample numbers	Correct rate (%)
	HM	CM	FM	CFM
HM	27	1	2	0	30	90%
CM	2	13	0	1	16	81.3%
FM	3	1	16	0	20	80%
CFM	0	1	1	14	16	87.5%

TABLE 3 Effect of learning numbers on the model

Learning numbers	HM	CM	FM	CFM	Correct recognition numbers	Correct recognition rate (%)
	Correct recognition numbers	Correct recognition numbers	Correct recognition numbers	Correct recognition numbers
100	27	26	25	27	105	87.50%
200	29	27	25	26	107	89.20%
250	28	27	26	29	110	91.70%
300	26	26	28	28	108	90.00%
350	25	29	25	26	105	87.50%
400	25	28	24	27	104	86.70%

TABLE 4 Testing results of the prediction set

True value	Predictive value				Total sample numbers		Correct rate (%)
	HM	FM	CM	CFM
HM	14	1	0	0	15	93.3%
FM	0	14	0	1	15	93.3%
CM	0	1	14	0	15	93.3%
CFM	0	0	0	15	15	100.0%