Variety Identification of Rice Vinegars Using Visible and Near Infrared Spectroscopy and Multivariate Calibrations

Figures & data

Figure 1 The sketch map of the structure of BP neural networks.

Figure 2 VIS/NIR transmission spectra of five different rice vinegars (color figure available online).

Figure 3 Cluster plot with top three PLS factors of five varieties of rice vinegars (color figure available online).

Table 1 The first six PLS factors and their explained variance

PLS factors	1	2	3	4	5	6
Explained variance (%)	80.3	86.5	90.9	94.8	97.1	97.3

Figure 4 The absolute error of each sample in validation set predicted by PLSDA and BPNN model.

Table 2 The identification results of rice vinegars in validation set by BPNN and LS-SVM models

		BPNN		LS-SVM
Variety	No.	Wrong ^a (%)	Recognition ^b (%)	(γ, σ^2)	Wrong ^a (%)	Recognition ^b (%)
Amoy	1–15	0	100	(10.4, 76.8)	0	100
Haitian	16–30	0	100	(350.0, 3.6)	0	100
Hengshun	31–45	0	100	(17.5, 6.5)	0	100
Beau deal	46–60	0	100	(0.4, 3.9)	0	100
Shuangyu	61–75	0	100	(0.6, 1.7)	0	100
Total	1–75	0	100	—	0	100
^aWrong: The percentage of wrongly identified rice vinegar samples.
^bRecognition: The percentage of correctly identified rice vinegar samples.

Figure 5 The plots of (a) x-loading weights of first five PLS factors and (b) regression coefficients of rice vinegars (color figure available online).

Table 3 The identification results of rice vinegars in validation set by EW-BPNN and EW-LS-SVM models

		EW-BPNN		EW-LS-SVM
Variety	No.	Wrong ^a (%)	Recognition ^b (%)	(γ, σ^2)	Wrong ^a (%)	Recognition ^b (%)
Amoy	01–15	0	100	(15.7, 0.1)	0	100
Haitian	16–30	100	0	(898.4, 4.2)	0	100
Hengshun	31–45	46.7	53.3	(308.5, 33.8)	0	100
Beau deal	46–60	20	80	(0.6, 2.2)	0	100
Shuangyu	61–75	0	100	(0.6, 0.3)	0	100
Total	01–75	33.3	66.7	—	0	100
^aWrong: The percentage of wrongly identified rice vinegar samples.
^bRecognition: The percentage of correctly identified rice vinegar samples.