A New Approach to Predict Acidity of Bayberry Juice by Using Vis/Near Infrared Spectroscopy

Figures & data

Figure 1 Collection system of bayberry juice.

Figure 2 Four original representative absorbance spectral data from 325 nm–1075 nm.

Table 1 Calibration and validation results of partial least square for analyzing acidity of bayberry juice

Method	Calibration			Validation
	SEC	RMSEC	r	SECV	RMSECV	r
SNV	0.1204	0.1193	0.9660	0.1675	0.1661	0.9334
MSC	0.1611	0.1597	0.9383	0.1904	0.1887	0.9128
1st-Der	0.1482	0.1469	0.9480	0.2028	0.2010	0.9021
WPT	0.1206	0.1195	0.9659	0.1664	0.1650	0.9343
SNV: standard normal variate; MSC: multiplicative scatter correction; 1 st-Der: S. Golay first derivative; WPT: wavelet package transforms; SEC: standard error of calibration; RMSEC: root mean square error of calibration; r: correlation coefficient; SECV: standard error of cross-validation; RMSECV: root mean square error of cross-validation.

Figure 3 Prediction results for the unknown 30 samples from the PLS-ANN models.

Figure 4 Regression coefficients with corresponding wavelengths for acidity.