A Study on Soluble Solids Content Assessment Using Electronic Nose: Persimmon Fruit Picked on Different Dates

Figures & data

TABLE 1 Sensors used and their main applications in PEN 3

Sensor number	General description	Reference
S1	Aromatic compounds	Toluene, 10 ppm
S2	Very sensitive, broad range sensitivity, react on nitrogen oxides, very sensitive with negative signal	NO2, 1 ppm
S3	Ammonia, used as sensor for aromatic compounds	Benzene, 10 ppm
S4	Mainly hydrogen, selectively (breath gases)	H2, 100 ppb
S5	Alkenes, aromatic compounds, less polar compounds	Propane, 1 ppm
S6	Sensitive to methane broad range	CH3, 100
S7	Reacts on sulfur compounds, otherwise sensitive to many terpenes and sulphur organic compounds, which are important for smell, limonene, pyrazine	H2S, 1 ppm
S8	Detects alcohols, partially aromatic compounds, broad range	CO, 100 ppm
S9	Aromatics compounds, sulphur organic compounds	H2S, 1 ppm
S10	Reacts on high concentrations >100 ppm, sometime very selective (methane)	CH3,10 CH3, 100 ppm

FIGURE 1 Soluble solid content (SSC) of persimmon at different picking dates for the daily means of 40 samples and their standard deviations.

FIGURE 2 Ten sensors responses of electronic nose to persimmon fruit aroma.

FIGURE 3 PCA analysis for persimmons at different picking date.

FIGURE 4 Loading analysis related to PC1 and PC2 for persimmons.

FIGURE 5 Results of (a) various C, where ϵ = 0.1 for ten-fold cross-validation (unit: %); (b) various ϵ, where C = 4 for ten-fold cross-validation (unit: %); and various δ², where C = 2², ϵ = 2⁻⁴ for ten-fold cross-validation (unit: %).

TABLE 2 Comparison of the prediction performance of three models (unit: %)

	Calibration			Prediction
Model type	APA	RMSE	MRE	APA	RMSE	MRE
MLR	73.16	1.68	1.43	63.36	2.32	1.88
BPNN	83.49	1.24	0.98	76.49	1.42	1.37
SVM	95.92	0.08	0.04	91.36	0.71	0.58

MLR represents multiple linear regression, BPNN represents back-propagation neural network, SVM represents support vector machine.