An original approach for the quantitative characterization of saffron aroma strength using electronic nose

Figures & data

Table 1. Safranal quantification for different saffron samples using UV–visible spectroscopy

Sample	Harvest	Mean Absorption (50 mg/L)	Aroma strength ($$\mathop E\nolimits_{1\,cm}^{1\,\% } $$330 nm)
S1	2013	0.2080	43.4800
S2		0.2410	51.3981
S3		0.2637	56.1868
S4		0.2418	51.7130
S5		0.2795	59.5691
S6		0.2460	52.3811
S7		0.2429	51.6985
S8		0.2477	53.0963
S9	2014	0.2150	45.2525
S10		0.2730	58.2192
S11		0.2500	53.4662
S12		0.1655	35.4006
S13		0.2430	51.9779
S14		0.1899	40.6197
S15		0.1720	36.7909
S16		0.2599	55.5928
S17		0.1626	34.7803
S18	2015	0.2190	46.8442
S19		0.1640	35.0797
S20		0.2370	50.6945
S21		0.2480	53.0474
S22		0.2230	47.6999
S23		0.2765	59.1435
S24		0.2639	56.4441
S25		0.2478	53.0046
S26		0.2396	51.2506
S27		0.1589	33.9888
S28		0.2332	49.8816
S29		0.1966	42.0529
S30		0.2344	50.1383
S31		0.2129	45.5395
S32		0.1871	40.0208
S33		0.1990	42.5662

Figure 1. Schematic representation of the developed technique for saffron aroma characterization.

Figure 2. PCA score plot for the characterization of 33 saffron samples having different aroma strengths.

Figure 3. Relative contribution of the used aroma features to each PC (the loading plot).

Figure 4. MLP model A-correlations between the predicted and actual values, from left to right: training, validation, and test sets (data of all the aroma features with 30 neurons in the hidden layer).

Figure 5. MLP model B-correlations between the predicted and actual values, from left to right: training, validation, and test sets (all the aroma features excluding the less-effective ones and 30 neurons in the hidden layer).

Figure 6. MLP model C-correlations between the predicted and actual values, from left to right: training, validation, and test sets (only the more effective aroma features with 30 neurons in the hidden layer).

Figure 7. MLP model D- correlations between the predicted and actual values, from left to right: training, validation, and test sets (data from MOS sensors excluding the less-effective sensors and 50 neurons in the hidden layer).

Figure 8. Training, validation, and test performance of the MLP model D.

Figure 9. RBF model E, correlations between the predicted and actual (data of all the aroma features).

Figure 10. RBF model F, correlations between the predicted and actual (all aroma features excluding the less-effective ones).

Figure 11. RBF model G, correlations between the predicted and actual (only the more-effective aroma features).