Development of a multispectral imaging system for online quality assessment of pomegranate fruit

Figures & data

Table 1. Statistics of both calibration and prediction data sets for some physical properties of pomegranate fruit samples.

Attributes	Calibration set (70 samples)				Prediction set (30 samples)
	Max	Min	Mean	SD	Max	Min	Mean	SD
L* (mm)	66.28	62.03	64.02	2.13	66.49	62.72	64.58	2.38
W* (mm)	73.15	67.14	70.15	3.05	72.12	67.36	70.68	2.31
T* (mm)	69.84	67.82	68.12	2.6	69.35	67.75	68.09	2.75
Dg* (mm)	68.25	64.15	66.95	2.13	68.72	65.61	66.54	2.23
Fruit mass (g)	166	153	160	8.12	165	154	160	7.56

L: length; W: width; T: thickness and D_g: geometric mean diameter.

Figure 1. Fruit and arils of pomegranate (cv. “Ashraf”) cultivar.

Figure 2. The constructed multispectral imaging system for acquiring images from the fruit.

Table 2. Statistics values of TSS, TA, and pH of 100 pomegranate samples.

Parameters	Data set	Number of samples	Min	Max	Mean	SD
TSS	Calibration samples	70	18.48	19.1	18.77	0.13
	Prediction samples	30	18.54	19.1	18.78	0.14
TA	Calibration samples	70	0.7	0.79	0.75	0.03
	Prediction samples	30	0.71	0.79	0.75	0.03
pH	Calibration samples	70	3.44	3.64	3.55	0.04
	Prediction samples	30	3.51	3.65	3.56	0.03

Figure 3. The average raw reflectance Vis/NIR spectra of A: Ashraf pomegranate fruit, and B: its absorbance spectrum.

Figure 4. Prediction of TSS, TA, and pH using PLS models: A: predicted residual error sum of squares (PRESS) for predicting TSS, TA, and pH as a function of number of factors; B: measured and predicted TSS values for training and validation sets using nine factors; C: measured and predicted pH values for training and validation sets using four factors; and D: measured and predicted TA values for training and validation sets using six factors.

Table 3. PLS models for predicting TSS, TA, and pH in pomegranate.

Attribute	Number of PLS factor	Calibration set			Prediction set
		R	RPD	RMSEC	R	RPD	RMSEP
TSS	9	0.95	6.7	0.22	0.94	6.72	0.21
TA	6	0.94	5.35	0.25	0.93	5.31	0.26
pH	8	0.86	5.01	0.062	0.88	5.14	0.062

Table 4. MLR models for predicting TSS, TA, and pH in pomegranate.

Attribute	Optimal wavelengths (nm)	Calibration set			Prediction set
		R	RPD	RMSEC	R	RPD	RMSEP
TSS	450, 521, 630, 780, 853, 950	0.97	6.7	0.21	0.97	5.77	0.22
TA	450, 521, 630, 780, 853, 950	0.93	5.21	0.26	0.92	5.22	0.26
pH	435, 520, 585, 680, 850, 993	0.93	5.01	0.035	0.94	4.98	0.038

Figure 5. Prediction of TSS, TA, and pH using MLR models: A: correlation between measured TSS and estimated TSS for training and validation sets; B: correlation between measured pH and estimated pH for training and validation sets; C: correlation between measured TA and estimated TA for training and validation sets.