Detection of Sugar Content of Pomegranates from Different Producing Areas Based on Near-Infrared Spectroscopy

Yande Liu; Yu Zhang; Hai Xu; Xiaogang Jiang; Junzheng Wang

doi:10.3788/LOP57.013002

Laser & Optoelectronics Progress, Volume. 57, Issue 1, 013002(2020)

Detection of Sugar Content of Pomegranates from Different Producing Areas Based on Near-Infrared Spectroscopy

Yande Liu^*, Yu Zhang, Hai Xu, Xiaogang Jiang, and Junzheng Wang

National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, Jiangxi 330013, China

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Figures & Tables(9)

Fig. 1. Dynamic detection device for near-infrared diffuse transmission. (a) Schematic of light path; (b) arrangement of light source

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Fig. 2. Typical spectra of two types of pomegranates

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Fig. 3. Spectra and appearances of samples with rough and normal surfaces. (a) Appearances; (b) spectra

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Fig. 4. Score scattered plot of principal component analysis

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Fig. 5. PLS-DA models. (a) PLS-DA model for calibration set; (b) PLS-DA model for prediction set

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Fig. 6. PLS-DA models of sugar content of pomegranates from two different producing areas. (a) Sichuan pomegranate; (b) Yunnan pomegranate

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Table 1. Related parameters of pomegranate
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Table 1. Related parameters of pomegranate
Pomegranate species Number (N) RD /mm LD /mm Mass /g RS /Brix Mean RS SD
Sichuan 60 63-87 63-80 198-334 12.7-16.3 14.32 0.711
Yunnan 40 79-96 67-94 246.2-443.8 12.9-15.7 14.22 0.570
Test 8 74-82 68-79 228-306.5 12.2-16 13.76 0.638

Table 2. Reconstructed results of PLS-DA model
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Table 2. Reconstructed results of PLS-DA model
Data set N R_p RMSEP R_c RMSEC Misjudgment rate /%
Calibration set 75 - - 0.85 1.04 1.6
Prediction set 25 0.82 1.16 - - 3

Table 3. Results of models optimized by different pretreatment methods

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Table 3. Results of models optimized by different pretreatment methods

Pretreatmentmethod	Origin	R_p	RMSEC	R_c	RMSEP
Hybridmodeling	Sichuanand Yunnan	0.49	0.66	0.46	0.61
Originalspectra	Sichuan	0.82	0.37	0.89	0.33
	Yunnan	0.80	0.34	0.85	0.29
S-Gsmoothing +3^*	Sichuan	0.74	0.44	0.68	0.52
	Yunnan	0.80	0.34	0.80	0.33
S-Gsmoothing+7^*	Sichuan	0.74	0.44	0.67	0.53
	Yunnan	0.77	0.35	0.71	0.39
Normalization	Sichuan	0.64	0.50	0.67	0.53
	Yunnan	0.69	0.41	0.78	0.34
MSC	Sichuan	0.63	0.50	0.63	0.56
	Yunnan	0.71	0.41	0.77	0.35
Baseline	Sichuan	0.82	0.37	0.90	0.31
	Yunnan	0.81	0.33	0.87	0.27
Baseline+S-G	Sichuan	0.74	0.44	0.67	0.53
smoothing+3^*	Yunnan	0.78	0.34	0.82	0.31

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Yande Liu, Yu Zhang, Hai Xu, Xiaogang Jiang, Junzheng Wang. Detection of Sugar Content of Pomegranates from Different Producing Areas Based on Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013002

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