Rapid and Quantitative Detection of Cypermethrin in Spinach Based on Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model

doi:10.19894/j.issn.1000-0518.250115

Chinese Journal of Applied Chemistry ›› 2025, Vol. 42 ›› Issue (11): 1510-1523.DOI: 10.19894/j.issn.1000-0518.250115

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Rapid and Quantitative Detection of Cypermethrin in Spinach Based on Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model

Xiu-Xiu WU¹, Zhi-Min LIU¹, Dong YANG¹, Shun MAO¹, Yan-Xin WANG¹, De-Hua GUO²(), Fei XU¹()

^1.School of Health Science and Engineering，Shanghai Engineering Research Center of Food Rapid Detection，University of Shanghai for Science and Technology，Shanghai 200093，China
^2.Technical Center for Animal Plant and Food Inspection and Quarantine of Shanghai Customs，Shanghai 200135，China

Received:2025-03-15 Accepted:2025-06-16 Published:2025-11-01 Online:2025-12-05
Contact: De-Hua GUO,Fei XU
About author:xufei8135@126.com
guodehua@customs.gov.cn
Supported by:
Program of Shanghai Artificial Intelligence to Promote the Reform of Scientific Research Paradigm Enabling Discipline Leaping Plan(Z-2025-312-023)

Abstract

Abstract:

The residues of pyrethroid pesticides in vegetables and fruits can cause harm to human health. In this work， a surface-enhanced Raman spectroscopy （SERS） coupled with back propagation （BP） neural network deep learning model was established for rapid detection of cypermethrin （CPM） in spinach. Concretely， Ag/ZnO were applied as SERS substrates. Then， the collected SERS spectra were expanded， and the expanded spectral data were preprocessed by Savitzky-Golay （S-G）， mean centralization （MC） and the combination of the two methods. Furthermore， BP neural network models were established for the prediction of CPM in actual samples for the original enhanced spectrum and the pretreated spectrum， respectively， and three traditional machine learning models were established for comparison. It was found that the BP model has the best prediction performance based on the original expanded spectrum， with the prediction set R_P=0.9902， the root mean square error RMSEP=0.102 and the limit of detection 20 μg/L. The two-tailed paired t-tests showed that there was no significant difference between the standard method LC-MS and this method. The detection can be finished in 5~10 min. This work established an Ag/ZnO-based SERS method coupled with BP neural network model for the rapid and quantitative detection of CPM residues in spinach.

Key words: Cypermethrin, Spinach, Surface-enhanced Raman spectroscopy, Deep learning, Back propagation neural network

CLC Number:

O655.9

Xiu-Xiu WU, Zhi-Min LIU, Dong YANG, Shun MAO, Yan-Xin WANG, De-Hua GUO, Fei XU. Rapid and Quantitative Detection of Cypermethrin in Spinach Based on Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model[J]. Chinese Journal of Applied Chemistry, 2025, 42(11): 1510-1523.

Figures/Tables 11

References 38

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Methods	Model	Calibration set		Prediction set
Methods	Model	R_C	RMSEC/（mg·L^-1）	R_P	RMSEP/（mg·L^-1）
None	BP	0.995 9	0.067	0.990 2	0.102
	PLSR	0.800 7	0.426	0.807 4	0.419
	RFR	0.972 2	0.172	0.886 2	0.329
	SVR	0.991 1	0.126	0.874 6	0.359
S-G	BP	0.995 9	0.067	0.985 7	0.123
	PLSR	0.822 9	0.404	0.823 5	0.403
	RFR	0.983 8	0.115	0.929 5	0.244
	SVR	0.991 1	0.126	0.874 6	0.359
MC	BP	0.995 9	0.067	0.985 7	0.123
	PLSR	0.850 5	0.374	0.811 5	0.426
	RFR	0.983 6	0.132	0.937 2	0.249
	SVR	0.991 1	0.126	0.874 6	0.359
S-G+MC	BP	0.985 9	0.119	0.984 9	0.123
	PLSR	0.836 9	0.389	0.834 2	0.392
	RFR	0.988 3	0.112	0.931 3	0.259
	SVR	0.991 4	0.123	0.882 4	0.349

Methods	Model	Calibration set		Prediction set
Methods	Model	R_C	RMSEC/（mg·L^-1）	R_P	RMSEP/（mg·L^-1）
None	BP	0.995 9	0.067	0.990 2	0.102
	PLSR	0.800 7	0.426	0.807 4	0.419
	RFR	0.972 2	0.172	0.886 2	0.329
	SVR	0.991 1	0.126	0.874 6	0.359
S-G	BP	0.995 9	0.067	0.985 7	0.123
	PLSR	0.822 9	0.404	0.823 5	0.403
	RFR	0.983 8	0.115	0.929 5	0.244
	SVR	0.991 1	0.126	0.874 6	0.359
MC	BP	0.995 9	0.067	0.985 7	0.123
	PLSR	0.850 5	0.374	0.811 5	0.426
	RFR	0.983 6	0.132	0.937 2	0.249
	SVR	0.991 1	0.126	0.874 6	0.359
S-G+MC	BP	0.985 9	0.119	0.984 9	0.123
	PLSR	0.836 9	0.389	0.834 2	0.392
	RFR	0.988 3	0.112	0.931 3	0.259
	SVR	0.991 4	0.123	0.882 4	0.349

Actual concentration/（mg·L^-1）	Detected concentration/（mg·L^-1）		Recovery ratio/%		RSD/%
Actual concentration/（mg·L^-1）	Proposed method	HPLC-MS	Proposed method	HPLC-MS	Proposed method	HPLC-MS
0.08	0.076±0.005	0.080±0.001	95.0	100.0	6.6	1.3
0.6	0.580±0.030	0.589±0.017	96.7	98.2	5.2	2.9
0.8	0.853±0.055	0.829±0.022	106.6	103.6	6.4	2.7
1.5	1.638±0.024	1.466±0.036	109.2	97.7	1.5	2.5
2.5	2.262±0.170	2.507±0.021	90.5	100.3	7.5	0.8

Actual concentration/（mg·L^-1）	Detected concentration/（mg·L^-1）		Recovery ratio/%		RSD/%
Actual concentration/（mg·L^-1）	Proposed method	HPLC-MS	Proposed method	HPLC-MS	Proposed method	HPLC-MS
0.08	0.076±0.005	0.080±0.001	95.0	100.0	6.6	1.3
0.6	0.580±0.030	0.589±0.017	96.7	98.2	5.2	2.9
0.8	0.853±0.055	0.829±0.022	106.6	103.6	6.4	2.7
1.5	1.638±0.024	1.466±0.036	109.2	97.7	1.5	2.5
2.5	2.262±0.170	2.507±0.021	90.5	100.3	7.5	0.8

Methods	Pesticides	LOD/（μg·L^-1）	Ref.
Banded immunoassay	Fenpropathrin	62	［33］
Colorimetric sensor	Fenvalerate	4053	［34］
Enzyme linked immunosorbent assay	Cypermethrin	300	［35］
Lateral flow immunoassay	Cypermethrin	50	［36］
Fluorescence sensor	Fenpropathrin	0.24	［37］
Electrochemical sensor	Fenpropathrin	4.63×10^-3	［38］
SERS combined with BP	Cypermethrin	20	This work

Rapid and Quantitative Detection of Cypermethrin in Spinach Based on Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model

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