Rapid determination of mineral elements in broccoli by near-infrared spectroscopy

doi:10.3969/j.issn.1004-1524.2022.03.15

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (3): 548-556.DOI: 10.3969/j.issn.1004-1524.2022.03.15

• Horticultural Science • Previous Articles Next Articles

Rapid determination of mineral elements in broccoli by near-infrared spectroscopy

LEI Yumeng(), YAN Guochao, YANG Jing, ZHU Zhujun()

College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China

Received:2021-06-07 Online:2022-03-25 Published:2022-03-30
Contact: ZHU Zhujun

Abstract

Abstract:

Broccoli is a cruciferous vegetable with high content of mineral elements. The traditional determination method was cumbersome, time-consuming and laborious. In this study, contents of mineral elements including potassium, sulfur, phosphorus, calcium, ferrum and magnesium in broccoli were determined by inductively coupled plasma atomic emission spectrometry (ICP-OES). At the same time, the samples were scanned by near-infrared spectrometer, and spectral files of samples were obtained. The near-infrared spectral method for rapid determination of mineral elements in broccoli was proposed. On the basis of partial least square (PLS) analysis, chemical analysis results and spectral files were smoothed by Savitzky-Golay convolution. Multivariate scattering correction (MSC) and standard normal variable (SNV) with different scattering processing methods, and the first derivative (FD) and second derivative (SD) of derivative processing methods were used to pretreat the spectra to obtain the calibration equation. The results showed that: (1) K, Mg and Ca had the best results after MSC+FD treatment, coefficient of determination in calibration (RSQ) was 0.884, 0.944 and 0.651, respectively, and coefficient of determination in valibration (R²) was 0.893, 0.928 and 0.604, respectively. Residual predictive deviation (RPD) values were 2.491, 2.710 and 1.344, respectively; (2) P had the best effect after SNV+FD treatment, RSQ, R² and RPD values were 0.733, 0.703 and 1.117, respectively; (3) S and Fe had the best result after MSC+SD treatment, RSQ were 0.523, 0.581, R² were 0.537 and 0.416, respectively, and RPD were 1.133 and 1.100, respectively. The established model for rapid detection of K and Mg in near infrared spectroscopy could be used for practical applications. P could be predicted approximately quantitatively, but the accuracy and stability of the model needed to be improved by increasing samples types; Prediction degree of the near infrared models of Ca, S and Fe could be improved by establishing two models of high concentration and low concentration.

Key words: broccoli, mineral elements, near-infrared spectrum, partial least square method

CLC Number:

S635.3

LEI Yumeng, YAN Guochao, YANG Jing, ZHU Zhujun. Rapid determination of mineral elements in broccoli by near-infrared spectroscopy[J]. Acta Agriculturae Zhejiangensis, 2022, 34(3): 548-556.

Figures/Tables 6

References 23

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组分 Components	样本集 Samples	最大值 Maximum	最小值 Minimum	平均值 Average	标准偏差 Standard deviation
钾K	校正集Calibration	12.610	0.104	10.260	3.228
	验证集Valibration	15.320	1.017	10.350	3.228
硫S	校正集Calibration	1.421	0.803	1.006	0.161
	验证集Valibration	1.293	0.784	1.083	0.144
钙Ca	校正集Calibration	3.101	2.363	2.503	0.245
	验证集Valibration	3.055	2.357	2.514	0.246
磷P	校正集Calibration	3.064	1.560	2.047	0.314
	验证集Valibration	2.609	1.579	2.013	0.263
铁Fe	校正集Calibration	6.610	5.175	5.890	0.318
	验证集Valibration	6.058	5.307	5.903	0.293
镁Mg	校正集Calibration	0.246	0.120	0.173	0.028
	验证集Valibration	0.217	0.125	0.174	0.029

组分 Components	样本集 Samples	最大值 Maximum	最小值 Minimum	平均值 Average	标准偏差 Standard deviation
钾K	校正集Calibration	12.610	0.104	10.260	3.228
	验证集Valibration	15.320	1.017	10.350	3.228
硫S	校正集Calibration	1.421	0.803	1.006	0.161
	验证集Valibration	1.293	0.784	1.083	0.144
钙Ca	校正集Calibration	3.101	2.363	2.503	0.245
	验证集Valibration	3.055	2.357	2.514	0.246
磷P	校正集Calibration	3.064	1.560	2.047	0.314
	验证集Valibration	2.609	1.579	2.013	0.263
铁Fe	校正集Calibration	6.610	5.175	5.890	0.318
	验证集Valibration	6.058	5.307	5.903	0.293
镁Mg	校正集Calibration	0.246	0.120	0.173	0.028
	验证集Valibration	0.217	0.125	0.174	0.029

组分 Components	光谱预处理方法 Spectral treatment method		校正集Calibration		验证集Valibration
组分 Components	光谱预处理方法 Spectral treatment method		RSQ	SEC	R²	SEP	RPD
钾K	多元散射校正	一阶First derivative	0.884	1.550	0.893	1.300	2.491
	MSC	二阶Second derivative	0.923	1.280	0.608	2.270	1.426
	标准正态变量变换	一阶First derivative	0.884	1.560	0.892	1.310	2.472
	SNV	二阶Second derivative	0.925	1.270	0.590	2.310	1.401
硫S	多元散射校正	一阶First derivative	0.434	0.147	0.414	0.136	1.029
	MSC	二阶Second derivative	0.523	0.138	0.537	0.127	1.133
	标准正态变量变换	一阶First derivative	0.432	0.147	0.411	0.135	1.067
	SNV	二阶Second derivative	0.522	0.136	0.536	0.127	1.133
铁Fe	多元散射校正	一阶First derivative	0.479	0.265	0.448	0.322	1.010
	MSC	二阶Second derivative	0.581	0.245	0.416	0.324	1.100
	标准正态变量变换	一阶First derivative	0.480	0.265	0.448	0.322	1.010
	SNV	二阶Second derivative	0.581	0.245	0.416	0.324	1.100
镁Mg	多元散射校正	一阶First derivative	0.944	0.009	0.928	0.011	2.710
	MSC	二阶Second derivative	0.934	0.010	0.899	0.012	2.339
	标准正态变量变换	一阶First derivative	0.943	0.010	0.929	0.011	2.710
	SNV	二阶Second derivative	0.934	0.010	0.898	0.012	2.339
磷P	多元散射校正	一阶First derivative	0.688	0.227	0.655	0.237	1.110
	MSC	二阶Second derivative	0.636	0.241	0.642	0.239	0.980
	标准正态变量变换	一阶First derivative	0.733	0.213	0.703	0.223	1.117
	SNV	二阶Second derivative	0.636	0.241	0.642	0.239	1.100
钙Ca	多元散射校正	一阶First derivative	0.651	0.183	0.604	0.203	1.344
	MSC	二阶Second derivative	0.529	0.205	0.676	0.206	1.200
	标准正态变量变换	一阶First derivative	0.651	0.183	0.590	0.204	1.344
	SNV	二阶Second derivative	0.525	0.205	0.675	0.206	1.200

组分 Components	光谱预处理方法 Spectral treatment method		校正集Calibration		验证集Valibration
组分 Components	光谱预处理方法 Spectral treatment method		RSQ	SEC	R²	SEP	RPD
钾K	多元散射校正	一阶First derivative	0.884	1.550	0.893	1.300	2.491
	MSC	二阶Second derivative	0.923	1.280	0.608	2.270	1.426
	标准正态变量变换	一阶First derivative	0.884	1.560	0.892	1.310	2.472
	SNV	二阶Second derivative	0.925	1.270	0.590	2.310	1.401
硫S	多元散射校正	一阶First derivative	0.434	0.147	0.414	0.136	1.029
	MSC	二阶Second derivative	0.523	0.138	0.537	0.127	1.133
	标准正态变量变换	一阶First derivative	0.432	0.147	0.411	0.135	1.067
	SNV	二阶Second derivative	0.522	0.136	0.536	0.127	1.133
铁Fe	多元散射校正	一阶First derivative	0.479	0.265	0.448	0.322	1.010
	MSC	二阶Second derivative	0.581	0.245	0.416	0.324	1.100
	标准正态变量变换	一阶First derivative	0.480	0.265	0.448	0.322	1.010
	SNV	二阶Second derivative	0.581	0.245	0.416	0.324	1.100
镁Mg	多元散射校正	一阶First derivative	0.944	0.009	0.928	0.011	2.710
	MSC	二阶Second derivative	0.934	0.010	0.899	0.012	2.339
	标准正态变量变换	一阶First derivative	0.943	0.010	0.929	0.011	2.710
	SNV	二阶Second derivative	0.934	0.010	0.898	0.012	2.339
磷P	多元散射校正	一阶First derivative	0.688	0.227	0.655	0.237	1.110
	MSC	二阶Second derivative	0.636	0.241	0.642	0.239	0.980
	标准正态变量变换	一阶First derivative	0.733	0.213	0.703	0.223	1.117
	SNV	二阶Second derivative	0.636	0.241	0.642	0.239	1.100
钙Ca	多元散射校正	一阶First derivative	0.651	0.183	0.604	0.203	1.344
	MSC	二阶Second derivative	0.529	0.205	0.676	0.206	1.200
	标准正态变量变换	一阶First derivative	0.651	0.183	0.590	0.204	1.344
	SNV	二阶Second derivative	0.525	0.205	0.675	0.206	1.200

Rapid determination of mineral elements in broccoli by near-infrared spectroscopy

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