青花菜中矿质元素的近红外光谱快速测定

doi:10.3969/j.issn.1004-1524.2022.03.15

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (3): 548-556.DOI: 10.3969/j.issn.1004-1524.2022.03.15

青花菜中矿质元素的近红外光谱快速测定

雷雨梦(), 闫国超, 杨静, 朱祝军()

浙江农林大学园艺科学学院,浙江杭州 311300

收稿日期:2021-06-07 出版日期:2022-03-25 发布日期:2022-03-30
通讯作者: 朱祝军
作者简介:*朱祝军,E-mail: zhuzj@zafu.edu.cn
雷雨梦(1995—),女,湖南常德人,硕士研究生,研究方向为园艺科学。E-mail: lymmhy@outlook.com
基金资助:
国家西兰花良种重大科研联合攻关方案(浙农专发〔2018〕67号)

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

摘要/Abstract

摘要：

青花菜矿质元素含量丰富,传统的青花菜矿质养分评价方法步骤繁琐、耗时费力。通过电感耦合等离子体原子发射光谱法(Inductively Coupled Plasma Optical Emission Spectrometer,ICP-OES)测定青花菜中钾(K)、硫(S)、磷(P)、钙(Ca)、铁(Fe)、镁(Mg)等矿质元素含量,同时使用近红外光谱仪扫描样品,获取样品光谱文件,拟建立青花菜矿质元素的近红外光谱快速测定的方法。对化学分析结果与光谱文件在偏最小二乘法(partial least square,PLS)分析的基础上,通过Savitzky-Golay卷积平滑处理,采用不同的散射处理方式[多元散射校正(multiplicative scatter correction,MSC)和标准正态变量变换(standard normal variate transformation,SNV)],以及不同导数处理方式[一阶导数(first derivative, FD)和二阶导数(second derivative,SD)]对光谱进行预处理,从而获得定标方程。结果表明:(1)K、Mg、Ca经过MSC+FD处理的结果最好,校正相关系数(coefficient of determination in calibration, RSQ)分别为0.884、0.944、0.651,验证决定系数(coefficient of determination in valibration, R²)分别为0.893、0.928、0.604,相对分析误差(residual predictive deviation, RPD)分别为2.491、2.710、1.344;(2)P经过SNV+FD处理的效果最好,RSQ、R²和RPD分别为0.733、0.703和1.117;(3)S、Fe经过MSC+SD处理的结果最好,RSQ分别为0.523、0.581,R²分别为0.537和0.416,RPD分别为1.133、1.100。建立的K和Mg的近红外光谱快速检测模型,可以用于实际应用;P可以近似定量预测,但还需要通过增加样品种类提高模型的准确度与稳定度;Ca、S和Fe的近红外模型可以通过建立高浓度和低浓度2个模型来提高模型预测度。

关键词: 青花菜, 近红外光谱, 偏最小二乘法, 矿质元素

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

中图分类号:

S635.3

雷雨梦, 闫国超, 杨静, 朱祝军. 青花菜中矿质元素的近红外光谱快速测定[J]. 浙江农业学报, 2022, 34(3): 548-556.

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.

图/表 6

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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

青花菜中矿质元素的近红外光谱快速测定

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

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