Analysis on the multi-elements origin discrimination model of wolfberry based on interannual changes

doi:10.6048/j.issn.1001-4330.2025.06.017

Xinjiang Agricultural Sciences ›› 2025, Vol. 62 ›› Issue (6): 1453-1460.DOI: 10.6048/j.issn.1001-4330.2025.06.017

• Horticultural Special Local Products·Agricultural Product Processing Engineering • Previous Articles Next Articles

Analysis on the multi-elements origin discrimination model of wolfberry based on interannual changes

CHEN Zhuoya¹^,²(), NIE Fang¹^,², LIU Hejiang², JU Yanjun², GOU Chunlin³, NIU Shuhui², KANG Lu², ZHAO Duoyong²()

1. College of Food Sciences and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China
2. Institute of Quality Standards & Testing Technology for Agro-Products, Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences/Laboratory of Quality & Safety Risk Assessment for Agro-Products(Urumqi), Ministry of Agriculture and Rural Affairs/Xinjiang Key Laboratory of Agro-Products Quality and Safety,Urumqi 830091, China
3. Institute of Quality Standard and Testing Technology for Agro-Products of Ningxia, Yinchuan 750002, China

Received:2024-11-05 Online:2025-06-20 Published:2025-07-29
Correspondence author: ZHAO Duoyong
Supported by:
Nuclear Energy Development Program of the National Defense Science and Industry Administration(NDSA)“Application of stable isotopes in authenticity identification and geographical origin traceability of Jinghe wolfberry”;Xinjiang Uyghur Autonomous Region Resource Sharing Platform Construction Project(PT2312);Xinjiang Uyghur Autonomous Region key research and development special project(2023B02009-4)

基于年际变化的枸杞多元素产地判别模型稳定性分析

陈卓雅¹^,²(), 聂芳¹^,², 刘河疆², 琚艳君², 苟春林³, 牛淑慧², 康露², 赵多勇²()

1.新疆农业大学食品科学与药学学院,乌鲁木齐 830052
2.新疆维吾尔自治区农业科学院农业质量标准与检测技术研究所/农业农村部农产品质量安全风险评估实验室(乌鲁木齐)/新疆农产品质量安全重点实验室,乌鲁木齐 830091
3.宁夏农产品质量标准与检测技术研究所,银川 750002

通讯作者: 赵多勇
作者简介:陈卓雅(1998-),女,山西忻州人,硕士研究生,研究方向为食品加工与安全,(E-mail)1554859778@qq.com
基金资助:
国防科工局核能开发项目子课题“稳定同位素在精河枸杞真实性鉴别与产地溯源中的应用研究”;新疆维吾尔自治区资源共享平台建设项目(PT2312);新疆维吾尔自治区重点研发专项子课题(2023B02009-4)

Abstract

Abstract:

【Objective】 This project aims to analyze the contents of 15 mineral elements in wolfberry and compare the differences in mineral element content of fruit from different years and geographical origins. The purpose is to establish a authenticity identification model based on multiple mineral element analysis and to explore the stability and reliability of authenticity identification methods for origin. 【Methods】 A total of 264 wolfberry fruit samples were collected from Jinghe County of Xinjiang and Zhongning County of Ningxia for 3 consecutive years (2020-2022). The contents of 15 mineral elements such as Na, Mo, Mn, Rb, Mg, Ca, Cu, Sr, Cr, Ni, K, Co, Ba, Fe and Zn were analyzed by inductively coupled plasma mass spectrometer, and the origin was identified by analysis of variance and orthogonal partial least square discriminant analysis (OPLS-DA). 【Results】 There were significant differences in 7 mineral elements of Na, Cu, Sr, Mg, Mo, Rb and Mn between Jinghe and Zhongning (P<0.05). 13 elements of Na, Mo, Mn, Rb, Mg, Ca, Cu, Sr, Cr, K, Co, Ba, Zn showed significant differences between the years (P<0.05). Based on the above 15 mineral elements, the origin discrimination model established with the samples of 2020 as the training set could 100% distinguish Jinghe and Zhongning wolfberry. The blind sample in 2021 was used for verification, the discriminant accuracy was 73.68%. The discrimination accuracy of the model based on the samples from 2020 to 2021 was 96.12%, and the discrimination accuracy of the model based on the samples from 2022 was 66.67%. 【Conclusion】 Although the discriminant model based on a variety of mineral elements in a single year can effectively distinguish Jinghe and Zhongning wolfberry, and the accuracy rate can reach more than 95%, the accuracy rate of discriminating blind samples is relatively low, indicating that the stability and reliability of the discriminant model has a strong interannual effect.

Key words: wolfberry; multi-elements; authenticity of origin; inter annual; OPLS-DA

摘要：

【目的】分析枸杞果实中15种矿物元素的含量,比较不同年际、产地来源的枸杞果实矿物元素含量的差异,建立基于多元素分析的枸杞产地真实性鉴别模型,探讨产地真实性鉴别方法的稳定性和可靠性。【方法】连续3年(2020~2022年)采集新疆精河县和宁夏中宁县的枸杞果实,共264份样品。采用电感耦合等离子质谱仪分析Na、Mo、Mn、Rb、Mg、Ca、Cu、Sr、Cr、Ni、K、Co、Ba、Fe和Zn 15种矿物元素的含量,同时结合方差分析和正交偏最小二乘法-判别分析(OPLS-DA)方法鉴别产地。【结果】精河枸杞和中宁枸杞Na、Cu、Sr、Mg、Mo、Rb和Mn等7种矿物元素存在显著性差异(P<0.05);Na、Mo、Mn、Rb、Mg、Ca、Cu、Sr、Cr、K、Co、Ba和Zn等13种元素在年际之间存在显著性差异(P<0.05),基于上述15种矿物元素,以2020年的样本作为训练集建立的产地判别模型可100%区分精河枸杞和中宁枸杞,以2021年的样本作为盲样进行验证,其判别准确率为73.68%。以2020~2021年两年的样本建立的判别模型,两个产地判别准确率为96.12%,以2022年的样本作为盲样进行验证,其判别准确率为66.67%。【结论】虽然基于单一年份多种矿物元素建立的判别模型可有效区分精河枸杞和中宁枸杞,且准确率均可达95%以上,但对盲样判别的准确率相对较低,说明判别模型的稳定性和可靠性有很强的年际效应。

关键词: 枸杞, 多元素, 产地真实性, 年际, OPLS-DA

CLC Number:

S663.9

CHEN Zhuoya, NIE Fang, LIU Hejiang, JU Yanjun, GOU Chunlin, NIU Shuhui, KANG Lu, ZHAO Duoyong. Analysis on the multi-elements origin discrimination model of wolfberry based on interannual changes[J]. Xinjiang Agricultural Sciences, 2025, 62(6): 1453-1460.

陈卓雅, 聂芳, 刘河疆, 琚艳君, 苟春林, 牛淑慧, 康露, 赵多勇. 基于年际变化的枸杞多元素产地判别模型稳定性分析[J]. 新疆农业科学, 2025, 62(6): 1453-1460.

Figures/Tables 8

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产地 Origins	样本量 Sample size	年份 Year	月份 Month	地点 Location
新疆精河县 Jinghe County, Xinjiang	71	2020	6,9	精河县枸杞生态示范采摘园、托里镇
	64	2021	6,9	精河县枸杞生态示范采摘园、托里镇
	33	2022	6,7	精河县枸杞生态示范采摘园、托里镇
宁夏中宁县 Zhongning County, Ningxia	37	2020	6月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村
	35	2021	9月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村
	24	2022	6月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村

产地 Origins	样本量 Sample size	年份 Year	月份 Month	地点 Location
新疆精河县 Jinghe County, Xinjiang	71	2020	6,9	精河县枸杞生态示范采摘园、托里镇
	64	2021	6,9	精河县枸杞生态示范采摘园、托里镇
	33	2022	6,7	精河县枸杞生态示范采摘园、托里镇
宁夏中宁县 Zhongning County, Ningxia	37	2020	6月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村
	35	2021	9月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村
	24	2022	6月	玺赞生态枸杞庄园、中杞枸杞有限公司、长山头乡花豹湾村

元素 Elements	2020	2021	2022
Na	1.89±0.8^b	1.91±0.9^b	2.46±0.5^a
Mg	7.67±1.18^a	6.67±1.01^b	7.52±1.13^a
K	14.87±2.5^a	12.11±2.16^b	15.40±1.68^a
Ca	4.36±1.02^b	5.03±1.20^a	5.03±0.82^a
Fe	0.42±0.01^a	0.43±0.13^a	0.41±0.12^a
Cu	8.86±3.01^a	8.26±1.93^a	9.12±2.25^a
Zn	19.69±6.56^b	28.23±6.40^a	15.32±3.49^c
Sr	3.13±0.78^c	3.89±1.23^b	4.44±1.14^a
Mo	0.30±0.08^b	0.27±0.09^b	0.35±0.10^a
Ba	0.58±0.27^c	1.32±0.53^a	0.92±0.35^b
Rb	1.01±0.51^b	1.26±0.65^a	0.92±0.53^b
Ni	0.44±0.26^a	0.48±0.23^a	0.52±0.22^a
Co	3.31±0.11^b	3.91±0.17^a	4.38±0.13^a
Mn	5.53±1.14^b	5.49±1.23^b	6.06±1.02^a
Cr	0.21±0.07^a	0.16±0.06^b	1.03±0.07^c

元素 Elements	2020	2021	2022
Na	1.89±0.8^b	1.91±0.9^b	2.46±0.5^a
Mg	7.67±1.18^a	6.67±1.01^b	7.52±1.13^a
K	14.87±2.5^a	12.11±2.16^b	15.40±1.68^a
Ca	4.36±1.02^b	5.03±1.20^a	5.03±0.82^a
Fe	0.42±0.01^a	0.43±0.13^a	0.41±0.12^a
Cu	8.86±3.01^a	8.26±1.93^a	9.12±2.25^a
Zn	19.69±6.56^b	28.23±6.40^a	15.32±3.49^c
Sr	3.13±0.78^c	3.89±1.23^b	4.44±1.14^a
Mo	0.30±0.08^b	0.27±0.09^b	0.35±0.10^a
Ba	0.58±0.27^c	1.32±0.53^a	0.92±0.35^b
Rb	1.01±0.51^b	1.26±0.65^a	0.92±0.53^b
Ni	0.44±0.26^a	0.48±0.23^a	0.52±0.22^a
Co	3.31±0.11^b	3.91±0.17^a	4.38±0.13^a
Mn	5.53±1.14^b	5.49±1.23^b	6.06±1.02^a
Cr	0.21±0.07^a	0.16±0.06^b	1.03±0.07^c

元素 Elements	2020	2021	2022
Na	3.36±0.88^a	3.73±0.94^a	1.97±0.93^b
Mg	8.33±1.27^b	9.20±1.02^a	6.86±1.63^c
K	13.13±2.19^b	14.87±2.53^a	13.66±3.90^ab
Ca	5.04±1.09^b	6.08±1.26^a	5.12±1.85^b
Fe	0.43±0.12^a	42.46±9.54^a	37.79±17.90^a
Cu	5.01±1.43^c	6.86±2.04^b	10.15±2.73^a
Zn	14.49±4.68^b	20.93±4.71^b	38.14±24.60^a
Sr	4.54±1.40^a	4.91±1.72^a	4.21±1.41^a
Mo	0.14±0.04^b	0.18±0.06^b	0.27±0.01^a
Ba	0.78±0.39^b	1.09±0.35^a	1.23±0.43^a
Rb	1.45±0.64^a	1.62±0.43^a	0.72±0.04^b
Ni	0.55±0.04^a	0.58±0.23^a	0.44±0.02^a
Co	3.73±0.12^b	4.92±0.16^a	3.42±0.15^b
Mn	6.85±1.30^b	7.79±1.24^a	5.91±1.64^c
Cr	1.26±0.57^b	1.32±0.57^b	2.29±0.89^a

Analysis on the multi-elements origin discrimination model of wolfberry based on interannual changes

基于年际变化的枸杞多元素产地判别模型稳定性分析

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元素 Elements	新疆精河县 Jinghe County, Xinjiang	宁夏中宁县 Zhongning County, Ning xia
Na	2.05±0.83^b	3.21±1.18^a
Mg	7.26±1.19^b	8.25±1.56^a
K	13.92±2.64^a	13.86±2.90^a
Ca	4.76±1.12^a	5.41±1.43^a
Fe	0.43±0.12^a	0.41±0.14^a
Cu	8.69±2.50^a	6.97±2.88^b
Zn	22.76±8.23^a	22.02±6.06^a
Sr	3.68±1.16^b	4.65±1.56^a
Mo	0.31±0.09^a	0.19±0.03^b
Ba	0.93±0.52^a	1.01±0.42^a
Rb	1.09±0.59^b	1.34±0.63^a
Ni	0.47±0.02^a	0.55±0.13^a
Co	3.78±1.51^a	4.18±1.71^a
Mn	5.62±1.16^b	6.91±1.51^a
Cr	1.68±0.77^a	1.56±0.81^a

年份 Years	样本量 Sample size	判别准确率 Discrimi- nation accuracy	正确识别样本数 Correctly identify the number of samples
年份 Years	样本量 Sample size	判别准确率 Discrimi- nation accuracy	新疆精河 Jinghe County, Xinjiang	宁夏中宁 Zhongning County, Ningxia
新疆精河 Jinghe County, Xinjiang (2020)	71	100%	71	0
宁夏中宁 Zhongning County, Ningxia (2020)	37	100%	0	37
盲样 Blind sample (2021)	99	73.68%	39	60
总计Total	207	91.23%	110	97

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