基于矿物元素指纹技术的‘富士’苹果产地溯源

doi:10.3864/j.issn.0578-1752.2022.21.012

中国农业科学 ›› 2022, Vol. 55 ›› Issue (21): 4252-4264.doi: 10.3864/j.issn.0578-1752.2022.21.012

基于矿物元素指纹技术的‘富士’苹果产地溯源

高小琴¹(),聂继云²(),陈秋生³,韩令喜²,刘璐³,程杨¹,刘明雨²

¹中国农业科学院果树研究所，辽宁兴城 125100
²青岛农业大学园艺学院/农业农村部果品质量安全风险评估实验室（青岛）/全国名特优新农产品（园艺产品）全程质量控制技术青岛中心/青岛市现代农业质量与安全工程重点实验室，山东青岛 266109
³天津市农业科学院农产品质量安全与营养研究所，天津 300381

收稿日期:2022-02-16 接受日期:2022-04-27 出版日期:2022-11-01 发布日期:2022-11-09
通讯作者: 聂继云
作者简介:高小琴，E-mail：gaoxiaoqin5280@163.com。
基金资助:
国家苹果产业技术体系(CARS-27);青岛农业大学高层次人才基金(665-1120015)

Geographical Origin Tracing of Fuji Apple Based on Mineral Element Fingerprinting Technology

GAO XiaoQin¹(),NIE JiYun²(),CHEN QiuSheng³,HAN LingXi²,LIU Lu³,CHENG Yang¹,LIU MingYu²

¹Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng 125100, Liaoning
²College of Horticulture, Qingdao Agricultural University/Laboratory of Quality & Safety Risk Assessment for Fruit (Qingdao), Ministry of Agriculture and Rural Affairs/National Technology Centre for Whole Process Quality Control of FSEN Horticultural Products (Qingdao)/Qingdao Key Lab of Modern Agriculture Quality and Safety Engineering, Qingdao 266109, Shandong
³Institute of Agricultural Product Quality, Safety and Nutrition, Tianjin Academy of Agricultural Sciences, Tianjin 300381

Received:2022-02-16 Accepted:2022-04-27 Online:2022-11-01 Published:2022-11-09
Contact: JiYun NIE

摘要/Abstract

摘要：

【目的】探讨‘富士’苹果果皮矿物元素含量的地域特征及产地溯源的可行性，结合多元统计分析，筛选出有效的判别指标，建立‘富士’苹果产地溯源模型，实现‘富士’苹果产地识别。【方法】以我国两大主产区（渤海湾产区和黄土高原产区）的124份‘富士’苹果为研究对象，采用电感耦合等离子质谱技术（ICP-MS）测定果皮中常量元素钠（Na）、镁（Mg）、钾（K）、钙（Ca），微量元素钒（V）、铬（Cr）、锰（Mn）、铁（Fe）、钴（Co）、镍（Ni）、铜（Cu）、锌（Zn）、砷（As）、钼（Mo）、镉（Cd）、锑（Sb）、钡（Ba）、铅（Pb）、铀（U），稀土元素钇（Y）、镧（La）、铯（Ce）、镨（Pr）、钕（Nd）、钐（Sm）、铕（Eu）、钆（Gd）、铽（Tb）、镝（Dy）、钬（Ho）、铒（Er），共31种矿物元素的含量，并结合独立样本T检验、主成分分析、线性判别分析、正交偏最小二乘判别分析进行‘富士’苹果的产地溯源。【结果】渤海湾产区和黄土高原产区的果皮样品中矿物元素Mg、Ca、Na、Fe、Mn、Cu、Ba、Ni、Nd、Pb、V、Ce、Pr、La、Dy、U、Ho和Co含量差异显著（P<0.05）。主成分分析结果表明，提取的12个主成分累计方差贡献率为81%，可对两大产区的样品进行初步聚类。利用线性判别分析，筛选出10种矿物元素（Mg、Ca、Cr、Mn、Fe、Ni、Gd、Tb、Dy、U）作为判别两大产区‘富士’苹果地域来源较好的指标，所建立的判别模型，对原始整体判别率为92%，交叉验证判别率为89.5%。通过正交偏最小二乘判别分析，确定Co、Ba、Ho、Dy、Pr这5种矿物元素在样品分类中起关键作用，模型的产地鉴别准确率可达98%，实现了两个产区‘富士’苹果的产地溯源。【结论】‘富士’苹果果皮可作为一种有效的溯源部位。稀土元素Dy、Ho、Pr、Gd、Tb的含量可作为‘富士’苹果产地溯源的重要指标。本研究结果可为‘富士’苹果产地溯源提供理论依据和技术支撑。

关键词: ‘富士’苹果, 多元统计分析, 矿物元素, 溯源, 产区

Abstract:

【Objective】 The aim of this study was to explore the geographical characteristics of the mineral element content in Fuji apple peel and the feasibility of geographical origin tracing, to screen out effective discriminant indicators by combining multivariate statistical analysis, and to establish an origin tracing model, so as to realize Fuji apple geographical origin identification. 【Method】 The 124 Fuji apple samples were collected from the two main production areas in China, namely, the Bohai Bay production area and the Loess Plateau production area, which were taken as the research object. Inductively coupled plasma-mass spectrometry (ICP-MS) was applied to determine the contents of 31 mineral elements in the peel, including macroelements (Sodium (Na), Magnesium (Mg), Potassium (K), and Calcium (Ca)), microelements (Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Molybdenum (Mo), Cadmium (Cd), Antimony (Sb), Barium (Ba), Lead (Pb), and Uranium (U)), and rare earth elements (Yttrium (Y), Lanthanum (La), Caesium (Ce), Praseodymium (Pr), Neodymium (Nd), Samarium (Sm), Europium (Eu), Gadolinium (Gd), Terbium (Tb), Dysprosium (Dy), Holmium (Ho), and Erbium (Er)). The independent samples t-test, principal component analysis (PCA), linear discriminant analysis, and orthogonal partial least squares discriminant analysis were conducted for geographical origin tracing. 【Result】 The mineral elements Mg, Ca, Na, Fe, Mn, Cu, Ba, Ni, Nd, Pb, V, Ce, Pr, La, Dy, U, Ho and Co in the peel samples from the Bohai Bay and Loess Plateau production areas were significantly different (P<0.05). The results of the PCA showed that the cumulative variance contribution rate of the 12 extracted principal components was 81%, which allowed the preliminary clustering of the samples from the two major production areas. After linear discriminant analysis, 10 mineral elements (Mg, Ca, Cr, Mn, Fe, Ni, Gd, Tb, Dy, and U) were screened as the ideal indicators to discriminate the geographical origin of Fuji apples in the two major production areas. The discriminant rate of the established discriminant model for the original whole was 92%, and the cross-validation discriminant rate was 89.5%. The orthogonal partial least squares discriminant analysis showed that five mineral elements, Co, Ba, Ho, Dy and Pr, played the key roles in the sample classification, and the accuracy of origin identification by the model could reach 98%, which realized the origin traceability of Fuji apples in the two production areas. 【Conclusion】 The peel of Fuji apple could be used as an effective site for origin tracing. The contents of rare earth elements Dy, Ho, Pr, Gd and Tb were important indicators for the geographical origin tracing of Fuji apples. This study provided a theoretical basis and technical support for Fuji apple origin tracing.

Key words: Fuji apple, multivariate statistical analysis, mineral elements, traceability, production area

高小琴,聂继云,陈秋生,韩令喜,刘璐,程杨,刘明雨. 基于矿物元素指纹技术的‘富士’苹果产地溯源[J]. 中国农业科学, 2022, 55(21): 4252-4264.

GAO XiaoQin,NIE JiYun,CHEN QiuSheng,HAN LingXi,LIU Lu,CHENG Yang,LIU MingYu. Geographical Origin Tracing of Fuji Apple Based on Mineral Element Fingerprinting Technology[J]. Scientia Agricultura Sinica, 2022, 55(21): 4252-4264.

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https://www.chinaagrisci.com/CN/Y2022/V55/I21/4252

图/表 7

表1

‘富士’苹果样品采样信息一览表"

序号 Serial number	产地 Place of origin	序号 Serial number	产地 Place of origin	序号 Serial number	产地 Place of origin	序号 Serial number	产地 Place of origin
1	烟台 Yantai	2	烟台 Yantai	3	威海 Weihai	4	威海 Weihai
5	威海 Weihai	6	威海 Weihai	7	威海 Weihai	8	威海 Weihai
9	威海 Weihai	10	威海 Weihai	11	威海 Weihai	12	威海 Weihai
13	泰安 Tai’an	14	泰安 Tai’an	15	泰安 Tai’an	16	泰安 Tai’an
17	泰安 Tai’an	18	泰安 Tai’an	19	泰安 Tai’an	20	泰安 Tai’an
21	泰安 Tai’an	22	泰安 Tai’an	23	青岛 Qingdao	24	青岛 Qingdao
25	青岛 Qingdao	26	保定 Baoding	27	保定 Baoding	28	保定 Baoding
29	保定 Baoding	30	保定 Baoding	31	保定 Baoding	32	保定 Baoding
33	保定 Baoding	34	保定 Baoding	35	衡水 Hengshui	36	衡水 Hengshui
37	石家庄 Shijiazhuang	38	石家庄 Shijiazhuang	39	石家庄 Shijiazhuang	40	石家庄 Shijiazhuang
41	石家庄 Shijiazhuang	42	石家庄 Shijiazhuang	43	石家庄 Shijiazhuang	44	石家庄 Shijiazhuang
45	石家庄 Shijiazhuang	46	营口 Yingkou	47	营口 Yingkou	48	营口 Yingkou
49	营口 Yingkou	50	营口 Yingkou	51	营口 Yingkou	52	营口 Yingkou
53	营口 Yingkou	54	营口 Yingkou	55	营口 Yingkou	56	渭南 Weinan
57	渭南 Weinan	58	渭南 Weinan	59	渭南 Weinan	60	渭南 Weinan
61	渭南 Weinan	62	渭南 Weinan	63	延安 Yan’an	64	延安 Yan’an
65	延安 Yan’an	66	延安 Yan’an	67	延安 Yan’an	68	延安 Yan’an
69	延安 Yan’an	70	延安 Yan’an	71	咸阳 Xianyang	72	咸阳 Xianyang
73	咸阳 Xianyang	74	咸阳 Xianyang	75	咸阳 Xianyang	76	咸阳 Xianyang
77	咸阳 Xianyang	78	咸阳 Xianyang	79	咸阳 Xianyang	80	平凉 Pingliang
81	平凉 Pingliang	82	平凉 Pingliang	83	平凉 Pingliang	84	平凉 Pingliang
85	平凉 Pingliang	86	天水 Tianshui	87	天水 Tianshui	88	天水 Tianshui
89	天水 Tianshui	90	天水 Tianshui	91	天水 Tianshui	92	天水 Tianshui
93	天水 Tianshui	94	天水 Tianshui	95	天水 Tianshui	96	运城 Yuncheng
97	运城 Yuncheng	98	运城 Yuncheng	99	运城 Yuncheng	100	运城 Yuncheng
101	运城 Yuncheng	102	运城 Yuncheng	103	运城 Yuncheng	104	运城 Yuncheng
105	运城 Yuncheng	106	晋中 Jinzhong	107	晋中 Jinzhong	108	晋中 Jinzhong
109	临汾 Linfen	110	临汾 Linfen	111	临汾 Linfen	112	临汾 Linfen
113	临汾 Linfen	114	临汾 Linfen	115	临汾 Linfen	116	三门峡 Sanmenxia
117	三门峡 Sanmenxia	118	三门峡 Sanmenxia	119	三门峡 Sanmenxia	120	三门峡 Sanmenxia
121	三门峡 Sanmenxia	122	三门峡 Sanmenxia	123	三门峡 Sanmenxia	124	三门峡 Sanmenxia

表1

表2

表3

前12个主成分中各变量的成分矩阵及累计方差贡献率"

元素 Element	主成分 Principal component
元素 Element	1	2	3	4	5	6	7	8	9	10	11	12
Dy	0.793	-0.463	0.175	-0.070	0.062	0.125	0.018	0.024	-0.010	0.028	0.010	0.038
Nd	0.786	-0.423	0.100	-0.121	-0.021	0.145	-0.088	-0.008	0.021	0.032	0.023	-0.086
Er	0.754	0.214	0.012	-0.261	-0.070	-0.025	0.097	-0.074	-0.043	-0.143	-0.198	0.056
Ho	0.729	-0.436	0.132	-0.214	-0.045	0.112	-0.095	-0.012	-0.035	-0.049	0.237	0.016
Pr	0.691	-0.440	0.143	-0.231	-0.039	0.136	-0.108	0.007	0.009	-0.043	0.247	-0.034
Gd	0.668	-0.141	-0.169	-0.077	-0.184	0.232	0.027	-0.213	-0.045	-0.045	-0.054	-0.027
Fe	0.646	0.408	0.221	0.281	0.009	-0.224	0.030	-0.141	-0.068	0.038	0.002	-0.193
Y	0.611	0.604	-0.008	-0.142	-0.126	-0.113	-0.008	-0.002	0.076	-0.097	-0.225	-0.010
Eu	0.608	0.414	-0.142	0.041	-0.258	-0.396	-0.039	-0.185	0.046	-0.094	0.052	0.071
Tb	0.592	-0.313	-0.056	-0.095	-0.075	0.032	0.077	-0.111	-0.232	0.298	0.011	-0.019
V	0.567	0.014	0.171	0.432	0.217	-0.240	0.147	0.193	0.116	-0.079	0.011	0.012
Mg	-0.232	0.640	0.305	-0.235	0.103	0.194	0.120	-0.160	-0.103	-0.119	0.247	-0.021
Na	0.144	0.586	0.411	0.052	-0.069	-0.027	-0.331	-0.018	0.066	0.193	0.008	-0.036
K	-0.198	0.538	0.382	0.134	0.067	0.117	-0.205	-0.199	-0.128	-0.089	0.401	-0.085
Mn	-0.082	0.525	0.414	-0.188	0.193	0.335	0.228	-0.165	0.034	-0.206	0.039	-0.076
Pb	0.263	0.495	-0.492	-0.042	0.269	0.241	-0.229	0.237	-0.139	-0.006	-0.177	0.117
Sm	0.416	0.477	-0.200	0.014	-0.146	-0.422	-0.142	-0.122	0.270	-0.233	0.013	-0.039
Sb	0.215	0.370	-0.330	0.192	0.216	0.183	-0.296	0.140	0.262	0.281	0.090	-0.028
Ca	0.183	0.310	-0.628	-0.433	0.162	-0.088	0.184	0.022	0.044	0.161	0.257	-0.031
Ba	0.071	0.262	-0.593	-0.294	0.164	-0.113	0.343	0.161	-0.030	0.065	0.408	-0.009
Cu	0.134	0.382	0.448	-0.099	-0.408	0.190	0.089	0.431	0.091	0.279	0.016	0.052
La	0.035	0.026	-0.430	0.417	-0.380	0.426	0.095	-0.170	0.182	-0.170	0.163	0.152
Ce	0.124	0.098	-0.438	0.523	-0.280	0.432	0.090	-0.150	0.141	-0.080	-0.020	0.054
Mo	0.363	0.160	-0.043	0.482	0.268	-0.146	0.301	0.315	-0.236	0.042	0.010	0.180
U	0.087	0.402	0.271	-0.178	-0.531	0.176	0.308	0.416	0.077	0.025	-0.035	0.060
Zn	0.171	0.332	-0.114	-0.328	0.453	0.346	0.146	-0.161	0.084	0.008	-0.431	-0.094
As	0.333	0.284	0.002	0.105	0.192	0.193	-0.550	0.148	-0.380	-0.070	0.067	0.285
Ni	0.151	-0.120	0.255	0.041	0.365	0.043	-0.090	-0.080	0.676	0.325	0.084	0.103
Cr	0.229	0.231	0.051	0.433	0.024	0.072	0.291	-0.290	-0.287	0.486	-0.024	-0.269
Cd	0.254	-0.167	0.000	0.264	0.242	0.160	0.016	0.409	0.091	-0.384	0.061	-0.575
Co	0.263	-0.097	0.411	0.164	0.401	0.007	0.393	-0.097	0.068	-0.188	0.055	0.444
特征值Eigenvalue	6.070	4.373	2.826	2.039	1.773	1.501	1.376	1.189	1.110	1.052	0.094	0.854
方差贡献率 Variance contribution rate (%)	19.58	14.11	9.12	6.58	5.72	4.84	4.44	3.83	3.58	3.39	3.04	2.76
累计贡献率 Cumulative variance (%)	19.58	33.69	42.81	49.39	55.11	59.95	64.39	68.22	71.80	75.19	78.23	80.99

表3

图1

表4

图2

图3

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元素 Element	渤海湾产区 Bohai Bay production area	黄土高原产区 The Loess Plateau production area
K	1811±441a	1710±302a
Mg	168.9±30.0a	159.7±18.6b
Ca	121.2±17.9b	155.4 ±32.1a
Na	10.61±6.11a	8.41±4.18b
Fe	6.16±1.78a	5.02±1.73b
Mn	1.60±0.63a	1.31±0.30b
Cu	0.70±0.45a	0.51±0.14b
Zn	0.61±0.15a	0.67±0.42a
Cr	0.23±0.14a	0.23±0.14a
Ba	0.19±0.09b	0.32±0.16a
Ni	0.07±0.04a	0.05±0.03b
Mo	0.03±0.02a	0.03±0.01a
Nd	0.013±0.017a	0.007±0.011b
Pb	0.013±0.006b	0.016±0.008a
As	0.0088±0.0139a	0.0063±0.0033a
V	0.0068±0.0054a	0.0049±0.0024b
Ce	0.0062±0.0035b	0.0118±0.0169a
Pr	0.0049±0.0082a	0.0019±0.0024b
La	0.0040±0.0026b	0.0093±0.0173a
Dy	0.0023±0.0029a	0.0010±0.0018b
Y	0.0018±0.0008a	0.0016±0.0010a
Sb	0.0012±0.0007a	0.0013±0.0007a
U	0.0012±0.0011a	0.0008±0.0006b
Cd	0.0010±0.0029a	0.0008±0.0005a
Ho	0.000697±0.001250a	0.000258±0.000503b
Gd	0.000589±0.000369a	0.000675±0.000722a
Sm	0.000439±0.000240a	0.000470±0.000420a
Er	0.000185±0.000101a	0.000176±0.000123a
Co	0.000143±0.000199a	0.000034±0.000027b
Eu	0.000109±0.000051a	0.000110±0.000069a
Tb	0.000089±0.000077a	0.000074±0.000060a

验证方法 Verification method	产区 Production area	预测组成员 Forecast group members
验证方法 Verification method	产区 Production area	渤海湾 Bohai Bay (%)	黄土高原 Loess Plateau (%)
原始 Original	渤海湾 Bohai Bay	90.9	9.1
原始 Original	黄土高原 Loess Plateau	7.2	92.8
交叉验证 Cross validated ^c	渤海湾 Bohai Bay	87.3	12.7
交叉验证 Cross validated ^c	黄土高原 Loess Plateau	8.7	91.3

基于矿物元素指纹技术的‘富士’苹果产地溯源

Geographical Origin Tracing of Fuji Apple Based on Mineral Element Fingerprinting Technology

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