基于矿物元素指纹差异的榴莲产地甄别

doi:10.7506/spkx1002-6630-20200716-218

食品科学 ›› 2021, Vol. 42 ›› Issue (14): 255-262.doi: 10.7506/spkx1002-6630-20200716-218

基于矿物元素指纹差异的榴莲产地甄别

周秀雯，吴浩，陈海泉，颜治，靳保辉，谢丽琪，赵燕，赵超敏，陈辉，潘家荣

（1.中国计量大学生命科学学院，浙江省海洋食品品质及危害物控制技术重点实验室，海洋食品加工质量控制技术与仪器国家地方联合工程实验室，浙江杭州 310018；2.深圳海关食品检验检疫技术中心，广东深圳 518054；3.中国农业科学院农业质量标准与检测技术研究所，北京 100081；4.上海海关动植物与食品检验检疫技术中心，上海 200135；5.中国检验检疫科学研究院，北京 100176）

发布日期:2021-07-27
基金资助:
“十三五”国家重点研发计划重点专项（2017YFF0211302）

Discrimination of Durian from Different Geographical Origins Based on Mineral Element Fingerprint Characteristics

ZHOU Xiuwen, WU Hao, CHEN Haiquan, YAN Zhi, JIN Baohui, XIE Liqi, ZHAO Yan, ZHAO Chaomin, CHEN Hui, PAN Jiarong

(1. National & Local United Engineering Laboratory of Quality Controlling Technology and Instrumentation for Marine Food, Key Laboratory of Marine Food Quality and Hazard Controlling Technology of Zhejiang Province, College of Life Sciences, China Jiliang University, Hangzhou 310018, China; 2. Food Inspection and Quarantine Technical Center, Shenzhen Customs, Shenzhen 518054, China;3. Institute of Quality Standard & Testing Technology for Agro-products, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 4. Technical Center for Animal, Plant and Food Inspection and Quarantine, Shanghai Customs, Shanghai 200135, China;5. Chinese Academy of Inspection and Quarantine, Beijing 100176, China)

Published:2021-07-27

摘要/Abstract

摘要： 为保护高值热带作物榴莲的原产地信息，采集马来西亚、泰国、柬埔寨和越南共4 个产区73 份不同品种榴莲样本，利用电感耦合等离子体质谱法测定榴莲果核与榴莲果肉中28 种矿物元素含量，结合方差分析、主成分分析、Fisher逐步判别分析和BP人工神经网络，建立基于矿物元素的榴莲产地判别模型并验证其准确率。结果表明，榴莲果核和果肉中分别有16 种和13 种矿物元素在4 个产区存在显著差异；主成分分析中前6 个主成分累计贡献率为85.207%，代表矿物元素含量的主要信息；将有显著差异的元素代入Fisher逐步判别方程，结果发现单一榴莲果核及榴莲果肉判别准确率较低，榴莲果核和榴莲果肉耦合指标显著提高判别准确率，筛选出果核中Li、Be、Mg、Mn、Rb元素和果肉中Be、Ag、Ba元素8 项指标构建榴莲产地溯源模型，模型的初始验证准确率为91.8%，交叉验证准确率为90.4%；将有显著差异的元素代入BP人工神经网络模型，榴莲果核As、Ag、Al、Rb和果肉中Ag元素为BP人工神经网络前5重要元素，模型训练验证准确率为96.1%，检验验证准确率为95.5%。初步证明利用矿物元素指纹特征结合化学计量学方法对东南亚产地榴莲判别具有可行性。

关键词: 榴莲；矿物元素指纹；产地；溯源；化学计量学

Abstract: Inductively coupled plasma-mass spectrometry was used to determine the contents of 28 mineral elements in the stone and pulp of 73 durian samples from different cultivars grown in Malaysia, Thailand, Cambodia and Vietnam. The data obtained were analyzed by means of analysis of variance (ANOVA), principal component analysis (PCA), Fisher linear discriminant analysis (FLDA) and back propagation artificial neural network (BP-ANN) to develop and validate a model for discriminating durian from different geographical origins. The results showed that the contents of 16 and 13 mineral elements in the stone and pulp of durian significantly varied among growing areas, respectively. In PCA, the cumulative contribution rates of the first six principal components were higher than 85.207%, which could represent the major information about mineral element contents. The significantly differential elements were substituted into Fisher’s stepwise discrimination equation, and the results showed that the discrimination accuracy for single durian stone and pulp was low. However, combinations of mineral elements in durian stone and pulp significantly improved the discrimination accuracy. Through stepwise discrimination analysis, Li, Be, Mg, Mn and Rb in durian stone and Be, Ag and Ba in durian pulp were selected for modeling, and it turned out that the initial validation accuracy of the model was 91.8%, and the cross validation accuracy was 90.4%. The significantly differential elements were substituted into the BP-ANN model. As a result, As, Ag, Al and Rb in durian stone and Ag in pulp were selected as the top five most important elements for artificial neural network; the validation accuracy was 96.1% and 95.5% for the training and test sets, respectively. Our finding proved that it is feasible to distinguish durian from different southeast Asian countries by mineral fingerprint characteristics combined with chemometrics.

Key words: durian; mineral element fingerprint; geographical origin; traceability; chemometrics

中图分类号:

TS202.1

周秀雯，吴浩，陈海泉，颜治，靳保辉，谢丽琪，赵燕，赵超敏，陈辉，潘家荣. 基于矿物元素指纹差异的榴莲产地甄别[J]. 食品科学, 2021, 42(14): 255-262.

ZHOU Xiuwen, WU Hao, CHEN Haiquan, YAN Zhi, JIN Baohui, XIE Liqi, ZHAO Yan, ZHAO Chaomin, CHEN Hui, PAN Jiarong. Discrimination of Durian from Different Geographical Origins Based on Mineral Element Fingerprint Characteristics[J]. FOOD SCIENCE, 2021, 42(14): 255-262.

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基于矿物元素指纹差异的榴莲产地甄别

Discrimination of Durian from Different Geographical Origins Based on Mineral Element Fingerprint Characteristics

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