Statistical comparison of trace elements in olive oil based on principal component analysis and cluster analysis

  • YANG Wenyi ,
  • CHEN Lin ,
  • ZHOU Xuezhong
Expand
  • 1(School of Computer Science and Engineering, Central South University, Changsha 410083, China)
    2(Institute of Chinese Materia Medica, Hunan Academy of Traditional Chinese Medicine, Changsha 410013, China)
    3(Department of Material and Chemical Engineering, Hunan Institute of Technology, Hengyang 421008, China)

Received date: 2020-04-01

  Online published: 2020-08-17

Abstract

The distribution of trace elements in olive oil and the relationship between trace elements and the producing areas of olive oils were investigated. The olive oil was diluted by kerosene, and 16 trace elements in 18 olive oil samples from different producing areas (China, Italy, Spain, and Greece) were determined using inductively coupled plasma tandem mass spectrometry (ICP-MS/MS). The applicability test showed that there was a strong linear correlation between the trace elements in olive oil. The principal component analysis and cluster analysis were applied to evaluate the distribution of trace elements of the determination results after data standardization. The results of principal component analysis showed that the cumulative variance contribution rate of the first five principal components was 80.961%, which could represent the information of original data matrix. Factor analysis showed that the characteristic elements of olive oil were Na, Ti, Cu, Zn, Sr, and Pb. Cluster analysis clustered 18 olive oil samples into four groups. The content of trace elements in olive oil from different producing areas had obvious differences, which could realize the preliminary discrimination of olive oil from different producing areas. Through principal component analysis and cluster analysis of trace elements, the use of characteristic trace elements could effectively trace and distinguish from the producing areas of olive oil.

Cite this article

YANG Wenyi , CHEN Lin , ZHOU Xuezhong . Statistical comparison of trace elements in olive oil based on principal component analysis and cluster analysis[J]. Food and Fermentation Industries, 2020 , 46(14) : 222 -227 . DOI: 10.13995/j.cnki.11-1802/ts.024115

References

[1] KALUA C M, ALLEN M S, BEDGOOD D R, et al. Olive oil volatile compounds, flavour development and quality: A critical review [J]. Food Chemistry, 2007, 100(1): 273-286.
[2] 刘少敏, 何天鹏, 薛丹丹, 等. 六种特级初榨橄榄油挥发性香气成分的比较[J].食品与发酵工业, 2018, 44(6): 251-256.
[3] 邓龙, 陈雄飞, 刘贤标, 等. GC-MS-O结合电子鼻对橄榄油挥发性成分的分析与鉴别[J].中国食品学报, 2019, 19(5): 276-286.
[4] 中华人民共和国国家质量监督检验检疫总局. GB 23347—2009 橄榄油、油橄榄果渣油[S].北京: 中国标准出版社, 2009.
[5] CHOE E, MIN D B. Mechanisms and factors for edible oil oxidation [J]. Comprehensive Reviews in Food Science and Food Safety, 2006, 5(4): 169-186.
[6] ZEINER M, STEFFAN I, CINDRIC I J. Determination of trace elements in olive oil by ICP-AES and ETA-AAS: A pilot study on the geographical characterization [J]. Microchemical Journal, 2005, 81(2): 171-176.
[7] 邱会东, 赵波, 张红, 等. 食用植物油中重金属分析方法的研究进展[J]. 中国油脂, 2017, 42(1): 76-79.
[8] 张萍, 谢华林, 朱乾华, 等. 食用橄榄油中重金属元素的质谱分析[J]. 现代食品科技, 2014, 30(3): 206-209.
[9] 倪张林, 汤富彬, 屈明华. 不同前处理方法测定植物油中重金属的研究[J]. 中国油脂, 2012, 37(7): 85-87.
[10] GONG J, SOLIVIO M J, MERINO E J, et al. Developing ICP-MS/MS for the detection and determination of synthetic DNA-protein crosslink models via phosphorus and sulfur detection [J]. Analytical Bioanalytical Chemistry, 2015, 407: 2 433-2 437.
[11] NELSON J, HOPFER H, SILVA F, et al. Evaluation of GC-ICP-MS/MS as a new strategy for specific heteroatom detection of phosphorus, sulfur, and chlorine determination in foods [J]. Journal of Agricultural and Food Chemistry, 2015, 63(18): 4 478-4 483.
[12] HU X, CAO Z, SUN W, et al. Accurate determination of arsenic and selenium in plant food samples by using ICP-MS/MS [J]. Analytical Methods, 2016, 8(32): 6 150-6 157.
[13] WOLD S, ESBENSEN K, GELADI P. Principal component analysis [J]. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1-3): 37-52.
[14] 倪永年. 化学计量学在分析化学中的应用[M]. 北京: 科学出版社, 2004: 228-229.
[15] LI D, MENG X, LI B. Profiling of anthocyanins from blueberries produced in China using HPLC-DAD-MS and exploratory analysis by principal component analysis [J]. Journal of Food Composition and Analysis, 2016, 47: 1-7.
[16] 郑彦婕, 胡书玉, 黎永乐, 等. 基于无机元素含量的地理标志食醋分类[J]. 食品与发酵工业, 2012, 38(9): 167-169.
[17] RANAMUKHAARACHCHI S A, PEIRIS R H, MORESOLI C. Fluorescence spectroscopy and principal component analysis of soy protein hydrolysate fractions and the potential to assess their antioxidant capacity characteristics [J]. Food Chemistry, 2017, 217: 469-475.
[18] MAIA M, NUNES F M. Authentication of beeswax (Apis mellifera) by high-temperature gas chromatography and chemometric analysis [J]. Food Chemistry, 2013, 136(2): 961-968.
[19] NIE X, LIANG Y, XIE H, et al. Trace amounts of impurities in electrolytic manganese metal by sector field inductively coupled plasma mass spectrometry [J]. Journal of Central South University, 2013, 20(12): 3 385-3 390.
[20] 中华人民共和国国家卫生部. GB 2762—2017 食品安全国家标准食品中污染物限量[S]. 北京: 中国标准出版社, 2017.
[21] PERES-NETO P R, JACKSON D A, SOMERS K M. How many principal components? Stopping rules for determining the number of non-trivial axes revisited [J]. Computational Statistics & Data Analysis, 2005, 49(4): 974-997.
[22] 刘晓燕, 李洪怡, 苏燕, 等. 臭氧和超声波对鲜切莲藕品质影响的主成分分析[J]. 食品与发酵工业, 2018, 44(5): 148-155.
Outlines

/