Qualitative and quantitative detection of camellia oil adulteration based on electronic nose

Authors

JIANG Han, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
LI Da-peng, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, ChinaFollow
WEN Tao, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
YANG Gan, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
GONG Zhong-liang, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China

Corresponding Author(s)

李大鹏（1983—），男，中南林业科技大学讲师，博士。E-mail: dapengli@csuft.edu.cn

Abstract

Objective: This study aims to realize the adulteration detection of camellia oil mixed with rapeseed oil, soybean oil and corn oil. Methods: The electronic nose detection platform was used for the adulteration detection of camellia oil mixed with different proportion of rapeseed oil, soybean oil and corn oil. Firstly, the linear discriminant analysis （LDA） and support vector machine （SVM） were used for the qualitative identification of camellia oil adulteration. Then multilayer perceptron （MLP） and partial least squares regression （PLSR） were used to establish quantitative prediction models for camellia oil adulteration. Results: The accuracy of SVM for qualitative authentication was higher than that of LDA, and the average precision rate, average recall rate and average F1-score were 94.85%, 96.11% and 95.34%, respectively, which were 5.17%, 4.44% and 5.29% higher than those of LDA. For quantitative prediction, MLP outperforms PLSR. In particular, the determination coefficients of MLP were 0.98, 0.99 and 0.98, and the root mean square errors were 4.02%, 1.45% and 3.74%, respectively, for camellia oil mixed with rapeseed oil, soybean oil and corn oil. Conclusion: The SVM-based identification model and MLP-based prediction model can effectively detect oil adulteration by using electronic nose platform.

Publication Date

6-5-2023

First Page

65

Last Page

70

DOI

10.13652/j.spjx.1003.5788.2022.81046

Recommended Citation

Han, JIANG; Da-peng, LI; Tao, WEN; Gan, YANG; and Zhong-liang, GONG (2023) "Qualitative and quantitative detection of camellia oil adulteration based on electronic nose," Food and Machinery: Vol. 39: Iss. 4, Article 12.
DOI: 10.13652/j.spjx.1003.5788.2022.81046
Available at: https://www.ifoodmm.cn/journal/vol39/iss4/12

References

[1] 朱晓阳, 龙奇志, 钟海雁. 炒籽温度对茶油关键香气成分及感官品质的影响[J]. 食品与机械, 2019, 35（5）: 48-54.
[2] 陈永忠, 邓绍宏, 陈隆升, 等. 油茶产业发展新论[J]. 南京林业大学学报（自然科学版）, 2020, 44（1）: 1-10.
[3] TU P S, TUNG Y T. Protective effect of camellia oil （Camellia oleifera Abel.） against ethanol-induced acute oxidative injury of the gastric mucosa in mice[J]. Journal of Agriculture and Food Chemistry, 2017, 65（24）: 4 932-4 941.
[4] 赵瑜亮, 仲山民. 山茶油与常见食用油的理化指标分析比较研究[J]. 安徽农业科学, 2014, 42（32）: 11 434-11 436, 11 439.
[5] 王彬, 石延榜, 吴晓茹, 等. 芝麻油掺伪后定性与定量的研究[J]. 广东化工, 2022, 49（5）: 21-25.
[6] ZHU M T, SHI T, CHEN Y, et al. Prediction of fatty acid composition in camellia oil by ¹H NMR combined with PLS regression[J]. Food Chemistry, 2019, 279（5）: 339-346.
[7] POPESCU R, COSTINEL D, DINCA O R, et al. Discrimination of vegetable oils using NMR spectroscopy and chemo-metrics[J]. Food Control, 2015, 48: 84-90.
[8] 赵婷婷, 王欣, 卢海燕, 等. 低场核磁共振结合主成分分析法在食用油脂品质分析中的应用[J]. 现代食品科技, 2014, 30（9）: 179-185.
[9] 郭文川, 朱德宽, 张乾, 等. 基于近红外光谱的掺伪油茶籽油检测[J]. 农业机械学报, 2020, 51（9）: 350-357.
[10] 杨佳, 武彦文, 李冰宁, 等. 近红外光谱结合化学计量学研究芝麻油的真伪与掺伪[J]. 中国粮油学报, 2014, 29（3）: 114-119.
[11] LI S, ZHU X, ZHANG J, et al. Authentication of pure camellia oil by using near infrared spectroscopy and pattern recognition techniques[J]. Journal of Food Science, 2012, 77（4）: C374-C380.
[12] 万恒兴, 冯丽雄, 余展旺. 基于拉曼光谱的葡萄籽油掺伪定量检测研究[J]. 山东化工, 2021, 50（20）: 68-74.
[13] LIMA T K, MAURIZIO M, DURVAL B M. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil[J]. Food Chemistry, 2020, 333（15）: 1-27.
[14] 邓平建, 梁裕, 杨冬燕, 等. 基于拉曼光谱—聚类分析快速鉴别掺伪油茶籽油[J]. 中国粮油学报, 2016, 31（4）: 72-75.
[15] 艾芳芳, 宾俊, 钟丹, 等. 油茶籽油与不同植物油脂肪酸成分的分析比较[J]. 中国油脂, 2013, 38（3）: 77-80.
[16] 张文, 张琪玮, 闫君, 等. 气相色谱—质谱法测定苦苣菜、葡萄籽超临界萃取物中的脂肪酸含量[J]. 当代化工, 2019, 48（4）: 876-880.
[17] JIANG H, HE Y C, CHEN Q S. Qualitative identification of edible oil storage period using a homemade portable electronic nose combined with multivariate analysis[J]. Journal of theScience of Food and Agriculture, 2020, 101（8）: 3 448-3 456.
[18] KARAMI H, RASEKH M, MIRZAEE-GHALEH E. Application of the E-nose machine system to detect adulterations in mixed edible oils using chemometrics methods[J]. Journal of Food Processing and Preservation, 2020, 44（9）: e14696.
[19] 鲁小利, 王俊. 仿生电子鼻在芝麻油掺伪检测中的应用研究[J]. 粮食与油脂, 2016, 29（6）: 75-77.
[20] TAO W, LIZHANG Z, SHUAI D, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis （Hendel） based on electronic nose[J]. Postharvest Biology and Technology, 2019, 147: 156-165.