Comparative analysis of optical characterization parameters for tea oil forensics

Authors

GUAN Jinwei, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
LI Dapeng, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, ChinaFollow
GONG Zhongliang, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
LIU Qiang, School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
JIANG Han, 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] In order to compare the ability of optical characteristic parameters [absorption coefficient （μ_a） and approximate scattering coefficient （μ′_s）] to identify adulterated tea oil and to explore the enhancement effect of the combination of extraction methods on the model to achieve a faster and more accurate identification of different kinds of adulterated oils. [Methods] In this study, vegetable oils were used as experimental materials to prepare adulterated tea oils with different mass fractions. Different preprocessing methods were used to preprocess the optical characteristic parameter data, followed by feature band extraction and subsequent establishment of a Random Forest （RF） qualitative identification model. [Results] After CRAS and UVE-CARS feature extraction, the identification accuracies of the models built using μ_a and μ′_s were 95.65%, 95.65%, and 98.55%, 97.10%, respectively. The combined extraction method （UVE-CARS） resulted in an improvement of at least 1.45 percentage points in the identification results of the models compared with the CARS feature extraction method. [Conclusion] The identification of different adulterated types of tea oil can be realized more quickly and accurately by using μ_a. The combined extraction method can effectively improve the identification ability of the model.

Publication Date

9-11-2024

First Page

30

Last Page

36

DOI

10.13652/j.spjx.1003.5788.2023.81001

Recommended Citation

Jinwei, GUAN; Dapeng, LI; Zhongliang, GONG; Qiang, LIU; and Han, JIANG (2024) "Comparative analysis of optical characterization parameters for tea oil forensics," Food and Machinery: Vol. 40: Iss. 7, Article 6.
DOI: 10.13652/j.spjx.1003.5788.2023.81001
Available at: https://www.ifoodmm.cn/journal/vol40/iss7/6

References

[1] 张群. 特殊医学用途配方食品制备及应用研究[J]. 食品与生物技术学报, 2021, 40（12）: 105. ZHANG Q. Research on the preparation and application of food formulas for special medical purposes[J]. Journal of Food and Biotechnology, 2021, 40（12）: 105.
[2] 陈则铭, 赵鑫鑫, 朱晓阳, 等. 基于浸出茶油香气成分压榨茶油掺伪模型及应用[J]. 食品与机械, 2022, 38（3）: 51-59, 102. CHEN Z M, ZHAO X X, ZHU X Y, et al. Adulteration model and application of pressed tea oil based on the aroma components of leached tea oil[J]. Food & Machinery, 2022, 38（3）: 51-59, 102.
[3] 潘鹏云, 郝莉花, 谢文佳, 等. 气相色谱法鉴别芝麻油掺伪大豆油方法探究[J]. 食品安全导刊, 2023（17）: 89-92. PAN P Y, HAO L H, XIE W J, et al. Identification of sesame oil adulterated with soybean oil by gas chromatography[J]. Food Safety Journal, 2023（17）: 89-92.
[4] MARTIN E S, AVENOZA A, PEREGRINE J M, et al. Solvent-based strategy improves the direct determination of key parameters in edible fats and oils by ¹H NMR[J]. Journal of the Science of Food and Agriculture, 2020, 100（4）: 1 726-1 734.
[5] 李娟, 黄木花, 燕一波, 等. 植物油中掺煎炸动物油的近红外快速无损检测[J]. 食品与机械, 2020, 36（6）: 99-102. LI J, HUANG M H, YAN Y B, et al. Near-infrared rapid nondestructive testing of vegetable oil adulterated with fried animal oil[J]. Food & Machinery, 2020, 36（6）: 99-102.
[6] LU R, VAN BEERS R, SAEYS W, et al. Measurement of optical properties of fruits and vegetables: a review[J]. Postharvest Biology and Technology, 2020, 159: 111003.
[7] LIU D, GUO W, LI Q, et al. Relationship of the bulk optical properties in 950～1 650 nm wavelength range with internal quality and microstructure of kiwifruit during maturation[J]. Biosystems Engineering, 2019, 184: 45-54.
[8] ROWE P I, KUNNEMEYER R, MCGLONE A, et al. Relationship between tissue firmness and optical properties of 'royal gala' apples from 400 to 1 050 nm[J]. Postharvest Biology and Technology, 2014, 94: 89-96.
[9] WANG W, LI C. Measurement of the light absorption and scattering properties of onion skin and flesh at 633 nm[J]. Postharvest Biology and Technology, 2013, 86: 494-501.
[10] 方振欢. 用于水果组织光传输特性检测的单积分球系统研制及应用[D]. 杭州: 浙江大学, 2015: 18-25. FANG Z H. Development and application of a single integrating sphere system for optical transmission characteristics detection of fruit tissues[D]. Hangzhou: Zhejiang University, 2015: 18-25.
[11] SUN J, KUNNEMEYER R, MC G A, et al. Optical properties of healthy and rotten onion flesh from 700 to 1 000 nm[J]. Postharvest Biology and Technology, 2018, 140: 1-10.
[12] 何学明, 庞妍妍, 杨小云, 等. 基于双积分球技术植物油热氧化过程吸收与散射特性变化规律的研究[J]. 中国粮油学报, 2023, 38（4）: 136-142. HE X M, PANG Y Y, YANG X Y, et al. Study on the changing law of absorption and scattering characteristics of vegetable oil during thermal oxidation based on double integrating sphere technique[J]. Chinese Journal of Cereals and Oils, 2023, 38（4）: 136-142.
[13] 庞妍妍, 陈敏, 王蓓, 等. 基于吸收与散射特性的掺伪山茶油检测研究[J]. 粮食科技与经济, 2020, 45（11）: 90-94, 130. PANG Y Y, CHEN M, WANG B, et al. Study on the detection of adulterated camellia oil based on absorption and scattering characteristics[J]. Food Science and Technology and Economy, 2020, 45（11）: 90-94, 130.
[14] PRAHL S A, GEMERT M J C, WELCH A J. Determining the optical properties of turbid media by using the adding-doubling method[J]. Applied Optics, 1993, 32（4）: 559-568.
[15] 张灵枝, 黄艳, 于英杰, 等. 基于近红外光谱技术的六大茶类快速识别[J]. 食品与生物技术学报, 2024, 43（1）: 48-59. ZHANG L Z, HUANG Y, YU Y J, et al. Rapid identification of six major tea species based on near-infrared spectroscopy[J]. Journal of Food and Biotechnology, 2024, 43（1）: 48-59.
[16] CENTNER V, MASSART D L, DE NOORD O E, et al. Elimination of uninformative variables for multivariate calibration[J]. Analytical Chemistry, 1996, 68（21）: 3 851-3 858.
[17] 王菲菲, 刘彭, 孙凤伟, 等. 基于支持向量机和压力传感器的水果分类系统[J]. 食品与机械, 2023, 39（9）: 83-88. WANG F F, LIU P, SUN F W, et al. A fruit classification system based on support vector machine and pressure sensor[J]. Food & Machinery, 2023, 39（9）: 83-88.
[18] LIU Y, DING H, HUANG Z, et al. Distributed and robust support vector machine[J]. International Journal of Computational Geometry and Applications, 2021, 30（3）: 213-233.
[19] 乔淼, 张磊, 母芳林. 基于电子鼻与LightGBM算法判别葡萄酒品种的研究[J]. 食品与机械, 2020, 36（5）: 76-79. QIAO M, ZHANG L, MU F L. Research on discriminating wine varieties based on electronic nose and LightGBM algorithm[J]. Food & Machinery, 2020, 36（5）: 76-79.
[20] 熊东阳, 张林, 李国庆. 基于最大熵模型的遥感土地利用多分类研究[J]. 自然资源遥感, 2023, 35（2）: 140-148. XIONG D Y, ZHANG L, LI G Q. A study of remote sensing land use multiclassification based on maximum entropy model[J]. Remote Sensing of Natural Resources, 2023, 35（2）: 140-148.
[21] ZHANG Y, GUO W. Moisture content detection of maize seed based on visible/near-infrared and near infrared hyperspectral imaging technology[J]. International Journal of Food Science and Amp; Technology, 2020, 55（2）: 631-640.
[22] 赵钜阳, 姚恒喆. 利用近红外光谱及电子鼻技术快速无损鉴别长期冻藏猪肉[J]. 食品与生物技术学报, 2021, 40（3）: 89-96. ZHAO J Y, YAO H C. Rapid and non-destructive identification of long-term frozen pork using near-infrared spectroscopy and electronic nose technology[J]. Journal of Food and Biotechnology, 2021, 40（3）: 89-96.