|
|
|
|
|
|
Study on Detection System of Grape Seed Oil Adulteration Based on Visible/Near Infrared Spectroscopy |
TANG Yun-feng1, 2, CHAI Qin-qin1, 2*, LIN Shuang-jie1, 2, HUANG Jie1, 2, LI Yu-rong1, 2, WANG Wu1, 2 |
1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2. Ministry of Education Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou University, Fuzhou 350108, China |
|
|
Abstract Various kinds of adulterated grape seed oil and concealed adulterated means cause a severe problem in food safety detection. In order to regulate the edible oil market, it is especially important to provide a convenient and reliable method for identifying the quality of grape seed oil. However, traditional methods for chromatography and mass spectrometry are time consuming, reagent intensive, highly specialized, etc.; and the near infrared spectrometer that realizes non-destructive analysis is expensive and has high operating environment requirements. Thus, a visible/near infrared spectrometer with low cost and high accuracy was designed to discriminate grape seed oil adulteration. Firstly, a visible/near infrared spectrometer hardware platform based on USB6500-Pro detector was built, and a simple human-computer interaction interface based on Qt was designed to realize the collection and processing of spectral data and the display of grape seed oil adulteration discrimination results. Secondly, for the spectral noise brought by hardware and detection environment, wavelet transform was used to filter out noise and reduce spectral distortion. Finally, considering that the existing quality discrimination models based on machine learning often rely on the known oil training sample set to predict the different adulterated categories; and driven by interest adulteration means will emerge in endlessly which will result in the emerging of new adulteration categories not in the original training set, the existing quality identification methods are difficult to give accurate results. Therefore, a discrimination method for known and new adulterated oil spectra was designed in the detection system. This method was realized by two steps: (1) classification: the extreme learning machine (ELM) classifier model was established by using the training set in the modeling database to realize the preliminary judgment of the preliminary adulteration category; (2) correction: the automatic clustering algorithm was then used to further correct the prediction result. If a clustering center is generated with the correction data set, it is proved that the prediction result is correct and belongs to the known adulteration category in the modeling database; if two cluster centers are generated, the prediction result is incorrect and the sample is a new adulteration category which does not appear in the modeling database. The result of the accurate adulterated category was eventually obtained. In order to test the performance of the system, five classes of oil, including pure grape seed oil, and grape seed oil blended with different proportions of soybean oil, corn oil, sunflower oil and blend oil were analyzed by the visible/near infrared hardware platform and their spectroscopy data were collected. It contains 30 sets of data for each class of oil, totals 150 sets. Before inputting the visible/near infrared spectroscopy data into the detection system, they were firstly de-noised by wavelet threshold method and pre-processed by multiple scattering correction. Assuming that the first four classes were known adulteration class in the modeling database and the fifth class was new adulteration class, samples from each of the four known adulteration classes were divided into 20 training sets and 10 test sets by using K-S algorithm. Then, ELM classification model was established by using 80 training sets, and 40 test sets were input into ELM for preliminary discrimination. The discrimination results were further analyzed and corrected by clustering. There was one clustering center, which meant that the ELM model discriminated accurately and could recognize 100% of the known classes. However, when 30 samples from the new adulterated class were put into the ELM model, all of them were discriminated as pure grape seed oil. The discrimination results were further clustered and corrected. There were two clustering centers, which showed that the model was misjudged and the fifth class was qualitatively determined as a new adulterated class. The experimental results showed that the designed visible/near infrared spectroscopy detection system was simple and fast, and can identify not only the known adulteration categories but also the new adulteration categories.
|
Received: 2018-11-08
Accepted: 2019-03-09
|
|
Corresponding Authors:
CHAI Qin-qin
E-mail: qq.chai@fzu.edu.cn
|
|
[1] Lutterodt H, Slavin M, Whent M, et al. Food Chemistry, 2011, 128(2): 391.
[2] SHUAI Qian, ZHANG Liang-xiao, LI Pei-wu, et al(帅 茜,张良晓,李培武,等). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(10): 1530.
[3] YE Hua-jun, XIA A-lin, ZHANG Xue-feng, et al(叶华俊,夏阿林,张学锋,等). Chinese Journal of Scientific Instrument(仪器仪表学报), 2012, 33(1): 85.
[4] Zhou Yang, Liu Tiebing, Li Jinrong, et al. Analytical Methods, 2015, 7(6): 2367.
[5] Wójcicki K, Khmelinskii I, Sikorski M, et al. Food Chemistry, 2015, 187: 416.
[6] Zhang Liguo, Zhang Xin, Ni Lijun, et al. Food Chemistry, 2014, 145: 342.
[7] SUN Tong, HU Tian,XU Wen-li, et al(孙 通,胡 田,许文丽,等). China Oils and Fats(中国油脂), 2013, 38(10): 75.
[8] WANG Wei, JIANG Hui, LIU Guo-hai, et al(王 玮, 江 辉, 刘国海, 等). Chinese Journal of Analytical Chemistry(分析化学), 2017, 45(8): 1137.
[9] GUO Wen-chuan, WANG Ming-hai, GU Jing-si, et al(郭文川,王铭海,谷静思,等). Optics & Precision Engineering(光学精密工程), 2013, 21(10): 2720.
[10] Alex Rodriguez, Alessandro Laio. Science, 2014, 344(6191): 1492.
[11] Huang Guangbin, Zhu Qinyu, SIEW Cheekheong. Neurocomputing, 2006, 70(1): 489.
[12] ZHANG Li-guo, HU Yong-tao, ZHANG Shu-qing, et al(张立国,胡永涛,张淑清,等). Chinese Journal of Scientific Instrument(仪器仪表学报), 2016, 37(9): 2061.
[13] LI Ying, LI Yao-xiang, LI Wen-bin, et al(李 颖,李耀翔,李文彬,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(5): 1384.
[14] FENG De-shan, YANG Bing-kun, WANG Xun, et al(冯德山,杨炳坤,王 珣,等). Chinese Journal of Geophysics(地球物理学报), 2016, 59(1): 342. |
[1] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[2] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[3] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[4] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[5] |
XU Rong1, AO Dong-mei2*, LI Man-tian1, 2, LIU Sai1, GUO Kun1, HU Ying2, YANG Chun-mei2, XU Chang-qing1. Study on Traditional Chinese Medicine of Lonicera L. Based on Infrared Spectroscopy and Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3518-3523. |
[6] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[7] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[8] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[9] |
LI Xin-li1, CONG Li-li2, XU Shu-ping2, LI Su-yi1*. Cell Growth Analysis Method Based on Spectral Clustering and Single-Cell Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2832-2836. |
[10] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
[11] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
[12] |
ZHANG Fu1, 2, 3, CAO Wei-hua1, CUI Xia-hua1, WANG Xin-yue1, FU San-ling4*, ZHANG Ya-kun1. Non-Destructive Detection of Soluble Solids in Cherry Tomatoes by
Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 737-743. |
[13] |
LIU Si-qi1, FENG Guo-hong1*, TANG Jie2, REN Jia-qi1. Research on Identification of Wood Species by Mid-Infrared Spectroscopy Based on CA-SDP-DenseNet[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 814-822. |
[14] |
LI Zi-yi1, LI Rui-lan1, LI Can-lin1, WANG Ke-ru2, FAN Jiu-yu3, GU Rui1*. Identification of Tibetan Medicine Zhaxun by Infrared Spectroscopy
Combined With Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 526-532. |
[15] |
YANG Cheng-en1, SU Ling2, FENG Wei-zhi1, ZHOU Jian-yu1, WU Hai-wei1*, YUAN Yue-ming1, WANG Qi2*. Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 577-582. |
|
|
|
|