|
|
|
|
|
|
| Quantitative Analysis of Single Component Oils in Quinary Blend Oil by Near-Infrared Spectroscopy Combined With Chemometrics |
| HU Xiao-yun1, BIAN Xi-hui1, 2, 3*, XIANG Yang2, ZHANG Huan1, WEI Jun-fu1 |
1. State Key Laboratory of Separation Membranes and Membrane Processes, School of Environmental Science and Engineering, Tiangong University, Tianjin 300387, China
2. State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
3. Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin 644000, China
|
|
|
|
|
Abstract The rapid and accurate quantitative analysis of blend oil is of great importance for the quality control of blend oil. However, most previous studies on the quantitative analysis of blend oil have focused on binary, ternary and quaternary blends, and few studies have been conducted on more multi-component blend oil, which is difficult to meet the needs of blend oil detection. This study explores the feasibility of near infrared spectroscopy combined with chemometrics for the quantitative analysis of the singlecomponentoil in quinary blend oil. 51 quinary blend oil samples were formulated from corn oil, soybean oil, rice oil, sunflower oil and sesame oil, and their NIR spectra were measured in a transmittance mode in the range of 12 000～4 000 cm-1. Firstly, the sample set partitioning based on joint x-y distances (SPXY) algorithm was used to divide the sample into 38 calibration and 13 prediction set samples. Secondly, the modeling effect of five multivariate calibration methods, including principal component regression (PCR), partial least squares (PLS), support vector regression (SVR), artificial neural network (ANN), and extreme learning machine (ELM), were examined for the quantitative analysis of each component in quinary blend oil. Then six spectral preprocessing methods including Savitzky Golag smoothing(SG smoothing), standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (1st Der), second derivative (2nd Der), and continuous wavelet transform (CWT) were compared based on the best modeling method and the reasons for the effectiveness of the preprocessing methods were discussed. Finally, based on the optimal preprocessing method, the competitive adaptive reweighted sampling (CARS) and Monte Carlo uninformative variable elimination (MCUVE) algorithms were further used to screen the variables associated with the predicted components. The results showed that PLS was the optimal modeling method among the five modeling methods, with root mean square error of the prediction set (RMSEP) of 5.564 4, 5.559 2, 3.592 6, 7.421 8, and 4.193 0 for the five components of corn oil, soybean oil, rice oil, sunflower oil, and sesame oil, respectively. After preprocessing-variable selection and then PLS modeling, the RMSEP for the five components were 1.955 3, 0.562 4, 1.145 0, 1.619 0 and 1.067 1, respectively and the correlation coefficients of prediction set (Rp) were all higher than 0.98, indicating that with appropriate spectral preprocessing, variable selection and modeling methods, the accuracy of quantitative analysis of each component in quinary blend oil was greatly improved. This research provided a reference for rapid and non-destructive quantitative detection of multi-component blend oil.
|
|
Received: 2021-11-26
Accepted: 2022-04-27
|
|
|
|
Corresponding Authors:
BIAN Xi-hui
E-mail: bianxihui@163.com
|
|
[1] Meenu M, Cai Q X, Xu B J. Trends in Food Science & Technology, 2019, 91: 391.
[2] Li S Y, Ng T T, Yao Z P, et al. Food Chemistry, 2021, 334: 127601.
[3] Xu J, Liu X F, Wang Y T. Food Chemistry, 2016, 212: 72.
[4] Hanieh N M, Zahra T, Mahsa A L, et al. Journal of Food Composition and Analysis, 2022, 107: 104343.
[5] Du Q W, Zhu M T, Shi T, et al. Food Control, 2021, 121: 107577.
[6] Leng T, Li F, Xiong L A, et al. Food Control, 2020, 113: 107203.
[7] Soares F, Anzanello M J. Chemometrics and Intelligent Laboratory Systems, 2018, 172: 167.
[8] Aroca-Santos R, Cancilla J C, Pariente E S, et al. Talanta, 2016, 161: 304.
[9] Wang K Y, Bian X H, Zheng M, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 263: 120138.
[10] Basri K N, Hussain M N, Bakar J, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2017, 173: 335.
[11] Borghi F T, Santos P C, Santos F D, et al. Microchemical Journal, 2020, 159: 105544.
[12] Bian X H, Wang K Y, Tan E X, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 197: 103916.
[13] Li P, Du G R, Ma Y J, et al. Chemometrics and Intelligent Laboratory Systems, 2018, 176: 157.
[14] Chen W H, Chen H Z, Feng Q X, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 248: 119182.
[15] Tian W F, Chen G J, Zhang G R, et al. Food Chemistry, 2021, 344: 128633.
[16] Shao X G, Ma C X. Chemometrics and Intelligent Laboratory Systems, 2003, 69: 157.
|
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
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. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
