拉曼光谱结合光谱特征区间筛选算法快速定量鉴别植物调和油品质

摘要/Abstract

摘要： 提出了一种基于拉曼光谱与光谱特征区间筛选算法实现植物调和油中高价值植物油含量快速定量检测的方法。首先，将粒子群优化（particle swarm optimization, PSO）算法与灰狼优化（grey wolf optimization, GWO）算法融合构建混合智能优化算法，即PSOGWO算法。其次，将PSOGWO与组合移动窗口（combined moving window, CMW）策略结合构建新型的拉曼光谱特征区间筛选算法，即PSOGWO-CMW算法。然后，将玉米油（corn oil, CO）和特级初榨橄榄油（extra virgin olive oil, EVOO）配比作为CO-EVOO植物调和油，并采集其拉曼光谱。将拉曼光谱输入偏最小二乘回归（partial least squares regression, PLSR）、PSO-CMW、GWO-CMW和PSOGWO-CMW模型预测EVOO含量，并比较建模效果。结果表明，PSOGWO-CMW模型具有最佳的预测性能。采用本方法与气相色谱-质谱法分别检测真实的CO-EVOO植物调和油样本中EVOO含量，结果表明，两者的检测性能无显著性差异。本方法快速、准确，亦可用于其他植物调和油中高价值植物油含量的快速定量检测。

关键词: 拉曼光谱, 植物调和油, 智能优化算法, 光谱特征区间筛选, 定量鉴别

Abstract: In this study, a method for rapid and quantitative determination of the content of high-value vegetable oil in vegetable blend oils was proposed based on Raman spectroscopy and a spectral characteristic intervals selection algorithm. First, the particle swarm optimization (PSO) algorithm and the grey wolf optimization (GWO) algorithm were combined to develop a hybrid intelligent optimization algorithm, i.e., PSOGWO algorithm. Second, the PSOGWO algorithm and the combined moving window (CMW) strategy were combined to develop a novel spectral characteristic intervals selection algorithm, i.e., PSOGWO-CMW algorithm. Third, the corn oil (CO)-extra virgin olive oil (EVOO) vegetable blend oil was prepared by mixing CO and EVOO with different ratios, and then the Raman spectra of CO-EVOO vegetable blend oil were measured. To investigate the performance of PSOGWO-CMW, the PLSR, PSO-CMW, GWO-CMW, and PSOGWO-CMW models were applied on the Raman spectra of CO-EVOO vegetable blend oil to predict the content of EVOO, and their prediction results were comparatively studied. The results showed that PSOGWO-CMW model possessed superior prediction performance. Finally, the proposed method and the gas chromatography-mass spectrometry method were employed to determine the content of EVOO in real CO-EVOO vegetable blend oils, respectively. The results showed that there was no significant difference between these two methods. In conclusion, the proposed method is rapid and accurate, and it can also be used for rapid and quantitative determination of the content of high-value vegetable oil in other vegetable blend oils.

Key words: Raman spectroscopy, vegetable blend oils, intelligent optimization algorithms, spectral characteristic intervals selection, quantitative authentication

中图分类号:

TS225.1

吴升德姜鑫郭志明朱家骥. 拉曼光谱结合光谱特征区间筛选算法快速定量鉴别植物调和油品质[J]. 食品科学.

Zhi-Ming GUO. Rapid quantitative authentication of vegetable blend oil quality by Raman spectroscopy coupled with spectral characteristic intervals selection algorithm[J]. FOOD SCIENCE.

参考文献

[1]WU Xijun, GAO Shibo, NIU Yudong, et al.Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network[J].Food Chemistry, 2022, 385:132655- [2]SUN Lei, TIAN Wen, FANG Yong, et al.Rapid and simultaneous extraction of phthalates, polychlorinated biphenyls and polycyclic aromatic hydrocarbons from edible oil for GC-MS determination[J].Journal of Food Composition and Analysis, 2022, 114:104827- [3]LEBANOV L, TEDONE L, GHIASVAND A, et al.Characterisation of complex perfume and essential oil blends using multivariate curve resolution-alternating least squares algorithms on average mass spectrum from GC-MS[J].Talanta, 2020, 219:121208- [4]SU Mingming, MI Weihui, ZHANG Yu, et al.Determination of illegal additive-ethyl maltol in edible oil by LC-MS/MS in China[J].Journal of Food Composition and Analysis, 2022, 114:104822- [5]MA Fei, YANG Qingqing, MATTHAUS B, et al.Simultaneous determination of capsaicin and dihydrocapsaicin for vegetable oil adulteration by immunoaffinity chromatography cleanup coupled with LC-MS/MS[J].Journal of Chromatography B, 2016, 1021:137-144 [6]LEE W-J, SU N-W, LEE M-H, et al.Assessment of authenticity of sesame oil by modified Villavecchia Test and HPLC-ELSD analysis of triacylglycerol profile[J].Food Research International, 2013, 53(1):195-202 [7]LI X-Q, JI Chao, SUN Y-Y, et al.Analysis of synthetic antioxidants and preservatives in edible vegetable oil by HPLCTOF-MS[J].Food Chemistry, 2009, 113(2):692-700 [8]ALONSO-SALCES R M, BERRUETA L A, QUINTANILLA-CASAS B, et al.Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures[J].Food Chemistry, 2022, 366:130588- [9]SIUDEM P, ZIELINSKA A, PARADOWSKA K.Application of 1H NMR in the study of fatty acids composition of vegetable oils[J].Journal of Pharmaceutical and Biomedical Analysis, 2022, 212:114658- [10]XU Yi, ZHONG Peng, JIANG Aimin, et al.Raman spectroscopy coupled with chemometrics for food authentication: A review[J].TrAC Trends in Analytical Chemistry, 2020, 131:116017- [11]WOLD S, SJOSTROM M, ERIKSSON L.PLS-regression: a basic tool of chemometrics[J].Chemometrics and Intelligent Laboratory Systems, 2001, 58(2):109-130 [12]DUMANCAS G G, RAMASAHAYAM S, BELLO G, et al.Chemometric regression techniques as emerging, powerful tools in genetic association studies[J].TrAC Trends in Analytical Chemistry, 2015, 74:79-88 [13]YUN Y-H, WANG W-T, DENG B-C, et al.Using variable combination population analysis for variable selection in multivariate calibration[J].Analytica Chimica Acta, 2015, 862:14-23 [14]DENG B-C, YUN Y-H, CAO D-S, et al.A bootstrapping soft shrinkage approach for variable selection in chemical modeling[J].Analytica Chimica Acta, 2016, 908:63-74 [15]LIN Y-W, XIAO N, WANG L-L, et al.Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data[J].Chemometrics and Intelligent Laboratory Systems, 2017, 168:62-71 [16]POLI R, KENNEDY J, BLACKWELL T.Particle swarm optimization[J].Swarm Intelligence, 2007, 1:33-57 [17]XUE Long, CAI Jun, LI Jing, et al.Application of Particle Swarm Optimization (PSO) Algorithm to Determine Dichlorvos Residue on the Surface of Navel Orange with Vis-NIR Spectroscopy[J].Procedia Engineering, 2012, 29:4124-4128 [18]ZHAO Jie, TIAN Geng, QIU Yanyan, et al.Rapid quantification of active pharmaceutical ingredient for sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on machine learning techniques[J].Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 245:118878- [19]MIRJALILI S, MIRJALILI S M, LEWIS A.Grey Wolf Optimizer[J].Advances in Engineering Software, 2014, 69:46-61 [20]ZHANG Pengfei, XU Zhoupin, WANG Qi, et al.A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling[J].Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 246:118986- [21]YUN Y-H, LI H-D, WOOD L R E, et al.An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration[J].Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2013, 111:31-36 [22]ZHANG Wei, MA Di, WEI Jin-jun, et al.A parameter selection strategy for particle swarm optimization based on particle positions[J].Expert Systems with Applications, 2014, 41(7):3576-3584 [23] 黄巧彬.基于非线性集成方法和误差修正的集装箱吞吐量预测研究[D].兰州: 兰州交通大学, 2020: 20-21. [24]MIAO Zhaoming, YUAN Xianfeng, ZHOU Fengyu, et al.Grey wolf optimizer with an enhanced hierarchy and its application to the wireless sensor network coverage optimization problem[J].Applied Soft Computing, 2020, 96:106602- [25]徐木子，林伟.基于改进-算法的无人艇路径跟踪控制系统研究[J].武汉理工大学学报, 2020, 42(09):38-49 [26]SUN Shaolong, JIN Feng, LI Hongtao, et al.A new hybrid optimization ensemble learning approach for carbon price forecasting[J].Applied Mathematical Modelling, 2021, 97:182-205 [27]BARROS I H A S, PAIXAO L S, NASCIMENTO M H C, et al.[J]. , 2021, 116: . DOI: .[J].Vibrational Spectroscopy, 2021, 116:103299- [28]FANG Peipei, WANG Hongpeng, WAN Xiong.[J]. , 2022, 397: 133763. DOI: .[J].Food Chemistry, 2022, 397:133763- [29]蒋万枫，张宁，张凤艳，等.顶空气相色谱-质谱测定橄榄调和油中橄榄油含量[J].色谱, 2017, 35(7):760-765 [30]YI Yinhui, ZHU Gangbing, LIU Chang, et al.A Label-Free Silicon Quantum Dots-Based Photoluminescence Sensor for Ultrasensitive Detection of Pesticide[J].Analytical Chemistry, 2013, 85(23):11464-11470