[1] Zheng J G, Chen Y F, Zhong H N et al. Advances in analysis techniques for mineral oil hydrocarbons in foods and food contact materials[J]. Journal of Instrumental Analysis, 38, 1393-1399(2019).

[2] Zhong H N, Zhu L, Lu Q et al. Toxicology and risk management of the mineral oil hydrocarbons of food contact material[J]. Chinese Journal of Food Hygiene, 31, 284-290(2019).

[3] Zhang Z Y, Wang G, Wang X X et al. Using SPME-GC/MS to identify the lubricating oil from combustion residues likely found at the arson spot[J]. Forensic Science and Technology, 41, 395-397(2016).

[4] Zhang Y G, Xiao Y P, Zhao Y L et al. Experimental research on high temperature oxidation of aviation engine oil by GC/MS[J]. Lubrication Engineering, 39, 77-82(2014).

[5] Eschner M S, Gröger T M, Horvath T et al. Quasi-simultaneous acquisition of hard electron ionization and soft single-photon ionization mass spectra during GC/MS analysis by rapid switching between both ionization methods: analytical concept, setup, and application on diesel fuel[J]. Analytical Chemistry, 83, 3865-3872(2011).

[6] Yang Q L, Deng X J, Sun X D et al. Application and research progress of spectral data fusion technology in food testing[J]. Science and Technology of Food Industry, 41, 324-329(2020).

[7] Sun F, Chen Y, Wang K Y et al. Identification method of ginger-processed Pinelliaternata based on infrared spectroscopy data fusion[J]. Journal of Beijing University of Traditional Chinese Medicine, 42, 862-868(2019).

[8] Yao S, Li T, Liu H G et al. Identification of geographical origin of Boletus tomentipes by multi-spectral data fusion[J]. Food Science, 39, 212-217(2018).

[9] Hou W, Wang J F. Rapid identification of the black marker ink based on infrared fingerprint spectroscopy[J]. Laser Technology, 44, 436-440(2020).

[10] He X L, Wang J F, Wang F et al. Rapid identification of rubber particles based on second derivative infrared spectra[J]. China Measurement & Test, 45, 60-64,83(2019).

[11] He X L, Wang J F, Zhang Q et al. Infrared spectroscopy analysis of marker ink based on multi-classification model[J]. Chemistry, 82, 169-174(2019).

[12] Shen X C, Xu L, Ye S B et al. Automatic baseline correction method for the open-path Fourier transform infrared spectra by using simple iterative averaging[J]. Optics Express, 26, A609-A614(2018). http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-26-10-A609

[13] He X L, Wang J F, Li Q S et al. Identification of vehicle bumper debris based on multi-layer perception-Fisher discriminant and infrared spectroscopy[J]. China Measurement & Test, 45, 74-78,92(2019).

[14] He X L, Wang J F, Wu F L et al. Identification of the infrared spectra of tire rubber based on chemometrics[J]. Journal of Analytical Science, 35, 357-361(2019).

[15] Johnson J, Mani J, Ashwath N et al. Potential for Fourier transform infrared (FTIR) spectroscopy toward predicting antioxidant and phenolic contents in powdered plant matrices[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 233, 118228(2020). http://www.sciencedirect.com/science/article/pii/S1386142520302067

[16] Shimbo G, Tagawa M, Matsumoto K et al. Effects of scatter correction processing on image quality of portable thoracic radiography in calves[J]. The Japanese Journal of Veterinary Research, 66, 105-112(2018). http://ci.nii.ac.jp/naid/120006469033

[17] Vaishali A. Ramakrishnan R, et al. Weather prediction model using Savitzky-Golay and Kalman filters[J]. Procedia Computer Science, 165, 449-455(2019).

[18] Qin J Q, Kong X Y, Sun X R. Influence of different data normalizing methods on the performance of Sevcik fractal dimension algorithm[J]. Chinese Journal of Scientific Instrument, 37, 1485-1491(2016).

[19] Li D, Jin Y Y, Tong Y et al. Intelligent detection and defect classification of infusion bags based on support vector machine[J]. Laser & Optoelectronics Progress, 56, 131502(2019).

[20] Chen Y, Yan X, Zhang X et al. Surface-enhanced Raman spectroscopy quantitative analysis of polycyclic aromatic hydrocarbons based on support vector machine algorithm[J]. Chinese Journal of Lasers, 46, 0311005(2019).

[21] Wang X F, Zhang X Y, Xu X H. Comparison of multi-factor-considered blood glucose prediction models by near-infrared spectroscopy[J]. Laser & Optoelectronics Progress, 56, 041701(2019).