Estimating bulk optical properties of AFB1 contaminated edible oils in 300–900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy. (1st May 2022)

• Record Type: Journal Article
• Authors: He, Xueming; Zhang, Yue; Yang, Xiaoyun; Chen, Min; Pang, Yanyan; Shen, Fei; Fang, Yong; Liu, Qin; Hu, Qiuhui
• Is Part Of: Food chemistry. Volume 375(2022)
• Publication Date: 2022-05-01
• Subjects: AFB1 Aflatoxin B1 -- TLC Thin-layer chromatography -- ELISA Enzyme linked immunosorbent assay -- HPLC High performance liquid chromatography -- GC Gas chromatography -- LC-MS Liquid chromatography-mass spectrometry -- NIR near infrared spectroscopy -- HIS Hyperspectral imaging -- SERS Surface enhanced Raman spectroscopy -- THz Terahertz -- LIF Laser-induced fluorescence spectroscopy -- μa Absorption coefficient -- μ's Reduced scattering coefficient -- DIS Double integrating sphere -- 'FLM' P 'Fulinmen' peanut oil -- 'LH' P 'Luhua' peanut oil -- 'DL' R 'Duoli' rapeseed oil -- 'LH' R 'Luhua' rapeseed oil -- 'FLM' C 'Fulinmen' corn oil -- 'JLY' C 'Jinlongyu' corn oil -- PCA Principal component analysis -- PCs principal components -- LDA Linear discriminant analysis -- CCR correct classification rate -- FPR False positive rate -- FNR False negative rate -- r2 Determination coefficients; AFB1 -- Edible oil -- Optical properties -- Double integrating spheres -- Laser induced fluorescence spectroscopy; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.131666
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Developing an optical detection device to obtain μa, μ's and fluorescence signal; Revealing the interference law of μa and μ's on fluorescence intensity; Comparing the spectra difference for different AFB1 contaminated oils; Building robust model for AFB1 discrimination based on μa and fluorescence signal. Abstract: An optical detection platform based on laser induced spectroscopy and double integrating spheres techniques was developed to obtain absorption ( μa ), reduced scattering coefficients ( μ's ) and fluorescence intensity of oil. The validation experiment carried on liquid phantoms showed that the developed system could achieve high linearity, and the results of spectra analysis indicated that the fluorescence intensity has a significant negative correlation with both μa and μ's . A total of 1620 oils with six categories were detected. The reason for the difference of fluorescence and μa spectra was analyzed by comparing the measured chlorophyll, polyphenol and α-tocopherol contents. Linear discriminant analysis combined with principal component analysis based on fluorescence and μa spectra was employed, to calibrate the AFB1 classification models. The discrimination results manifested that by integrating μa with fluorescence signal, the correct classification rate could be improved by more than 10%, and the false negative rate was greatly reduced.