Laser induced fluorescence spectroscopy for detection of Aflatoxin B1 contamination in peanut oil

A novel method for determination of peroxide value and acid value of extra-virgin olive oil based on fluorescence internal filtering effect correction

Peroxide value (PV) and acid value (AV) are widely used indicators for evaluating oxidation degree of olive oils. Fluorescence spectroscopy has been extensively studied on the detection of oil oxidation, however, the detection accuracy is limited due to internal filtering effect (IFE). Due to the primary and secondary IFE, at least two wavelengths of absorption information are required. Least squares support vector regression (LSSVR) models for PV and AV were established based on two absorption coefficients (μa) at 375 nm and emission wavelength and one fluorescence intensity at corresponding wavelength. The regression results proved that the model based on 375 and 475 nm could reach the best performance, with the highest correlation coefficient for prediction (rp) of 0.889 and 0.960 for PV and AV respectively. Finally, the explicit formulations for PV and AV were determined by nonlinear least squares fitting, and the rp could reach above 0.94 for two indicators.

Quantitative prediction of AFB1 in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths

This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B1 (AFB1) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (μa) and reduced scattering (μ's) coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB1 contents in six different kinds of edible oils. A research process of "Monte Carlo (MC) simulation - phantom verification - actual sample validation" was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the μa, μ's at emission wavelength (μa,em, μ's,em) and μa at excitation wavelength (μa,ex), however increased with the μ's at excitation wavelength (μ's,ex). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (μeff,ex, μeff,em) and μ's,ex. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB1 contamination. The LSSVR model for AFB1 based on μeff,ex, μeff,em, μ's,ex and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (Rc) and the validation (Rv) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB1 content in different types of vegetable oils.

Aflatoxin detection technologies: recent advances and future prospects

Aflatoxins have posed serious threat to food safety and human health. Therefore, it is important to detect aflatoxins in samples rapidly and accurately. In this review, various technologies to detect aflatoxins in food are discussed, including conventional ones such as thin-layer chromatography (TLC), high performance liquid chromatography (HPLC), enzyme linked immunosorbent assay (ELISA), colloidal gold immunochromatographic assay (GICA), radioimmunoassay (RIA), fluorescence spectroscopy (FS), as well as emerging ones (e.g., biosensors, molecular imprinting technology, surface plasmon resonance). Critical challenges of these technologies include high cost, complex processing procedures and long processing time, low stability, low repeatability, low accuracy, poor portability, and so on. Critical discussion is provided on the trade-off relationship between detection speed and detection accuracy, as well as the application scenario and sustainability of different technologies. Especially, the prospect of combining different technologies is discussed. Future research is necessary to develop more convenient, more accurate, faster, and cost-effective technologies to detect aflatoxins.

Recent Progress on Techniques in the Detection of Aflatoxin B1 in Edible Oil: A Mini Review

Contamination of agricultural products and foods by aflatoxin B1 (AFB1) is becoming a serious global problem, and the presence of AFB1 in edible oil is frequent and has become inevitable, especially in underdeveloped countries and regions. As AFB1 results from a possible degradation of aflatoxins and the interaction of the resulting toxic compound with food components, it could cause chronic disease or severe cancers, increasing morbidity and mortality. Therefore, rapid and reliable detection methods are essential for checking AFB1 occurrence in foodstuffs to ensure food safety. Recently, new biosensor technologies have become a research hotspot due to their characteristics of speed and accuracy. This review describes various technologies such as chromatographic and spectroscopic techniques, ELISA techniques, and biosensing techniques, along with their advantages and weaknesses, for AFB1 control in edible oil and provides new insight into AFB1 detection for future work. Although compared with other technologies, biosensor technology involves the cross integration of multiple technologies, such as spectral technology and new nano materials, and has great potential, some challenges regarding their stability, cost, etc., need further studies.

High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis

This study proposes a label-free rapid detection method for aflatoxin B₁ (AFB₁) in pressing peanut oil based on Raman spectroscopy technology combined with appropriate chemometric methods. A DXR laser Raman spectrometer was used to acquire the Raman spectra of the pressed peanut oil samples, and the obtained spectra were preprocessed by wavelet transform (WT) combined with adaptive iteratively reweighted penalized least squares (airPLS). The competitive adaptive reweighted sampling (CARS) method was used to optimize the characteristic bands of the Raman spectra pretreated by the WT + airPLS, and a partial least squares (PLS) detection model for the AFB₁ content was established based on the features optimized. The results obtained showed that the root mean square error of prediction (RMSEP) and determination coefficient of prediction (RP2) of the optimal CARS-PLS model in the prediction set were 22.6 µg/kg and 0.99, respectively. The results demonstrate that the Raman spectroscopy combined with appropriate chemometrics can be used to quickly detect the safety of edible oil with high precision. The overall results can provide a technical basis and method reference for the design and development of the portable Raman spectroscopy system for the quality and safety detection of edible oil storage, and also provide a green tool for fast on-site analysis for regulatory authorities of edible oil and production enterprises of edible oil.

Estimating bulk optical properties of AFB1 contaminated edible oils in 300-900 nm by combining double integrating spheres technique with laser induced fluorescence spectroscopy

An optical detection platform based on laser induced spectroscopy and double integrating spheres techniques was developed to obtain absorption (μ_a), 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 μ_a and μ'_s. A total of 1620 oils with six categories were detected. The reason for the difference of fluorescence and μ_a 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 μ_a spectra was employed, to calibrate the AFB₁ classification models. The discrimination results manifested that by integrating μ_a with fluorescence signal, the correct classification rate could be improved by more than 10%, and the false negative rate was greatly reduced.