Jin Mao

A Structure Identification and Toxicity Assessment of the Degradation Products of Aflatoxin B1 in Peanut Oil under UV Irradiation

Aflatoxins, a group of extremely hazardous compounds because of their genotoxicity and carcinogenicity to human and animals, are commonly found in many tropical and subtropical regions. Ultraviolet (UV) irradiation is proven to be an effective method to reduce or detoxify aflatoxins. However, the degradation products of aflatoxins under UV irradiation and their safety or toxicity have not been clear in practical production such as edible oil industry. In this study, the degradation products of aflatoxin B1 (AFB1) in peanut oil were analyzed by Ultra Performance Liquid Chromatograph-Thermo Quadrupole Exactive Focus mass spectrometry/mass spectrometry (UPLC-TQEF-MS/MS). The high-resolution mass spectra reflected that two main products were formed after the modification of a double bond in the terminal furan ring and the fracture of the lactone ring, while the small molecules especially nitrogen-containing compound may have participated in the photochemical reaction. According to the above results, the possible photodegradation pathway of AFB1 in peanut oil is proposed. Moreover, the human embryo hepatocytes viability assay indicated that the cell toxicity of degradation products after UV irradiation was much lower than that of AFB1, which could be attributed to the breakage of toxicological sites. These findings can provide new information for metabolic pathways and the hazard assessment of AFB1 using UV detoxification.

An enhanced Monte Carlo outlier detection method.

Outlier detection is crucial in building a highly predictive model. In this study, we proposed an enhanced Monte Carlo outlier detection method by establishing cross‐prediction models based on determinate normal samples and analyzing the distribution of prediction errors individually for dubious samples. One simulated and three real datasets were used to illustrate and validate the performance of our method, and the results indicated that this method outperformed Monte Carlo outlier detection in outlier diagnosis. After these outliers were removed, the value of validation by Kovats retention indices and the root mean square error of prediction decreased from 3.195 to 1.655, and the average cross‐validation prediction error decreased from 2.0341 to 1.2780. This method helps establish a good model by eliminating outliers.

Multispecies Adulteration Detection of Camellia Oil by Chemical Markers

Adulteration of edible oils has attracted attention from more researchers and consumers in recent years. Complex multispecies adulteration is a commonly used strategy to mask the traditional adulteration detection methods. Most of the researchers were only concerned about single targeted adulterants, however, it was difficult to identify complex multispecies adulteration or untargeted adulterants. To detect adulteration of edible oil, identification of characteristic markers of adulterants was proposed to be an effective method, which could provide a solution for multispecies adulteration detection. In this study, a simple method of multispecies adulteration detection for camellia oil (adulterated with soybean oil, peanut oil, rapeseed oil) was developed by quantifying chemical markers including four isoflavones, trans-resveratrol and sinapic acid, which used liquid chromatography tandem mass spectrometry (LC-MS/MS) combined with solid phase extraction (SPE). In commercial camellia oil, only two of them were detected of daidzin with the average content of 0.06 ng/g while other markers were absent. The developed method was highly sensitive as the limits of detection (LODs) ranged from 0.02 ng/mL to 0.16 ng/mL and the mean recoveries ranged from 79.7% to 113.5%, indicating that this method was reliable to detect potential characteristic markers in edible oils. Six target compounds for pure camellia oils, soybean oils, peanut oils and rapeseed oils had been analyzed to get the results. The validation results indicated that this simple and rapid method was successfully employed to determine multispecies adulteration of camellia oil adulterated with soybean, peanut and rapeseed oils.

Palladium Nanoparticles-Based Fluorescence Resonance Energy Transfer Aptasensor for Highly Sensitive Detection of Aflatoxin M1 in Milk

A highly sensitive aptasensor for aflatoxin M₁ (AFM₁) detection was constructed based on fluorescence resonance energy transfer (FRET) between 5-carboxyfluorescein (FAM) and palladium nanoparticles (PdNPs). PdNPs (33 nm) were synthesized through a seed-mediated growth method and exhibited broad and strong absorption in the whole ultraviolet-visible (UV-Vis) range. The strong coordination interaction between nitrogen functional groups of the AFM₁ aptamer and PdNPs brought FAM and PdNPs in close proximity, which resulted in the fluorescence quenching of FAM to a maximum extent of 95%. The non-specific fluorescence quenching caused by PdNPs towards fluorescein was negligible. After the introduction of AFM₁ into the FAM-AFM₁ aptamer-PdNPs FRET system, the AFM₁ aptamer preferentially combined with AFM₁ accompanied by conformational change, which greatly weakened the coordination interaction between the AFM₁ aptamer and PdNPs. Thus, fluorescence recovery of FAM was observed and a linear relationship between the fluorescence recovery and the concentration of AFM₁ was obtained in the range of 5-150 pg/mL in aqueous buffer with the detection limit of 1.5 pg/mL. AFM₁ detection was also realized in milk samples with a linear detection range from 6 pg/mL to 150 pg/mL. The highly sensitive FRET aptasensor with simple configuration shows promising prospect in detecting a variety of food contaminants.

Identification of Nutritional Components in Black Sesame Determined by Widely Targeted Metabolomics and Traditional Chinese Medicines

Chemical composition of secondary metabolites is of great importance for quality control of agricultural products. Black sesame seeds are significantly more expensive than white sesame seeds, because it is thought that black sesame seeds are more beneficial to human health than white sesame seeds. However, the differences in nutrient composition between black sesame seeds and white sesame seeds are still unknown. The current study examined the levels of different metabolites in black and white sesame seeds via the use of a novel metabolomics strategy. Using widely targeted metabolomics data, we obtained the structure and content of 557 metabolites, out of which 217 metabolites were identified, and discovered 30 metabolic pathways activated by the secondary metabolites in both black and white sesame seeds. Our results demonstrated that the main pathways that were differentially activated included: phenylpropanoid biosynthesis, tyrosine metabolism, and riboflavin metabolism. More importantly, the biomarkers that were significantly different between black seeds and white sesame seeds are highly related to the functions recorded in traditional Chinese medicine. The results of this study may serve as a new theoretical reference for breeding experts to promote the genetic improvement of sesame seeds, and therefore the cultivation of higher quality sesame varieties.