Molecular Characterization of Aspergillus flavus Strains Isolated from Animal Feeds

Aflatoxin (AF)-producing fungi such as Aspergillus flavus commonly contaminate animal feeds, causing high economic losses. A. flavus is the most prevalent and produces AFB1, a potent mutagen, and carcinogen threatening human and animal health. Aspergillaceae is a large group of closely related fungi sharing number of morphological and genetic similarities that complicate the diagnosis of highly pathogenic strains. We used here morphological and molecular assays to characterize fungal isolates from animal feeds in Southwestern Algeria. These tools helped to identify 20 out of 30 Aspergillus strains, and 15 of them belonged to the Aspergillus section Flavi. Further analyses detected four out of 15 as belonging to Aspergillus flavus-parasiticus group. PCR targeting the AF genes' aflR-aflS(J) intergenic region amplified a single 674 bp amplicon in all four isolates. The amplicons were digested with a BglII endonuclease, and three specific fragments were observed for A. flavus but A. parasitucus lacked two typical fragments. Sequencing data of four amplicons confirmed the presence of the two BglII restriction sites yielding the three fragments, confirming that all four strains were A. flavus. In addition, this analysis illustrated the genetic variability within the A. flavus strains.

Early identification of Aspergillus spp. contamination in milled rice by E-nose combined with chemometrics

BACKGROUND

Rice grains can be contaminated easily by certain fungi during storage and in the market chain, thus generating a risk for humans. Most classical methods for identifying and rectifying this problem are complex and time-consuming for manufacturers and consumers. However, E-nose technology provides analytical information in a non-destructive and environmentally friendly manner. Two-feature fusion data combined with chemometrics were employed for the determination of Aspergillus spp. contamination in milled rice.

RESULTS

Linear discriminant analysis (LDA) indicated that the efficiency of fusion signals ('80^th s values' and 'area values') outperformed that of independent E-nose signals. Linear discriminant analysis showed clear discrimination of fungal species in stored milled rice for four groups on day 2, and the discrimination accuracy reached 92.86% by using an extreme learning machine (ELM). Gas chromatography-mass spectrometry (GC-MS) analysis showed that the volatile compounds had close relationships with fungal species in rice. The quantification results of colony counts in milled rice showed that the monitoring models based on ELM and the genetic algorithm optimized support vector machine (GA-SVM) (R²  = 0.924-0.983) achieved better performances than those based on partial least squares regression (PLSR) (R²  = 0.877-0.913). The ability of the E-nose to monitor fungal infection at an early stage would help to prevent contaminated rice grains from entering the food chains.

CONCLUSIONS

The results indicated that an E-nose coupled with ELM or GA-SVM algorithm could be a useful tool for the rapid detection of fungal infection in milled rice, to prevent contaminated rice from entering the food chain. © 2021 Society of Chemical Industry.

Detection of Aflatoxin and Ochratoxin A in Spices by High-Performance Liquid Chromatography

Background. Spices are one of the flavoring components of food in the cooking recipes of different nations that are used daily. However, these ingredients may be contaminated by toxicogenic fungi and subsequent production of mycotoxins that cannot be neutralized through cooking. In the present study, the possible contamination of spices by aflatoxins (AFs) and ochratoxin A (OTA) was investigated from Shiraz, the south part of Iran. Materials and Methods. A total of 80 spice samples including turmeric (n = 20), red pepper (n = 20), black pepper (n = 20), and cinnamon (n = 20) were purchased from markets and cultured on appropriate medium. The isolated fungi were identified. Simultaneously, mycotoxins from spices were extracted with immunoaffinity columns (IAC) and the occurrence of AFs (B1 + B2 + G1 + G2) and OTA was then determined using high-performance liquid chromatography (HPLC) with a fluorescence detector (FD). Result. The results depicted that 40 spice samples were contaminated with AFs and 48 samples with OTA. The highest rate of AFs contamination was related to red pepper, in 80% of which the amount of contamination was excessive than the standard level (>10 μg/kg). All black pepper samples were determined to be contaminated with OTA at over legislation limits of >15 μg/kg. Aspergillus species were the predominant isolated fungi, followed by Penicillium, and Mucor species. Discussion. Regarding the high mycotoxins contamination in spices in the current study, regular effective surveillance and quality control procedures are highly recommended. To achieve this goal, it is necessary to empower food-related laboratories with precise methods of isolation and detection of mycotoxins.

Early discrimination and growth tracking of Aspergillus spp. contamination in rice kernels using electronic nose

Early detection of Aspergillus spp. contamination in rice was investigated by electronic nose (E-nose) in this study. Sterilized rice artificially inoculated with three Aspergillus strains were subjected to GC-MS and E-nose analyses. Principle Component Analysis (PCA), Partial Least Squares Regression (PLSR), Back-propagation neural network (BPNN), Support Vector Machine (SVM) and Learning Vector Quantization (LVQ) were employed for qualitative classification and quantitative regression. GC-MS analysis revealed a significant correlation between the volatile compounds and total amounts/species of fungi. While X-axis barycenters of PC1 scores were significantly correlated with fungal counts, logistic model could be employed to simulate the growth of individual fungus (R² = 0.978-0.996). Fungal species and counts in rice could be classified and predicted by BPNN (96.4%) and PLSR (R² = 0.886-0.917), respectively. The results demonstrated that E-nose combined with BPNN might offer the feasibility for early detection of Aspergillus spp. contamination in rice.