Prediction of benzo[a]pyrene content of smoked sausage using back-propagation artificial neural network

Exposure of benzo[a]pyrene induces HCC exosome-circular RNA to activate lung fibroblasts and trigger organotropic metastasis

BACKGROUND

Benzo[a]pyrene (B[a]P), a carcinogen pollutant produced by combustion processes, is present in the western diet with grilled meats. Chronic exposure of B[a]P in hepatocellular carcinoma (HCC) cells promotes metastasis rather than primary proliferation, implying an unknown mechanism of B[a]P-induced malignancy. Given that exosomes carry bioactive molecules to distant sites, we investigated whether and how exosomes mediate cancer-stroma communications for a toxicologically associated microenvironment.

METHOD

Exosomes were isolated from B[a]P stimulated BEL7404 HCC cells (7404-100Bap Exo) at an environmental relevant dose (100 nmol/L). Lung pre-education animal model was prepared via injection of exosomes and cytokines. The inflammatory genes of educated lungs were evaluated using quantitative reverse transcription PCR array. HCC LM3 cells transfected with firefly luciferase were next injected to monitor tumor burdens and organotropic metastasis. Profile of B[a]P-exposed exosomes were determined by ceRNA microarray. Interactions between circular RNA (circRNA) and microRNAs (miRNAs) were detected using RNA pull-down in target lung fibroblasts. Fluorescence in situ hybridization and RNA immunoprecipitation assay was used to evaluate the "on-off" interaction of circRNA-miRNA pairs. We further developed an adeno-associated virus inhalation model to examine mRNA expression specific in lung, thereby exploring the mRNA targets of B[a]P induced circRNA-miRNA cascade.

RESULTS

Lung fibroblasts exert activation phenotypes, including focal adhesion and motility were altered by 7404-100Bap Exo. In the exosome-educated in vivo model, fibrosis factors and pro-inflammatory molecules of are up-regulated when injected with exosomes. Compared to non-exposed 7404 cells, circ_0011496 was up-regulated following B[a]P treatment and was mainly packaged into 7404-100Bap Exo. Exosomal circ_0011496 were delivered and competitively bound to miR-486-5p in recipient fibroblasts. The down-regulation of miR-486-5p converted fibroblast to cancer-associated fibroblast via regulating the downstream of Twinfilin-1 (TWF1) and matrix metalloproteinase-9 (MMP9) cascade. Additionally, increased TWF1, specifically in exosomal circ_0011496 educated lungs, could promote cancer-stroma crosstalk via activating vascular endothelial growth factor (VEGF). These modulated fibroblasts promoted endothelial cells angiogenesis and recruited primary HCC cells invasion, as a consequence of a pre-metastatic niche formation.

CONCLUSION

We demonstrated that B[a]P-induced tumor exosomes can deliver circ_0011496 to activate miR-486-5p/TWF1/MMP9 cascade in the lung fibroblasts, generating a feedback loop that promoted HCC metastasis.

Quality prediction of whole-grain rice noodles using backpropagation artificial neural network

BACKGROUND

Whole-grain rice noodles are a kind of healthy food with rich nutritional value, and their product quality has a notable impact on consumer acceptability. The quality evaluation model is of great significance to the optimization of product quality. However, there are few methods that can establish a product quality prediction model with multiple preparation conditions as inputs and various quality evaluation indexes as outputs. In this study, an artificial neural network (ANN) model based on a backpropagation (BP) algorithm was used to predict the comprehensive quality changes of whole-grain rice noodles under different preparation conditions, which provided a new way to improve the quality of extrusion rice products.

RESULTS

The results showed that the BP-ANN using the Levenberg-Marquardt algorithm and the optimal topology (4-11-8) gave the best performance. The correlation coefficients (R2) for the training, validation, testing, and global data sets of the BP neural network were 0.927, 0.873, 0.817, and 0.903, respectively. In the validation test, the percentage error in the quality prediction of whole-grain rice noodles was within 10%, indicating that the BP-ANN could accurately predict the quality of whole-grain rice noodles prepared under different conditions.

CONCLUSION

This study showed that the quality prediction model of whole-grain rice noodles based on the BP-ANN algorithm was effective, and suitable for predicting the quality of whole-grain rice noodles prepared under different conditions. © 2024 Society of Chemical Industry.

Formation and prediction of heterocyclic amines and N-nitrosamines in smoked sausages using back propagation artificial neural network

BACKGROUND

Heterocyclic amines (HAs) and N-nitrosamines (NAs) are formed easily during the thermal processing of food, and epidemiological studies have demonstrated that consuming HAs and NAs increases the risk of cancer. However, there are few studies on the application of back propagation artificial neural network (BP-ANN) models to simultaneously predict the content of HAs and NAs in sausages. This study aimed to investigate the effects of cooking time and temperature, smoking time and temperature, and fat-to-lean ratio on the formation of HAs and NAs in smoked sausages, and to predict their total content based on the BP-ANN model.

RESULTS

With an increase in processing time, processing temperature and fat ratio, the content of HAs and NAs in smoked sausages increased significantly, while the content of HA precursors and nitrite residues decreased significantly. The optimal network topology of the BP-ANN model was 5-11-2, the correlation coefficient values for training, validation, testing and all datasets were 0.99228, 0.99785, 0.99520 and 0.99369, respectively, and the mean squared error value of the best validation performance was 0.11326. The bias factor and the accuracy factor were within acceptable limits, and the predicted values approximated the true values, indicating that the model has good predictive performance.

CONCLUSION

The contents of HAs and NAs in smoked sausages were significantly influenced by the cooking conditions, smoking conditions and fat ratio. The BP-ANN model has high application value in predicting the contents of HAs and NAs in sausages, which provides a theoretical basis for the suppression of carcinogen formation. © 2024 Society of Chemical Industry.

Inhibition of benzo[a]pyrene formation in charcoal-grilled pork sausages by ginger and its key compounds

BACKGROUND

Ginger and its extracts have been frequently used in food processing and pharmaceuticals. However, the influence of ginger and its key compounds on benzo[a]pyrene (BaP) production in meat processing has not been investigated. The purpose of this study was to explore the effect of application of ginger and its important active ingredients on BaP formation and the mechanism of inhibiting BaP formation in charcoal-grilled pork sausages.

RESULTS

The DPPH scavenging (23.59-59.67%) activity and the inhibition rate of BaP (42.1-68.9%) were significantly increased (P < 0.05) with increasing ginger addition. The active components extracted by supercritical carbon dioxide from ginger were identified by gas chromatography-mass spectrometry and 14 representative compounds (four terpenes, two alcohols, two aldehydes, four phenols and two other compounds, totaling 77.57% of the detected compounds) were selected. The phenolic compounds (eugenol, 6-gingerol, 6-paradol and 6-shogaol, accounting for 29.73% of the total composition) in ginger played a key role and had the strongest inhibitory effect on BaP (61.2-68.2%), whereas four other kinds of compound showed obviously feeble inhibitory activity (6.47-17.9%). Charcoal-grilled sausages with phenolic substances had lower values of thiobarbituric acid-reactive substances, carbonyl and diene (three classic indicators of lipid oxidation) (P < 0.05).

CONCLUSION

Ginger and its key compounds could effectively inhibit the formation of BaP in charcoal-grilled pork sausages. Phenolic compounds make the strongest contribution to the inhibition of Bap formation, and the inhibitory mechanism was related to the inhibition of lipid oxidation. © 2023 Society of Chemical Industry.

A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network

Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.

Application of Artificial Intelligence in Food Industry—a Guideline

Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.