Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems

Modeling Growth Kinetics of Escherichia coli and Background Microflora in Hydroponically Grown Lettuce

Hydroponic cultivation of lettuce is an increasingly popular sustainable agricultural technique. However, Escherichia coli, a prevalent bacterium, poses significant concerns for the quality and safety of hydroponically grown lettuce. This study aimed to develop a growth model for E. coli and background microflora in hydroponically grown lettuce. The experiment involved inoculating hydroponically grown lettuce with E. coli and incubated at 4, 10, 15, 25, 30, 36 °C. Growth models for E. coli and background microflora were then developed using Origin 2022 (9.9) and IPMP 2013 software and validated at 5 °C and 20 °C by calculating root mean square errors (RMSEs). The result showed that E. coli was unable to grow at 4 °C and the SGompertz model was determined as the most appropriate primary model. From this primary model, the Ratkowsky square root model and polynomial model were derived as secondary models for E. coli-R168 and background microflora, respectively. These secondary models determined that the minimum temperature (Tmin) required for the growth of E. coli and background microflora in hydroponically grown lettuce was 6.1 °C and 8.7 °C, respectively. Moreover, the RMSE values ranged from 0.11 to 0.24 CFU/g, indicating that the models and their associated kinetic parameters accurately represented the proliferation of E. coli and background microflora in hydroponically grown lettuce.

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

Inhibition of Several Bacterial Species Isolated from Squid and Shrimp Skewers by Different Natural Edible Compounds

Seafood is an excellent source of nutrients, essential for a healthy diet, ranging from proteins and fatty acids to vitamins and minerals. Seafood products are highly perishable foods due to their nutritional characteristics and composition. The application of nontoxic, natural, and edible preservatives to extend the shelf-life and inhibit bacterial proliferation of several foods has been a hot topic. Consequently, this work aimed to perform the microbiological characterization of squid and shrimp skewers during their shelf-life (five days) and evaluate the susceptibility of randomly isolated microorganisms to several natural edible compounds so that their application for the preservation and shelf-life extension of the product might be analyzed in the future. The product had considerably high total microorganisms loads of about 5 log CFU/g at day zero and 9 log CFU/g at day five. In addition, high bacterial counts of Pseudomonas spp., Enterobacterales, and lactic acid bacteria (LAB) were found, especially on the last day of storage, being Pseudomonas the dominant genus. However, no Escherichia coli or Listeria monocytogenes were detected on the analyzed samples. One hundred bacterial isolates were randomly selected and identified through 16s rRNA sequencing, resulting in the detection of several Enterobacterales, Pseudomonas spp., and LAB. The antibacterial activity of carvacrol, olive leaf extract, limonene, Citrox®, different chitosans, and ethanolic propolis extracts was evaluated by the agar diffusion method, and the minimum inhibitory concentration was determined only for Citrox® since only this solution could inhibit all the identified isolates. At concentrations higher than or equal to 1.69% (v/v), Citrox® demonstrated bacteriostatic and bactericidal activity to 97% and 3% of the isolates, respectively. To our knowledge, there are no available data about the effectiveness of this commercial product on seafood isolates. Although preliminary, this study showed evidence that Citrox® has the potential to be used as a natural preservative in these seafood products, improving food safety and quality while reducing waste. However, further studies are required, such as developing a Citrox®-based coating and its application on this matrix to validate its antimicrobial effect.

Growth of Salmonella Enteritidis in the presence of quorum sensing signaling compounds produced by Pseudomonas aeruginosa

Quorum sensing (QS) can exist in food-related bacteria and potentially affect bacterial growth through acyl-homoserine lactones (AHLs). To verify the role of QS compounds in the cell-free supernatant, this study examined the effect of supernatant extracted from Pseudomonas aeruginosa culture on the growth kinetics of Salmonella Enteritidis. The results showed that the lag time (λ) of S. Enteritidis was apparently reduced (p < 0.05) under the influence of P. aeruginosa culture supernatant compared with the S. Enteritidis culture supernatant. HPLC-MS/MS test demonstrated that AHLs secreted by P. aeruginosa were mainly C14-HSL with a content of 85.71 μg/mL and a small amount of 3-oxo-C12-HSL. In addition, the commercially synthetic C14-HSL had positive effects on the growth of S. Enteritidis, confirming once again that the growth of S. Enteritidis was affected by AHL metabolized by other bacteria and the complexity of bacterial communication.

Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach

Sustainable development goals are used as a guidance for strategies development on local, regional and national levels. The importance of including young people in this complex process is recognized in all relevant documents (i.e. Agenda 21), however it is not an easy task to elicit opinions and preferences from the youth. Furthermore, the assessment of the sustainable development goals itself presents a challenge for the noisy data and nonlinear relationships in data. Popular approach is fuzzy set models where expert knowledge is presented with comprehensible rules; however expert knowledge elicitation takes a long time too. Several studies proposed an adaptive neuro-fuzzy inference system approach that combines the fuzzy set theory to model expert knowledge with neural networks for inferring rules and membership functions from data to assess the sustainable development performance. We base our assumptions that ANFIS can be used to predict the importance of sustainable development pillars from the demographic data of young people. For this purpose, we have conducted an online survey on sustainable development goals opinions and importance of young people in Serbia. The sample of 386 respondents has been split into a training sample of 300 instances (to generate membership functions and fuzzy rules) and a testing sample of 86 instances to predict the importance of the three pillars. We have conducted a trace-driven simulation test to validate the results of the proposed ANFIS model. Results of the study provided insights into how the young people in Serbia assess the importance of sustainable development goals. Secondly, the results suggest that ANFIS can be applied to predict values of importance of the three sustainable development pillars with the relative error of Rel Err < 5%. It must be noted that the considered model could be further improved by using training samples with more data.

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement concrete (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates. The prepared mixtures contained fly ash, sodium hydroxide in solid state, sodium silicate solution, coarse and fine steel slag aggregates as well as water, in which four variables (fly ash, sodium hydroxide, sodium silicate solution, and water) were used as input parameters for modeling. A total number of 210 samples were prepared with target-specified compressive strength at standard age of 28 days of 25, 35, and 45 MPa. Such values were obtained and used as targets for the two AI prediction tools. Evaluation of the model's performance was achieved via criteria such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The results showed that both ANN and ANFIS models have strong potential for predicting the compressive strength of GPC but ANFIS (MAE = 1.655 MPa, RMSE = 2.265 MPa, and R² = 0.879) is better than ANN (MAE = 1.989 MPa, RMSE = 2.423 MPa, and R² = 0.851). Sensitivity analysis was then carried out, and it was found that reducing one input parameter could only make a small change to the prediction performance.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Modeling Growth Kinetic Parameters of Salmonella Enteritidis SE86 on Homemade Mayonnaise Under Isothermal and Nonisothermal Conditions

During the last decade, a specific strain of Salmonella Enteritidis (named SE86) has been identified as the major etiological agent responsible for salmonellosis in the State of Rio Grande do Sul, Southern Brazil, and the main food vehicle was homemade mayonnaise (HM). This study aimed to model the growth prediction of SE86 on HM under isothermal and nonisothermal conditions. SE86 was inoculated on HM and stored at 7, 10, 15, 20, 25, 30, and 37°C. Growth curves were built by fitting data to the Baranyi's DMFit, generating r(2) values greater than 0.98 for primary models. Secondary model was fitted with Ratkowsky equation, generating r(2) and root mean square error values of 0.99 and 0.016, respectively. Also, the growth of SE86 under nonisothermal conditions simulating abuse temperature during preparation, storage, and serving of HM was studied. Experimental data showed that SE86 did not grow on HM at 7°C for 30 days. At 10°C, no growth was observed until approximately 18 h, and the infective dose (assumed as 10(6) CFU/g) was reached after 8.1 days. However, the same numbers of SE86 were attained after 6 hours at 37°C. Experimental data demonstrated shorter lag times than those generated by ComBase Predictive Models, suggesting that SE86 is very well adapted for growing on HM. SE86 stored under nonisothermal conditions increased population to reach about 10(6) CFU/g after approximately 30 hours of storage. In conclusion, the developed model can be used to predict the growth of SE86 on HM under various temperatures, and considering this pathogen, HM can be produced if safe eggs are used and HM is stored below 7°C.