Modelling of solid-phase tea waste extraction for the removal of manganese from food samples by using artificial neural network approach

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

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.

Analytical application of flower-shaped nickel nanomaterial for the preconcentration of manganese in domestic wastewater samples

In this study, detection sensitivity of the conventional flame atomic absorption spectrophotometer (FAAS) for the determination of manganese (Mn2+) was enhanced by employing a preconcentration method from wastewater samples. Flower-shaped Ni(OH)2 nanomaterials were synthesized and used as sorbent material in preconcentration procedure. With the aim of attaining optimum experimental conditions, effective parameters of extraction method were optimized and these included pH of buffer solution, desorption solvent concentration and volume, mixing type and period, nanoflower amount, and sample volume. The detection limit of the optimized method was determined to be 2.2 μg L-1, and this correlated to about 41-fold enhancement in detection power relative to direct FAAS measurement. Domestic wastewater was used to test the feasibility of the proposed method to real samples by performing spike recovery experiments. The wastewater sample was spiked at four different concentrations of manganese, and the percent recoveries determined were in the range of 95-120%.

Simultaneously speciation and determination of manganese (II) and (VII) ions in water, food, and vegetable samples based on immobilization of N-acetylcysteine on multi-walled carbon nanotubes

A novel method based on the immobilization of N-acetylcysteine on chloro-functionalized multi-walled carbon nanotubes (MWCNTs@NAC) was used for the speciation of manganese ions [Mn (II) and Mn(VII)] in water samples. Also, the total manganese (TMn) in vegetables and food samples was determined by the AT-FAAS. By ultrasound-assisted-dispersive ionic liquid trap micro solid-phase extraction (UA-DILT-μ-SPE), the Mn (II)/Mn(VII) ions were extracted in the presence of MWCNTs@NAC for 50 mL of water samples at a pH of 6.5 and 3.0, respectively. The adsorption capacity of MWCNTs@NAC for Mn(II) and Mn(VII) ions was obtained at 146.7 mg g^-1 and 138.8 mg g^-1, respectively. Under the optimized conditions, the detection limits (LOD), linear range (LR), and enrichment factor (EF) for Mn(II) and Mn(VII) ions were obtained (0.12 μg L^-1; 0.14 μg L^-1), (0.48-36 μg L^-1; 0.55-38.1 μg L^-1) and (100.2; 94.5), respectively. The proposed methodology was successfully validated by the CRM samples.

Poly(lauryl methacrylate)-Grafted Amino-Functionalized Zirconium-Terephthalate Metal-Organic Framework: Efficient Adsorbent for Extraction of Polycyclic Aromatic Hydrocarbons from Water Samples

In this study, a novel porous hybrid material, poly(lauryl methacrylate) polymer-grafted UiO-66-NH2 (UiO = University of Oslo), was synthesized for efficient extraction of polycyclic aromatic hydrocarbons (PAHs) from aqueous samples. The polymer end-tethered covalently to the MOF's surface was synthesized by surface-initiated atom transfer radical polymerization, revealing a distinct type of morphology. The adsorbent was characterized by scanning electron microscopy, energy-dispersive spectroscopy, transmission electron microscopy, powder X-ray diffraction, N2 adsorption-desorption analysis, Fourier transform infrared spectroscopy, and thermogravimetric analysis. The analyses were carried out by gas chromatography-mass spectrometry. Parameters including the type and volume of the eluent, the amount of the adsorbent, and adsorption and desorption times were investigated and optimized. Under optimal conditions, the limit of detection, intraday precision, and interday precision were in the range of 3-8 ng L-1, 1.4-3.1, and 4.1-6.5%, respectively. The procedure was used for analysis of PAHs from natural water samples.

Prediction of Pasting Properties of Dough from Mixolab Measurements Using Artificial Neuronal Networks

An artificial neuronal network (ANN) system was conducted to predict the Mixolab parameters which described the wheat flour starch-amylase part (torques C3, C4, C5, and the difference between C3-C4and C5-C4, respectively) from physicochemical properties (wet gluten, gluten deformation index, Falling number, moisture content, water absorption) of 10 different refined wheat flourssupplemented bydifferent levels of fungal α-amylase addition. All Mixolab parameters analyzed and the Falling number values were reduced with the increased level of α-amylase addition. The ANN results accurately predicted the Mixolab parameters based on wheat flours physicochemical properties and α-amylase addition. ANN analyses showed that moisture content was the most sensitive parameter in influencing Mixolab maximum torque C3 and the difference between torques C3 and C4, while wet gluten was the most sensitive parameter in influencing minimum torque C4 and the difference between torques C5 and C4, and α-amylase level was the most sensitive parameter in predicting maximum torque C5. It is obvious that the Falling number of all the Mixolab characteristics best predicted the difference between torques C3 and C4.

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

BACKGROUND

Benzo[a]pyrene (BaP), a potent mutagen and carcinogen, is reported to be present in processed meat products and, in particular, in smoked meat. However, few methods exist for predictive determination of the BaP content of smoked meats such as sausage. In this study, an artificial neural network (ANN) model based on the back-propagation (BP) algorithm was used to predict the BaP content of smoked sausage.

RESULTS

The results showed that the BP network based on the Levenberg-Marquardt algorithm was the best suited for creating a nonlinear map between the input and output parameters. The optimal network structure was 3-7-1 and the learning rate was 0.6. This BP-ANN model allowed for accurate predictions, with the correlation coefficients (R) for the experimentally determined training, validation, test and global data sets being 0.94, 0.96, 0.95 and 0.95 respectively. The validation performance was 0.013, suggesting that the proposed BP-ANN may be used to predictively detect the BaP content of smoked meat products.

CONCLUSION

An effective predictive model was constructed for estimation of the BaP content of smoked sausage using ANN modeling techniques, which shows potential to predict the BaP content in smoked sausage. © 2017 Society of Chemical Industry.

Modeling and optimization of Newfoundland shrimp waste hydrolysis for microbial growth using response surface methodology and artificial neural networks

The hydrolyzed protein derived from seafood waste is regarded as a premium and low-cost nitrogen source for microbial growth. In this study, optimization of enzymatic shrimp waste hydrolyzing process was investigated. The degree of hydrolysis (DH) with four processing variables including enzyme/substrate ratio (E/S), hydrolysis time, initial pH value and temperature, were monitored. The DH values were used for response surface methodology (RSM) optimization through central composite design (CCD) and for training artificial neural network (ANN) to make a process prediction. Results indicated that the optimum levels of variables are: E/S ratio at 1.64%, hydrolysis time at 3.59h, initial pH at 9 and temperature at 52.57°C. Hydrocarbon-degrading bacteria Bacillus subtilis N3-1P was cultivated using different DHs of hydrolysate. The associated growth curves were generated. The research output facilitated effective shrimp waste utilization.