Intelligence detection of oil absorption in French fries by surface profiles.

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

New Biological and Chemical Insights into Optimization of Chamomile Extracts by Using Artificial Neural Network (ANN) Model.

Chamomile is one of the most consumed medicinal plants worldwide. Various chamomile preparations are widely used in various branches of both traditional and modern pharmacy. However, in order to obtain an extract with a high content of the desired components, it is necessary to optimize key extraction parameters. In the present study, optimization of process parameters was performed using the artificial neural networks (ANN) model using a solid-to-solvent ratio, microwave power and time as inputs, while the outputs were the yield of the total phenolic compounds (TPC). Optimized extraction conditions were as follows: a solid-to-solvent ratio of 1:80, microwave power of 400 W, extraction time of 30 min. ANN predicted the content of the total phenolic compounds, which was later experimentally confirmed. The extract obtained under optimal conditions was characterized by rich composition and high biological activity. Additionally, chamomile extract showed promising properties as growth media for probiotics. The study could make a valuable scientific contribution to the application of modern statistical designs and modelling to improve extraction techniques.

A novel pseudo-multicomponent isoconversional approach for the estimation of kinetic and thermodynamic parameters of potato stalk thermal degradation.

This study examined the thermal degradation kinetics of potato stalk (PS) using a unique isoconversional technique. The kinetic analysis was assessed based on mathematical deconvolution approach with model-free method. The thermogravimetric analyzer (TGA) was used for the non-isothermal pyrolysis of PS at different heating rates. The Gaussian function was then used to extract three pseudo-components (PC) from the TGA findings. The average activation energy value for PS (125.99, 122.79, and 122.85 kJ/mol), PC1 (106.78, 103.83, and 103.92 kJ/mol), PC2 (120.26, 116.31, and 116.55 kJ/mol), and PC3 (373.12, 379.40, and 378.93 kJ/mol) based on OFW, KAS, and VZN model respectively. Furthermore, an artificial neural network (ANN) was used to forecast the thermal degradation data. The findings demonstrated a significant correlation between real and anticipated values. The kinetic and thermodynamic results, along with ANN are critical for constructing pyrolysis reactors that might use waste biomass as a potential feedstock for bioenergy production.

Changes in polyphenolic compounds and antioxidant activities of seed-used pumpkin during hydrothermal treatment.

An environmentally friendly physical processing method, hydrothermal treatment (HT), was used to increase the content of specific compounds and antioxidant activities of seed-used pumpkin byproducts. The influence of hydrothermal temperature (80 °C-160 °C) and time (30-150 min) on changes in polyphenols and antioxidation was evaluated. The results revealed that the maximum free polyphenol content (140 °C for 120 min) was 3.96-fold higher than the untreated samples. Elevated temperature and long duration changed phenolic acid contents. For example, p-coumaric acid, rutin and chlorogenic acid exhibited a decreasing trend, and p-hydroxybenzoic acid, quercetin and cinnamic acid showed an increasing trend. Compared to controls, HT was significantly associated with increased antioxidant activities. To comprehensively reveal the influence of hydrothermal temperature and time on changes in polyphenolic content, back propagation artificial neural network (BP-ANN) models with accurate prediction ability were developed, and the results exhibited well-fitted and strong approximation ability (R2 > 0.95 and RMSE < 2 %) and stability.

Recovery of Biologically Active Compounds from Stinging Nettle Leaves Part II: Processing of Exhausted Plant Material after Supercritical Fluid Extraction.

Stinging nettle (Urtica dioica L.) is one fantastic plant widely used in folk medicine, pharmacy, cosmetics, and food. This plant's popularity may be explained by its chemical composition, containing a wide range of compounds significant for human health and diet. This study aimed to investigate extracts of exhausted stinging nettle leaves after supercritical fluid extraction obtained using ultrasound and microwave techniques. Extracts were analyzed to obtain insight into the chemical composition and biological activity. These extracts were shown to be more potent than those of previously untreated leaves. The principal component analysis was applied as a pattern recognition tool to visualize the antioxidant capacity and cytotoxic activity of extract obtained from exhausted stinging nettle leaves. An artificial neural network model is presented for the prediction of the antioxidant activity of samples according to polyphenolic profile data, showing a suitable anticipation property (the r2 value during the training cycle for output variables was 0.999).

Biotransformation of grease waste into fatty acid by Penicillium chrysogenum SNP5 through media engineering and artificial neural network.

Degradation of grease waste remains a challenging task. Current work deals with the biotransformation of grease waste into fatty acids under submerged fermentation using Penicillium chrysogenum SNP5 through media formulation and artificial neural network (ANN). Fermentation media was formulated to ameliorate the uptake of hydrocarbon by enhancing alkane hydroxylase (AlkB) activity, extracellular release of fatty acids and inhibiting beta-oxidation of fatty acid by regulating transketolase. Further, the process parameters of fermentation were optimized through Artificial Neural Network (ANN) using three critical variables viz; inoculum size (spores/ml), pH, and incubation time (days) while media engineering was done with the optimal supplementation of various medium components such as glucose, YPD, MnSO4, tetrahydrobiopterin (THB) and phloretin. The maximum conversion of 66.5% of grease waste into fatty acid was achieved at optimum conditions: inoculums size 3.36 × 107 spores/ml, incubation time 11.5 days, pH 7.2 along with formulated media composed of 1% grease in czapek-dox medium supplemented with 55.5 mM glucose, 0.5% YPD, 16.6 mM hexadecane, 1 mM MnSO4, 1 mM THB, and 1 mM phloretin. The presence of long-chain fatty acids in purified extracts such as oleic acid and octadecanoic acid as end products has valued the evolved process as another source of alternative fuel.

A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which uses an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86% for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.

Reading Wishes from the Lips: Cancer Patients' Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment.

BACKGROUND: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. METHODS: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. RESULTS: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients' requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. DISCUSSION: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system.

To Estimate Performance of Artificial Neural Network Model Based on Terahertz Spectrum: Gelatin Identification as an Example.

It is a necessity to determine significant food or traditional Chinese medicine (TCM) with low cost, which is more likely to achieve high accurate identification by THz-TDS. In this study, feedforward neural networks based on terahertz spectra are employed to predict the animal origin of gelatins, whose adaption to the mission is examined by parallel models built by random sample partition and initialization. It is found that the generalization performance of feedforward ANNs in original data is not satisfactory although prediction on trained samples can be accurate. A multivariate scattering correction is conducted to enhance prediction accuracy, and 20 additional models verify the effectiveness of such dispose. A special partition of total dataset is conducted based on statistics of parallel models, whose influence on ANN performance is investigated with another 20 models. The performance of the models is unsatisfactory because of notable differences in training and test sets according to principal component analysis. By comparing the distribution of the first two principal components before and after multivariate scattering correction, we found that the reciprocal of the minimum number of line segments required for error-free classification in 2-D feature space can be viewed as an index to describe linear separability of data. The rise of proposed linear separability would have a lower requirement for harsh parameter tuning of ANN models and tolerate random initialization. The difference in principal components of samples between a training set and a data set determines whether partition is acceptable or whether a model would have generality. A rapid way to estimate the performance of an ANN before sufficient tuning on a classification mission is to compare differences between groups and differences within groups. Given that a representative peak missing curve is discussed in this article, an analysis based on gelatin THz spectra may be helpful for studies on some other feature-less species.

Metronidazole photocatalytic degradation by zinc oxide nanoparticles synthesized in watermelon peel extract; Advanced optimization, simulation and numerical models using machine learning applications.

Antibiotics in water systems and wastewater are among the greatest major public health problem and it is global environmental issues. Herein a novel approach for the photocatalytic degradation of metronidazole (MTZ) by using eco-green zinc oxide nanoparticles (EG-ZnO NPs) which biosynthesised using watermelon peels extracts has been investigated. Mathematical prediction models using an adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and response surface methodology (RSM) were used to determine the optimal conditions for the degradation process. The FESEM analysis revealed that EG-ZnO NPs was white with a spherical shape and size between 40 and 88 nm. The simulation process for the mathematical prediction model revealed that the best validation performance was 55.35 recorded at epoch 2, the coefficient (R2) was 0.9967 for training data, as detected using ANN analysis. The best operating parameters for MTZ degradation was predicted using RSM to be: 170 mg L-1 of EG-ZnO NPs, 20.61 mg 100 mL-1 of MTZ, 10 min exposure time, and a pH of 5, with 77.48 vs 78.14% corresponding to the predicted and empirically measured respectively. The photocatalytic degradation of MTZ was fitted with pseudo-first-order kinetic (R2 > 0.90). MTZ lost the antimicrobial activity against Bacillus cereus (B. cereus) and Escherichia coli (E. coli) after degradation with EG-ZnO NPs at the optimal conditions as determined in the optimization process. These findings reflect the important role ANFIS and ANN in predicting and optimising the efficacy of engineered nanomaterials, including EG-ZnO NPs, for antibiotic degradation.

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil.

In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (E)-2-decenal, (E,E)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.

Ultrasound-assisted extraction of phenolic acids, flavonols, and flavan-3-ols from muscadine grape skins and seeds using natural deep eutectic solvents and predictive modelling by artificial neural networking.

The objective of this study was to investigate the extraction efficiency of 9 natural deep eutectic solvents (NDES) with the assistance of ultrasound for phenolic acids, flavonols, and flavan-3-ols in muscadine grape (Carlos) skins and seeds in comparison to 75% ethanol. Artificial neural networking (ANN) was applied to optimize NDES water content, ultrasonication time, solid-to-solvent ratio, and extraction temperature to achieve the highest extraction yields for ellagic acid, catechin and epicatechin. A newly formulated NDES (#1) consists of choline chloride: levulinic acid: ethylene glycol 1:1:2 and 20% water extracted the highest amount of ellagic acid in the skin at 22.1 mg/g. This yield was 1.73-fold of that by 75% ethanol. A modified NDES (#3) consisting of choline chloride: proline: malic acid 1:1:1 and 30% water extracted the highest amount of catechin (0.61 mg/g) and epicatechin (0.89 mg/g) in the skin, and 2.77 mg/g and 0.37 mg/g in the seed, respectively. The optimal yield of ellagic acid in the skin using NDES #1 was 25.3 mg/g (observed) and 25.3 mg/g (predicted). The optimal yield of (catechin + epicatechin) in seed using NDES #3 was 9.8 mg/g (observed) and 9.6 mg/g (predicted). This study showed the high extraction efficiency of selected NDES for polyphenols under optimized conditions.

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network.

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R2 > 0.926, RMSEC < 0.481) compared with partial least-squares model (R2 > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.

A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array.

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people's diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples' dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.

Development, validation, and comparison of FE modeling and ANN model for mixed-mode solar drying of potato cylinders.

This work aims to develop a finite element (FE) model for predicting temperature and moisture ratio of potato cylinders having diameters of 8, 10, 13 mm and 50 mm length during solar drying using COMSOL Multiphysics software. The developed model computed conduction, convection, and radiation with appropriate governing and boundary conditions by coupling heat transfer in solid, laminar flow, transport of diluted species, and moving mesh modules together. Moving mesh module was employed to embrace the effect of inevitable shrinkage parameter all through solar drying. Experimentations and calculations were done based on the requirement of FE model. The developed model showed the increment of product temperature from 299.51-313.73 K, 299.07-313.03 K, and 298.34-314.57 K in case of 8, 10, and 13 mm diameter samples for an effective drying period of 3 h 15 min, 4 h 15 min, and 5 h, respectively. At the same time, the moisture content reduced from 83.57%, 86.57%, and 82.12% (wb) to 9.08%, 9.99%, 10.44% (wb) for the respective samples. To prove the reliability of the FE model predicted results, an attempt was made through the artificial neural network (ANN) model for describing the drying performance of the potato as well. It was found that the FE model better simulated the drying behavior with higher R2 values (R2  = 0.988-0.995). The drying chamber air temperature was also simulated from FE model and validated with experimental data during drying of samples. The prediction capability of FE proposed model based on statistical error analysis showed lower values than ANN model. PRACTICAL APPLICATION: In the present study, the potential of mixed-mode solar drying in food processing industries was established showing detailed investigation of transport processes throughout the solar drying process of potato cylinders. The established finite element (FE) model can be considered as a realistic alternative to experimentation. The food processing industries and dryer engineers can achieve better quality dried products by precisely operating the dryers at the optimum condition by help of the proposed FE model and product shrinkage analysis.

Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy.

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Development of Artificial Neural Network for prediction of radon dispersion released from Sinquyen Mine, Vietnam.

Understanding the radon dispersion released from this mine are important targets as radon dispersion is used to assess radiological hazard to human. In this paper, the main objective is to develop and optimize a machine learning model namely Artificial Neural Network (ANN) for quick and accurate prediction of radon dispersion released from Sinquyen mine, Vietnam. For this purpose, a total of million data collected from the study area, which includes input variables (the gamma data of uranium concentration with 3 × 3m grid net survey inside mine, 21 of CR-39 detectors inside dwellings surrounding mine, and gamma dose at 1 m from ground surface data) and an output variable (radon dispersion) were used for training and validating the predictive model. Various validation methods namely coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used. In addition, Partial dependence plots (PDP) was used to evaluate the effect of each input variable on the predictive results of output variable. The results show that ANN performed well for prediction of radon dispersion, with low values of error (i.e., R2 = 0.9415, RMSE = 0.0589, and MAE = 0.0203 for the testing dataset). The increase of number of hidden layers in ANN structure leads the increase of accuracy of the predictive results. The sensitivity results show that all input variables govern the dispersion radon activity with different amplitudes and fitted with different equations but the gamma dose is the most influenced and important variable in comparison with strike, distance and uranium concentration variables for prediction of radon dispersion.

Dielectric Engineered Two-Dimensional Neuromorphic Transistors.

Two-dimensional (2D) materials, which exhibit planar-wafer technique compatibility and pure electrically triggered communication, have established themselves as potential candidates in neuromorphic architecture integration. However, the current 2D artificial synapses are mainly realized at a single-device level, where the development of 2D scalable synaptic arrays with complementary metal-oxide-semiconductor compatibility remains challenging. Here, we report a 2D transition metal dichalcogenide-based synaptic array fabricated on commercial silicon-rich silicon nitride (sr-SiNx) substrate. The array demonstrates uniform performance with sufficiently high analogue on/off ratio and linear conductance update, and low cycle-to-cycle variability (1.5%) and device-to-device variability (5.3%), which are essential for neuromorphic hardware implementation. On the basis of the experimental data, we further prove that the artificial synapses can achieve a recognition accuracy of 91% on the MNIST handwritten data set. Our findings offer a simple approach to achieve 2D synaptic arrays by using an industry-compatible sr-SiNx dielectric, promoting a brand-new paradigm of 2D materials in neuromorphic computing.

Development of ANN models based on combined UV-vis-NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts.

OBJECTIVES: The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid-liquid extraction and microwave-assisted extraction) based on combined UV-VIS-NIR spectra. Spectral data were gathered for 46 samples per extraction method. RESULTS: The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave-assisted extraction. CONCLUSIONS: Results showed that reliable ANN models (R2 >0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV-VIS-NIR spectra of industrial hemp extracts without spectra pre-processing.