Modeling of the lycopene extraction from tomato pulps

Optimization of Millet Malting Parameters Using Artificial Neural Network and Response Surface Methodology

The quality of malt produced from cereals is significantly influenced by various factors, including steeping and germination periods. Monitoring these factors and their effects on malt grain characteristics is often time-consuming and costly. In this context, this study aimed to predict trends in changes to certain characteristics of millet-derived malt, influenced by varying steeping durations (24-48 h) and germination times (5-9 days). Changes in these characteristics were predicted using response surface methodology (RSM), which incorporated a central composite design and an artificial neural network (ANN). The findings indicated that increasing the steeping and germination durations led to a decrease in malting efficiency, thousand grain weight, and true density of the samples. Conversely, the cold-water extract efficiency, the Kolbach index, and the extract color increased. The optimization process revealed that to achieve the highest-quality malt, the steeping duration should be 42.54 h, followed by a germination period of 5 days. Under these conditions, the malting efficiency reached 75.44%, with a thousand grain weight of 4.85 g, a true density of 977.43 kg/m3, a cold-water extract efficiency of 9.19%, a Kolbach index of 32.45%, and an extract color value of 13.87. An analysis of different neural networks revealed that the feed-forward backpropagation network with a 2-6-6 topology was the best-performing model. This network achieved a correlation coefficient greater than 0.999 and a mean squared error of less than 0.00001. It employed the hyperbolic tangent sigmoid transfer function, the resilient backpropagation learning algorithm, and 1000 learning cycles. Furthermore, a comparison of the correlation coefficients derived from the RSM and the ANN demonstrated that the ANN method is superior for predicting changing trends in millet grains during the malting process.

The effect of maltodextrin edible coating containing pyracantha extract and potassium nano-carbonate on secondary metabolites, antioxidant capacity and microbiological properties of grape during cold storage

This study aimed to investigate the postharvest application of edible coating of maltodextrin containing potassium nanoparticles (KNPs; 0-2%) and pyracantha extract (PE; 0-1.5 %) on the maintenance of phenolic compounds, antioxidant capacity and microbial properties of 'Rishbaba' grape during 60 days at -1 °C using response surface methodology and central composite design. The results showed that the applied coating on Rishbaba grape maintained total phenol, total flavonoids, total anthocyanin, stilbenes (resveratrol and viniferin) and catechin. That also caused higher antioxidant capacity and lower mold and yeast growth in grape during the storage time. Finally, the best cold storage conditions of 'Rishbaba' grape were determined by using the edible maltodextrin coating containing 2 % KNPs and 1.5 % of PE for 34 days with a desirability of 0.728 in terms of investigated Characteristics. The optimized sample has the amount of total phenol (5.79 mg/g), total flavonoid (8.95 mg/g), total anthocyanin (6.48 mg/g) and the greatest ability to inhibit DPPH free radical (42.56 %) and the lowest growth rate of mold and yeast (21 Cfu/g).

Artificial intelligence-based prediction of lycopene content in raw tomatoes using physicochemical attributes

INTRODUCTION

Lycopene consumption reduces risk and incidence of cancer and cardiovascular diseases. Tomatoes are a rich source of phytochemical compounds including lycopene as a major constituent. Lycopene estimation using high-performance liquid chromatography is time-consuming and expensive.

OBJECTIVE

To develop artificial intelligence models for prediction of lycopene in raw tomatoes using 14 different physicochemical parameters including salinity, total dissolved solids (TDS), electrical conductivity (EC), firmness, pH, total soluble solids (TSS), titratable acidity (TA), colour values on Hunter scale (L, a, b), total phenolic content (TPC), total flavonoid content (TFC) and antioxidant activity (AOA).

MATERIAL AND METHODS

The post-harvest data acquisition was collected through investigation for more than 100 raw tomatoes stored for 15 days. Linear multivariate regression (LMVR), principal component regression (PCR) and partial least squares regression (PLSR) models were developed by splitting data set into train and test datasets. The training of models was performed using 10-fold cross validation (CV).

RESULTS

Principal component analysis showed strong positive association between lycopene, colour value 'a', TPC, TFC and AOA. The R2 (CV), root mean square error (RMSE) (CV) and RMSE (Test) for best LMVR model was observed to be at 0.70, 8.48 and 9.69 respectively. The PCR model revealed R2 (CV) at 0.59, RMSE (CV) at 8.91 and RMSE (Test) at 10.17 while PLSR model revealed R2 (CV) at 0.60, RMSE (CV) at 9.10 and RMSE (Test) at 10.11.

CONCLUSION

Results of the present study show that epidemiological studies suggest fully ripened tomatoes are most beneficial for consumption to ensure recommended daily intake of lycopene content.

A Comprehensive Overview of Tomato Processing By-Product Valorization by Conventional Methods versus Emerging Technologies

The tomato processing industry can be considered one of the most widespread food manufacturing industries all over the world, annually generating considerable quantities of residue and determining disposal issues associated not only with the wasting of invaluable resources but also with the rise of significant environmental burdens. In this regard, previous studies have widely ascertained that tomato by-products are still rich in valuable compounds, which, once recovered, could be utilized in different industrial sectors. Currently, conventional solvent extraction is the most widely used method for the recovery of these compounds from tomato pomace. Nevertheless, several well-known drawbacks derive from this process, including the use of large quantities of solvents and the difficulties of utilizing the residual biomass. To overcome these limitations, the recent advances in extraction techniques, including the modification of the process configuration and the use of complementary novel methods to modify or destroy vegetable cells, have greatly and effectively influenced the recovery of different compounds from plant matrices. This review contributes a comprehensive overview on the valorization of tomato processing by-products with a specific focus on the use of "green technologies", including high-pressure homogenization (HPH), pulsed electric fields (PEF), supercritical fluid (SFE-CO₂), ultrasounds (UAE), and microwaves (MAE), suitable to enhancing the extractability of target compounds while reducing the solvent requirement and shortening the extraction time. The effects of conventional processes and the application of green technologies are critically analyzed, and their effectiveness on the recovery of lycopene, polyphenols, cutin, pectin, oil, and proteins from tomato residues is discussed, focusing on their strengths, drawbacks, and critical factors that contribute to maximizing the extraction yields of the target compounds. Moreover, to follow the "near zero discharge concept", the utilization of a cascade approach to recover different valuable compounds and the exploitation of the residual biomass for biogas generation are also pointed out.

Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine

Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned that their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.

Comprehensive study on applications of artificial neural network in food process modeling

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed.HIGHLIGHTS1. This paper discusses application of ANN in different emerging trends in food process.2. Application of ANN to develop non-linear multivariate modeling is illustrated.3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy.4. ANN resulted in fast response making it suitable for application in real time systems.

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP

Self-made milk knots in Xinjiang Kazakh ethnic group were used as material to establish the quality assessment system of flavor quality. The fuzzy analytic hierarchy process based on the optimal consistency matrix was used to evaluate the quality of the samples qualitatively and quantitatively. Its result is consistent with the cluster analysis of the SOM neural network. The results showed that the milk knot samples of Altay had differences with the milk knot samples of Yili. The comprehensive evaluation system is feasible and can evaluate the quality of milk knot samples by flavor characteristics. This can provide a reference for further research on the origin of differences between two types of milk knot samples.

Increase of Akkermansia muciniphila by a Diet Containing Japanese Traditional Medicine Bofutsushosan in a Mouse Model of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is considered a worldwide healthcare problem that mirrors the increased prevalence of obesity. Gut microbiota plays a crucial role in the progression and treatment of NAFLD. Bofutsushosan (BTS), a pharmaceutical-grade Japanese traditional medicine, has long been prescribed in Japan for obesity and obesity-related syndrome. Although BTS has been reported to exert an anti-obesity effect in obese patients as well as various obesity-model animals, its effect on gut microbiota is unknown. Here, the effects of BTS on obesity, liver damage, and the gut microbiome in genetically obese mice, ob/ob, were studied. Seven-week-old ob/ob mice were fed a standard diet with (BTS group) or without (CONT group) 5% BTS for 4 weeks. By comparison to the CONT group, the BTS group showed reduced body weight gain and hyperlipidemia as well as improved liver function. Moreover, gut microbiota in the CONT and BTS group formed a significantly different cluster. Specifically, the genera Akkermansia, Bacteroides and an unknown genus of the family Enterobacteriaceae expanded dramatically in the BTS group. Noteworthy, the population of Akkermansia muciniphila, which is reported to elicit an anti-obesity effect and improve various metabolic abnormalities, was markedly increased (93-fold) compared with the CONT group. These results imply that BTS may be a promising agent for treating NAFLD.

Microwave Pretreatment and Enzymolysis Optimization of the Lotus Seed Protein

Pretreatment with a microwave was conducted before enzymolysis and shown to enhance the enzymolysis, which changed the secondary structure of the lotus seed protein. Under high-power microwave irradiation, sub bonds of the protein were broken, causing disaggregation and unfolding of the secondary structure, namely a decrease in the intermolecular aggregate structure and increase in the random coil structure, making the protein bonds susceptible to papain in the enzymolysis. On the other hand, a response surface methodology (RSM) was launched to investigate the influence of the enzymolysis process variables on the DH (degree of hydrolysis). The statistical analysis revealed that the optimized conditions were a protein substrate concentration of 15 g/L, pH of 5.5, enzymolysis temperature of 57 °C, papain amount of 0.5 g/L, and enzymolysis time of 45 min, for which the predicted value of the DH was 35.64%. The results indicated that a microwave also had better potential for applications in the enzymolysis of foods.

Effects of thermal processing on the structural and functional properties of soluble dietary fiber from whole grain oats

Normal pressure steaming, high pressure steaming, microwave, and frying are widely used to deactivate enzyme in the oats, but these thermal processing methods may affect the structural and functional properties of soluble dietary fiber, which contribute greatly to the health benefits of oat foods. The objective of this study was to evaluate the effects of four different thermal processing methods on the structural and functional properties of soluble dietary fiber from whole grain oats. The results showed that the thermal processing resulted in changes on nutritional components of whole grain oats. Especially dietary fiber components, the total dietary fiber, insoluble dietary fiber, and soluble dietary fiber content of heat-treated oats were significantly increased ( p < 0.05). Moreover, thermal processing can not only result in an increase in molecular weight and particle size, but also cause molecular aggregation and different functional properties of soluble dietary fiber. High pressure steaming-treated oat soluble dietary fiber displayed significantly higher swelling and emulsifying ( p < 0.05), but microwave-treated oat soluble dietary fiber exhibited the highest glucose, cholesterol, and sodium cholate adsorption capacities. These results might provide basic information to help to better understand the functionality of oat soluble dietary fiber and improve the process efficiency of oat foods with high nutritional qualities.

Change in the color of heat-treated, vacuum-packed broccoli stems and florets during storage: effects of process conditions and modeling by an artificial neural network

BACKGROUND

Vacuum-packed broccoli stems and florets were subjected to heat treatment (60-99 °C) for various time intervals. The activity of peroxidase was measured after processing. Thermally processed samples were then stored at 4 °C for 35 days, and the color of the samples was measured every 7 days. Effects of parameters (heating temperature and duration, storage time) on the color of broccoli were modeled and simulated by an artificial neural network (ANN).

RESULTS

Simulations confirmed that stems were predicted to be more prone to changes than florets. More color loss was observed with longer processing or storage combinations. The simulations also confirmed that higher temperatures during heat processing could retard color changes during storage. For stems treated at 80 °C for short durations, color loss was more predominant than both 65 and 99 °C, probably due to the incomplete inactivation of enzymes besides more tissue damage, with increased enzyme access to the substrate.

CONCLUSION

The greenness of both stems and florets during storage can be better preserved at higher temperatures (99 °C) and short times. The simulation results revealed that the ANN method could be used as an effective tool for predicting and analyzing the color values of heat-treated broccoli. © 2018 Society of Chemical Industry.

Neural Network Modeling of AChE Inhibition by New Carbazole-Bearing Oxazolones

Acetylcholine esterase (AChE) is one of the targeted enzymes in the therapy of important neurodegenerative diseases such as Alzheimer's disease. Many studies on carbazole- and oxazolone-based compounds have been conducted in the last decade due to the importance of these compounds. New carbazole-bearing oxazolones were synthesized from several carbazole aldehydes and p-nitrobenzoyl glycine as AChE inhibitors by the Erlenmeyer reaction in the present study. The inhibitory effects of three carbazole-bearing oxazolone derivatives on AChE were studied in vitro and the experimental results were modeled using artificial neural network (ANN). The developed ANN provided sufficient correlation between several dependent systems, including enzyme inhibition. The inhibition data for AChE were modeled by a two-layered ANN architecture. High correlation coefficients were observed between the experimental and predicted ANN results. Synthesized carbazole-bearing oxazolone derivatives inhibited AChE under in vitro conditions, and further research involving in vivo studies is recommended. An ANN may be a useful alternative modeling approach for enzyme inhibition.

The influence of pulsed electric fields and microwave pretreatments on some selected physicochemical properties of oil extracted from black cumin seed

Application of novel technologies such as microwave and pulsed electric fields (PEF) might increase the speed and efficiency of oil extraction. In the present research, PEF (3.25 kV/cm electric field intensity and 30 pulse number) and microwave (540 W for 180 s) pretreatments were used to study the process of oil extraction from black cumin (Nigella sativa) seeds. After applying the selected pretreatments, the oil of seeds was extracted with the use of a screw press and the extraction efficiency, refractive index, oil density, color index, oxidative stability, and chemical components of oil and protein of meal were evaluated. The achieved results expressed that PEF and microwave pretreatments increased the oil extraction efficiency and its oxidative stability. Different pretreatments didn't have any significant influence on the refractive index of black cumin seed oil (p>.05). When microwave and PEF were used, the oil density showed an enhancement as the following: 1.51% and 0.96%, respectively in comparison with the samples with no pretreatments. Evaluation of the extracted oils, using GC/MS analysis indicated that thymoquinone was the dominant phenolic component in the black cumin oil. Finally, the SEM analysis revealed that microwave and PEF can be useful in the extraction of oil from black cumin seeds since these treatments damaged cell walls and facilitated the oil extraction process.