Spectrophotometric determination of synthetic colorants using PSO–GA-ANN

Benvidi, Ali; Abbasi, Saleheh; Gharaghani, Sajjad; Tezerjani, Marzieh Dehghan; Masoum, Saeed

doi:10.1016/j.foodchem.2016.10.010

Cited by 67 publications

(23 citation statements)

References 28 publications

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“…ANN mimics how a biological brain works with a network of neurons processing information and signals from neighboring neurons [ 27 ]. Many applications in the literature have been reported using ANN in analysis of various foods, including classification of red wines [ 28 ], maturity of dry-cured hams [ 29 ], classification of vegetable oils [ 30 ], evaluation of olive oil stability [ 31 ], classification of olive oils cultivars [ 32 ], detection of synthetic colorants [ 33 ], geographical traceability of bottled spring water [ 34 ], and classification of cow’s milk [ 35 ] to name just a few. Advantages of ANN over linear chemometric methods includes its ability of dealing with non-linearity allowing for a better fit of the data, insensitivity to noise providing more accurate prediction, adaptability permitting the model to modify its structure accordingly for the changing environment, and generalization allowing to fit new data to the trained model [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Akpolat

Barineau²,

Jackson³

et al. 2020

Sensors

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Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and β-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (rpre = 0.9, SEP = 1.1 mg/100 g) and PLSR (rpre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for β-carotene quantification, PLSR modeling showed promising results (rcv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in β-carotene and high in cis-lycopene).

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Section: Methodsmentioning

confidence: 99%

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Akpolat

Barineau²,

Jackson³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Su et al [6] elaborated a Hickory milk-based yogurt, finding an important antioxidant activity, effect attributed to the presence of phenolic compounds and small peptides. In addition to some reports with the addition of colorants of natural origin [7,8] to replace artificial colorants that could damage health because its use has been associated with allergies, asthma, hyperactive behavior in children, and it has been associated with an increased risk of cancer [9,10].…”

Section: Introductionmentioning

confidence: 99%

Multiple Emulsions with Extracts of Cactus Pear Added in A Yogurt: Antioxidant Activity, In Vitro Simulated Digestion and Shelf Life

Cenobio-Galindo

Díaz-Monroy

Medina‐Pérez

et al. 2019

Foods

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Consumers demand so-called natural in which additive and antioxidant preservatives are from natural origin. Research focuses in using extracts from plants and fruits that are rich in bioactive compounds such as phenolics and betalains, but these are also prone to interact with proteins and are exposed to suffer degradation during storage. In this work, we developed a fortified yogurt with the addition of betalains and polyphenols from cactus pear extract encapsulated in a multiple emulsion (ME) (W1/O/W2). Different formulations of ME were made with two polymers, gum arabic (GA) and maltodextrin (MD) and with the best formulation of ME four types of yogurt were prepared using different % (w/w) of ME (0%, 10%, 20% and 30%). Bioactive compounds, antioxidant activity, color and lactic acid bacteria (LAB) were analyzed in the different yogurts over 36 days of shelf life. Furthermore, in vitro simulated digestion was evaluated. The yogurts had significant (p < 0.05) differences and the ME protected the bioactive compounds, activity of antioxidants and color. The ME did not affect the viability of LAB during 36 days of storage. The in vitro digestion showed the best bioaccessibilities of antioxidant compounds with the yogurts with ME.

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“…Levenberg-Marquardt function with gradient descent methods is typically used in assessing ANN parameters (Benvidi, Abbasi, Gharaghani, Tezerjani, & Masoum, 2017). The error value resulting from the actual and predicted values of the dependent variables was propagated backward from output to the hidden layer and then from hidden to the input layer in a chain fashion.…”

Section: Development Of Ann Modelingmentioning

confidence: 99%

Modeling and optimization of microwave puffing of rice using artificial neural network and genetic algorithm

Dash

Das

2020

J Food Process Engineering

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A genetic algorithm based on artificial neural network (ANN) modeling was developed for the microwave puffing of preconditioned rice. The microwave puffing method was analyzed in order to find out the effect of microwave power, puffing time, butter level, and sodium bicarbonate level on the expansion ratio and puffing percentage of puffed rice. ANN modeling was applied between the independent and dependent variables and 4‐7‐2 architecture was selected as the best ANN architecture with mean squared error value ranging between 1.138 and 1.839. The optimized conditions obtained from the hybrid ANN‐GA approach were 850 W of microwave power, 35 s of puffing time, 5.26% of butter, and 1.46% of sodium bicarbonate. The optimum combination of independent variables resulted in maximum expansion ratio and percentage puffing of 8.4 and 94.37%, respectively. The relative deviations between the experimental and ANN‐GA model predicted values for expansion ratio and percentage puffing were 1.42 and 1.04%, respectively. The micrograph analysis showed that the parboiled rice had closely packed swollen starch granules with a fused endosperm, while large porous structured vacuoles were found in puffed rice. This study would provide a simple and efficient way to obtain crispy puffed rice by using a domestic microwave oven.Practical ApplicationsMicrowave puffing produces low fat ready to eat healthy products than the deep fat fried product. The microwave generates rapid heat and mass transfer in the food product resulting in the puffed product of desirable porous structure. Evaluation of optimal microwave puffing conditions will produce puffed rice with high sensory value and nutritional value. On this basis, the present study emphasized the effects of processing parameters and the potential of two additives, that is, butter and sodium bicarbonate, for microwave puffing of rice. The impact of various processing and operating conditions of microwave puffing of rice was analyzed. This research would make a way to make puffed rice at the domestic level in a quicker and easier process. This process would develop a suitable package containing preconditioned rice kernel with butter and sodium bicarbonate to obtain ready to eat puffed rice by using a domestic microwave oven.

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Spectrophotometric determination of synthetic colorants using PSO–GA-ANN

Cited by 67 publications

References 28 publications

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Multiple Emulsions with Extracts of Cactus Pear Added in A Yogurt: Antioxidant Activity, In Vitro Simulated Digestion and Shelf Life

Modeling and optimization of microwave puffing of rice using artificial neural network and genetic algorithm

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