Spectrophotometric determination of synthetic colorants using PSO–GA-ANN

Identification of chrysanthemum variety via hyperspectral imaging and wavelength selection based on multitask particle swarm optimization

Chrysanthemum, a widely favored flower tea, contains numerous phytochemicals for health benefits. Due to the different geographical origins and processing technics, its variety has a direct influence on the phytochemical content and pharmacological effect. Accordingly, an accurate identification for chrysanthemum varieties is significant for quality detection and market supervision. In this study, the hyperspectral imaging (HSI) combined with chemometrics methods was exploited to identify the chrysanthemum varieties. First, to alleviate the problem of easily trapping into local optimum in traditional spectral variable selection methods, the multi-tasking particle swarm optimization (MTPSO) was developed to select the key wavelengths by dividing hundreds of variables into low-dimensional subtasks. Second, to enrich the feature information, the spatial texture and color features contained in hyperspectral images were extracted and applied to chrysanthemum identification for the first time. Finally, an ensemble learning model, extreme gradient boosting (XGBoost), was constructed to conduct the chrysanthemum variety classification due to its strong generalization ability. Experimental results showed that the proposed MTPSO achieved the identification accuracy of 96.89%, and increased by 1.11-5.91% than classical spectral feature selection methods. Furthermore, after the involvement of spatial image information, the classification accuracy using spatial-spectral features was improved further, and reached 98.39%. Overall, this study highlights that the feature fusion of key wavelengths and spatial information is more effective for chrysanthemum variety identification, and can also provide technical reference for other HSI-related applications.

Selective Voltammetric Sensor for the Simultaneous Quantification of Tartrazine and Brilliant Blue FCF

Tartrazine and brilliant blue FCF are synthetic dyes used in the food, cosmetic and pharmaceutical industries. The individual and/or simultaneous control of their concentrations is required due to dose-dependent negative health effects. Therefore, the paper presents experimental results related to the development of a sensing platform for the electrochemical detection of tartrazine and brilliant blue FCF based on a glassy carbon electrode (GCE) modified with MnO₂ nanorods, using anodic differential pulse voltammetry. Homogeneous and stable suspensions of MnO₂ nanorods have been obtained involving cetylpyridinium bromide solution as a cationic surfactant. The MnO₂ nanorods-modified electrode showed a 7.9-fold increase in the electroactive surface area and a 72-fold decrease in the electron transfer resistance. The developed sensor allowed the simultaneous quantification of dyes for two linear domains: in the ranges of 0.10-2.5 and 2.5-15 μM for tartrazine and 0.25-2.5 and 2.5-15 μM for brilliant blue FCF with detection limits of 43 and 41 nM, respectively. High selectivity of the sensor response in the presence of typical interference agents (inorganic ions, saccharides, ascorbic and sorbic acids), other food dyes (riboflavin, indigo carmine, and sunset yellow), and vanillin has been achieved. The sensor has been tested by analyzing soft and isotonic sports drinks and the determined concentrations were close to those obtained involving the chromatography technique.

An Optimization Method for Mix Proportion of Wet-Mix Shotcrete: Combining Artificial Neural Network with Particle Swarm Optimization

Wet-mix shotcrete has been widely used to support roadways during the excavation of underground mining. In practice, the mix proportion of wet-mix shotcrete plays a key role in later successful application. To obtain an optimal mix proportion, a large number of trial physical experiments should be carried out. Therefore, in this paper, a new ANN–PSO model is proposed to obtain the mix proportion of wet-mix shotcrete quickly, precisely and economically. The artificial neural network (ANN) model was used to establish the objective functions for particle swarm optimization (PSO) optimization, while the PSO was adopted to optimize mix proportions of wet-mix shotcrete to achieve optimal objectives. This hybrid model was applied to optimize mix proportions of wet-mix shotcrete in the Jinchuan mine. The results revealed that the ANN model yielded a mean relative error (MRE) of 2.755% and an R2 of 0.980, indicating an excellent prediction to establish the reasonable objective function. Additionally, PSO spent less than 60 s obtaining an optimal mix proportion of wet-mix shotcrete required by the mine. Consequently, this ANN–PSO model can be used as an efficient design guide to facilitate decision making, prior to the construction phase.

Improved solid phase extraction for selective and efficient quantification of sunset yellow in different food samples using a novel molecularly imprinted polymer reinforced by Fe3O4@UiO-66-NH2

The overuse of synthetic dyes in food products has gradually increased in recent years, resulting food safety and human health has become a global issue. An innovative design of a magnetic molecularly imprinted polymer (Fe3O4@UiO-66-NH2@MIP) for efficient, fast, and selective determination of sunset yellow (SY) from different food products was described in this study. The absorption properties of Fe3O4@UiO-66-NH2@MIP were elucidated by adsorption kinetics, isotherms, reusability, and selectivity experiments. Because of the incorporation of porous Fe3O4@UiO-66-NH2nanocomposite into molecularly imprinted polymer an efficient nanosorbent with a short equilibrium time, a high adsorption capacity, and a good imprinting factor was finally obtained. The porous Fe3O4@UiO-66-NH2@MIP are also used for quantification of the SY. Under optimal conditions, good linearity (R2 0.9964) in the range of 1.0-120 mg L-1 and a low limit of detection (0.41 mg L-1) was observed with satisfactory recoveries (92.50-106.1%) and excellent reusability (RSD ≤ 6.6% after 12 cycles).

Ultrasonic-assisted cloud point microextraction and spectrophotometric determination of Ponceau 4R in various beverage samples using Non-ionic surfactant PONPE 7.5

In the current study, a simple, cheap, and fast analytical procedure, termed ultrasonic-assisted cloud point microextraction (UA-CPME), combined with UV-VIS spectrophotometry, was developed for the pre-concentration and identification of Ponceau 4R in some beverage samples. Ponceau 4R was extracted from aqueous solution using polyethylene glycol mono-p-nonylphenyl ether (PONPE 7.5) as extraction solvent in the presence of Cu(II) at pH 6.0. Variables influencing the UA-CPME extraction efficiency such as pH, metal type and amount, temperature, ultrasonic effect, solvent type, non-ionic surfactant type and concentration were optimised in detail. Under optimum conditions, the analytical properties of the developed method were as follows: linear working range, 20-750 μg L^-1; limit of detection, 6.5 µg L^-1; and the pre-concentration factor, 200. The relative standard deviation (RSD%) obtained for 50 µg L^-1 (n = 5) of Ponceau 4R was 2.9%. The accuracy and precision of the method were evaluated by intra-day and inter-day studies. Finally, the developed method has been successfully applied to the separation and identification of Ponceau 4R in the selected samples and the recoveries ranged from 94.3 to 104.2.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

Evaluation of fish freshness using impedance spectroscopy based on the characteristic parameter of orthogonal direction difference

BACKGROUND

As a nondestructive testing technology, electrochemical impedance spectroscopy (EIS) has been applied to evaluate food quality because of its features of rapidity, low cost, nondestructiveness and portability. However, fish freshness evaluation based on existing EIS technology is affected by the differences of individual biological samples. In this study, the difference of electrical properties between two orthogonal directions was extracted to develop a new freshness indicator. A real part orthogonal direction difference parameter set (RODDS) was used to establish a prediction model for total volatile basic nitrogen (TVB-N).

RESULTS

Compared with the traditional parameter of EIS, coefficient of determination between RODDS and TVB-N increased from 0.55 to 0.71 for the calibration group, and root mean squared error between predicted and measured values of TVB-N decreased from 5.46 to 3.81 for the test group.

CONCLUSIONS

The results implied that RODDS could effectively offset individual differences in basic electrical properties and improve the TVB-N prediction accuracy in practical application scenarios with samples from multiple origins. The proposed method may provide a new idea for the development and improvement of EIS-based portable testing devices for fish and meat. © 2020 Society of Chemical Industry.

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

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 (r_pre = 0.9, SEP = 1.1 mg/100 g) and PLSR (r_pre = 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 (r_cv = 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).

Simultaneous Determination of Synthetic Food Dyes Using a Single Cartridge for Preconcentration and Separation Followed by Photometric Detection

A novel preconcentration/separation method for simultaneous sorption-spectrophotometric determination of anionic food dyes Sunset Yellow and Tartrazine is proposed. The method is based on preconcentration of the dyes using solid phase extraction on a cartridge filled with silica chemically modified with C16 groups from aqueous solution at pH 1 followed by elution with water/acetonitrile mixture containing 2 mmol·L−1 KH2PO4 adjusted to pH 3 with a step gradient of acetonitrile content. This elution allows quantitative separation of the dyes which makes their individual spectrophotometric determination possible. The detection limits for Tartrazine and Sunset Yellow are 0.15 and 0.11 μg·mL−1 and the linearity range is 2–20 μg·mL−1. The method is applied for analysis of beverages. The recovery of dyes is higher than 97% at the relative standard deviation not exceeding 10%.

Synthetic Food Dyes – Some Aspects Of Use And Methods Of Determination

Color is one of the key ingredients for increasing the appetizing of food, so food dyes have become firmly established in food production technologies. However, with the acquisition of toxicity data of synthetic food dyes (SFD), there were restrictions and standards for their content in food have emerged. Numerous papers published in recent years demonstrate the importance of the problem of the use and definition of SFD. The review contains over 180 literary references in the field of usage and methods of determination of synthetic food dyes, among them regulatory documents (regulations), official internet resources of international and Ukrainian organizations, review articles and original works. Varieties of chromatography, enzyme-linked immunoassay, optical and electrochemical methods are used to identify and determine SFD. Special attention was paid to voltammetry (VA) as a method that is cheaper than chromatography and completely satisfies selectivity, sensitivity, reliability requirements and is compatible with the concept of green analytical chemistry, as it doesn't need organic solvents. Moreover, single sweep voltammetry can be considered as a screening method with low limits of determination and rapid respons

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

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.

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Potentiality of PARAFAC approaches for simultaneous determination of N-acetylcysteine and acetaminophen based on the second-order data obtained from differential pulse voltammetry

N-acetylcysteine (N-AC) has widespread application such as pharmaceutical drug and nutritional supplement. Its adverse effects are rash, urticaria, and itchiness and large doses of N-AC could potentially cause damage to the heart and lungs. Therefore, in this work, a sensitive voltammetric sensor based on a carbon paste electrode modified with silica nano particles (i.e. Mobil Composition of Matter (No. 41) modified with Boron Trifluoride or BF₃@MCM-41) with a combination of 4,4'-dihydroxybiphenyl (DHB) (BF₃@MCM-41/DHB/CPE) was designed for determination of N-AC. The electrochemical oxidation of N-AC was examined using various techniques such as cyclic voltammetry (CV), chronoamperometry and differential pulse voltammetry (DPV). Under the optimum conditions, some parameters such as electron transfer coefficient (α) and heterogeneous rate constant (k_s) were estimated for N-AC. Due to the use of N-AC for the treatment of acetaminophen (AC) overdose, the application of modified electrode was investigated for the simultaneous determination of N-AC and AC in blood serum and tablet samples. Since, the signals of these species overlap and due to the presence of interfering species in blood samples, the simultaneous determination of mentioned species is difficult or impossible. To overcome this challenge, parallel factor analysis (PARAFAC) was used for the analysis of the complex matrices to obtain the spectral profile of each component and interference. To achieve this goal, electrochemical second-order data were generated using a simple change in pulse height of differential pulse voltammetry. The results of the presently proposed strategy for the real samples analysis are similar to those obtained with HPLC. Thus, the proposed method has acceptable performance for simultaneous determination of the two species in real samples.

Novel insights into the effect of folate–albumin binding on the transport of ascorbic acid as an anticancer agent: chemometric analysis based on combined spectroscopic and electrochemical studies

The electrochemical behavior of ascorbic acid and folic acid upon their interaction with albumin is investigated using electrochemical, chemometric and docking studies.