State-of-the-art nanosensors and kits for the detection of antibiotic residues in milk and dairy products.

Antibiotic resistance is increasingly seen as a future concern, but antibiotics are still commonly used in animals, leading to their accumulation in humans through the food chain and posing health risks. The development of nanomaterials has opened up possibilities for creating new sensing strategies to detect antibiotic residues, resulting in the emergence of innovative nanobiosensors with different benefits like rapidity, simplicity, accuracy, sensitivity, specificity, and precision. Therefore, this comprehensive review provides pertinent and current insights into nanomaterials-based electrochemical/optical sensors for the detection of antibitic residues (ANBr) across milk and dairy products. Here, we first discuss the commonly used ANBs in real products, the significance of ANBr, and also their binding/biological properties. Then, we provide an overview of the role of using different nanomaterials on the development of advanced nanobiosensors like fluorescence-based, colorimetric, surface-enhanced Raman scattering, surface plasmon resonance, and several important electrochemical nanobiosensors relying on different kinds of electrodes. The enhancement of ANB electrochemical behavior for detection is also outlined, along with a concise overview of the utilization of (bio)recognition units. Ultimately, this paper offers a perspective on the future concepts of this research field and commercialized nanomaterial-based sensors to help upgrade the sensing techniques for ANBr in dairy products.

Characterization of gluten-free cupcakes without sucrose based on defatted soybean flour and monk fruit.

This study aimed to produce and characterize a novel gluten-free cupcake for celiac and diabetes people. For this purpose, wheat flour and sugar in the cupcake formulation were fully replaced with soy flour and monk fruit. Also, samples containing wheat flour with sugar and monk fruit were prepared for comparison. The gluten-free cupcake without sucrose had a less specific volume and porosity index. To improve these properties, Cydonia oblonga (Cydonia Vulgaris) and Plantago ovata (Plantago genus) were used individually and in combination at concentrations of 1 and 2%. The cake containing no gum was made as a control as well. It was observed that addition of gums had positive effects on the specific volume, porosity index, and weight loss of cakes, but their incorporation increased their hardness. Based on the results, the fabrication of a novel and successful gluten-free cupcake replaced with soy flour, monk fruit, and gum is possible.

Cold Plasma-Assisted Extraction of Phytochemicals: A Review.

In recent years, there has been growing interest in bioactive plant compounds for their beneficial effects on health and for their potential in reducing the risk of developing certain diseases such as cancer, cardiovascular diseases, and neurodegenerative disorders. The extraction techniques conventionally used to obtain these phytocompounds, however, due to the use of toxic solvents and high temperatures, tend to be supplanted by innovative and unconventional techniques, in line with the demand for environmental and economic sustainability of new chemical processes. Among non-thermal technologies, cold plasma (CP), which has been successfully used for some years in the food industry as a treatment to improve food shelf life, seems to be one of the most promising solutions in green extraction processes. CP is characterized by its low environmental impact, low cost, and better extraction yield of phytochemicals, saving time, energy, and solvents compared with other classical extraction processes. In light of these considerations, this review aims to provide an overview of the potential and critical issues related to the use of CP in the extraction of phytochemicals, particularly polyphenols and essential oils. To review the current knowledge status and future insights of CP in this sector, a bibliometric study, providing quantitative information on the research activity based on the available published scientific literature, was carried out by the VOSviewer software (v. 1.6.18). Scientometric analysis has seen an increase in scientific studies over the past two years, underlining the growing interest of the scientific community in this natural substance extraction technique. The literature studies analyzed have shown that, in general, the use of CP was able to increase the yield of essential oil and polyphenols. Furthermore, the composition of the phytoextract obtained with CP would appear to be influenced by process parameters such as intensity (power and voltage), treatment time, and the working gas used. In general, the studies analyzed showed that the best yields in terms of total polyphenols and the antioxidant and antimicrobial properties of the phytoextracts were obtained using mild process conditions and nitrogen as the working gas. The use of CP as a non-conventional extraction technique is very recent, and further studies are needed to better understand the optimal process conditions to be adopted, and above all, in-depth studies are needed to better understand the mechanisms of plasma-plant matrix interaction to verify the possibility of any side reactions that could generate, in a highly oxidative environment, potentially hazardous substances, which would limit the exploitation of this technique at the industrial level.

A Novel Multi-Objective Binary Chimp Optimization Algorithm for Optimal Feature Selection: Application of Deep-Learning-Based Approaches for SAR Image Classification.

Removing redundant features and improving classifier performance necessitates the use of meta-heuristic and deep learning (DL) algorithms in feature selection and classification problems. With the maturity of DL tools, many data-driven polarimetric synthetic aperture radar (POLSAR) representation models have been suggested, most of which are based on deep convolutional neural networks (DCNNs). In this paper, we propose a hybrid approach of a new multi-objective binary chimp optimization algorithm (MOBChOA) and DCNN for optimal feature selection. We implemented the proposed method to classify POLSAR images from San Francisco, USA. To do so, we first performed the necessary preprocessing, including speckle reduction, radiometric calibration, and feature extraction. After that, we implemented the proposed MOBChOA for optimal feature selection. Finally, we trained the fully connected DCNN to classify the pixels into specific land-cover labels. We evaluated the performance of the proposed MOBChOA-DCNN in comparison with nine competitive methods. Our experimental results with the POLSAR image datasets show that the proposed architecture had a great performance for different important optimization parameters. The proposed MOBChOA-DCNN provided fewer features (27) and the highest overall accuracy. The overall accuracy values of MOBChOA-DCNN on the training and validation datasets were 96.89% and 96.13%, respectively, which were the best results. The overall accuracy of SVM was 89.30%, which was the worst result. The results of the proposed MOBChOA on two real-world benchmark problems were also better than the results with the other methods. Furthermore, it was shown that the MOBChOA-DCNN performed better than methods from previous studies.

A goal programming-based fuzzy best-worst method for the viable supplier selection problem: a case study.

Since the COVID-19 outbreak has led to drastic changes in the business environment, researchers attempt to introduce new approaches to improve the capability and flexibility of the industries. In this regard, recently, the concept of the viable supply chain, which tried to incorporate the leagile, resiliency, sustainability, and digitalization aspects into the post-pandemic supply chain, has been introduced by researchers. However, the literature shows that there is lack of study that investigated the viable supplier selection problem, as one of the crucial branches of viable supply chain management. Therefore, to cover this gap, the current work aims to develop a decision-making framework to investigated the viable supplier selection problem. In this regard, owing to the crucial role of the oxygen concentrator device during the COVID-19 outbreak, this research selects the mentioned product as a case study. After determining the indicators and alternatives of the research problem, a novel method named goal programming-based fuzzy best-worst method (GP-FBWM) is proposed to compute the indicators' weights. Then, the potential alternatives are prioritized employing the Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje method. In general, the main contributions and novelties of the present research are to incorporate the elements of the viability concepts in the supplier selection problem for the medical devices industry and to develop an efficient method GP-FBWM to measure the importance of the criteria. Then, the developed method is implemented and the obtained results are analyzed. Finally, managerial and theoretical implications are provided. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-022-07572-0.

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems.

The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.