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Effect of Packaging and Portioning on the Dynamics of Water–Fat Serum Release from Fresh Pasta Filata Soft Cheese
Quantitative determination of volatile compounds in a mixture using a single thermally modulated metal oxide semiconductor gas sensor and convolutional neural networks
Leveraging infrared spectroscopy for cocoa content prediction: A dual approach with Kohonen neural network and multivariate modeling
The Impact of Process Parameters on 1,3-Propanediol Production and 3-Hydroxypropionaldehyde Accumulation in Fed-Batch Fermentation of Glycerol with Citrobacter freundii AD119
Microbial production of 1,3-propanediol (1,3-PD) has attracted the interest of scientists for decades. Its product offers an environmentally friendly and sustainable alternative to fossil-based raw materials for chemical synthesis. Citrobacter freundii is one of the natural producers of 1,3-PD known for its ability to yield it in significant titers. An efficient bioprocess requires an in-depth understanding of the factors that influence the performance of its biocatalyst. The effects of pH, temperature, stirring rate, and substrate concentration on glycerol fermentation in fed-batch cultures of C. freundii AD119 were investigated in this study. In addition to monitoring the kinetics of substrate utilization and the formation of the final products, the concentration of 3-hydroxypropionaldehyde (3-HPA), an inhibitory intermediate of glycerol bioconversion, was analyzed. When the optimal working conditions were used (pH 7.0, temperature 30 °C, stirring rate of 80 rpm, and glycerol concentration below 15 g/L during the fed-batch phase), 53.44 g/L of 1,3-PD were obtained. When the process was performed at temperatures of 33 °C or higher or in acidic pH (6.5), an elevated concentration of 3-HPA was observed and the process halted prematurely.
Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor
Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.
Impact of Dried Garlic on the Kinetics of Bacterial Growth in Connection with Thiosulfinate and Total Phenolic Content
The health properties of garlic (Allium sativum L.) are attributed to thiosulfinates, flavonoids, phenols, and bioactive polysaccharides. These compounds, however, can degrade during processing methods. As hot air-drying is a commonly used preservation method due to its relatively simple operation, this study investigated the effects of garlic slices dried at various temperatures (50, 70, and 90 °C) on the growth kinetic parameters of model strain Escherichia coli ATCC 25922, the total thiosulfinate content (TTC), and the total phenolic content (TPC). Observations showed that the concentration of garlic extracts was a significant factor influencing the kinetics of bacterial growth, while the garlic drying temperature appeared to have no effect on E. coli activity. Analysis of TTS in fresh and dried garlic did not reveal statistically significant differences in their levels. However, hot air drying at 50 °C significantly reduced the TPC by nearly 25%, whereas drying garlic at higher temperatures (70 °C and 90 °C) did not lead to a significant loss in TPC compared to the raw samples. The determined growth kinetic parameters of the tested E. coli strain could serve as a basis for selecting optimal drying process conditions and extract concentrations when designing garlic products with enhanced antimicrobial properties.
Optimization and Modeling of Citrobacter freundii AD119 Growth and 1,3-Propanediol Production Using Two-Step Statistical Experimental Design and Artificial Neural Networks
1,3-propanediol (1,3-PD) has a wide range of industrial applications. The most studied natural producers capable of fermenting glycerol to 1,3-PD belong to the genera Klebsiella, Citrobacter, and Clostridium. In this study, the optimization of medium composition for the biosynthesis of 1,3-PD by Citrobacter freundii AD119 was performed using the one-factor-at-a-time method (OFAT) and a two-step statistical experimental design. Eleven mineral components were tested for their impact on the process using the Plackett–Burman design. MgSO4 and CoCl2 were found to have the most pronounced effect. Consequently, a central composite design was used to optimize the concentration of these mineral components. Besides minerals, carbon and nitrogen sources were also optimized. Partial glycerol substitution with other carbon sources was found not to improve the bioconversion process. Moreover, although yeast extract was found to be the best nitrogen source, it was possible to replace it in part with (NH4)2SO4 without a negative impact on 1,3-PD production. As a part of the optimization procedure, an artificial neural network model of the growth of C. freundii and 1,3-PD production was developed as a predictive tool supporting the design and control of the bioprocess under study.
Machine and Deep Learning in the Evaluation of Selected Qualitative Characteristics of Sweet Potatoes Obtained under Different Convective Drying Conditions
This paper discusses the use of various methods to distinguish between slices of sweet potato dried in different conditions. The drying conditions varied in terms of temperature, the values were: 60 °C, 70 °C, 80 °C, and 90 °C. Examination methods included instrumental texture analysis using a texturometer and digital texture analysis based on macroscopic images. Classification of acquired data involved the use of machine learning techniques using various types of artificial neural networks, such as convolutional neural networks (CNNs) and multi-layer perceptron (MLP). As a result, in the convective drying, changes in color darkening were found in products with the following temperature values: 60 °C (L = 83.41), 70 °C (L = 81.11), 80 °C (L = 79.02), and 90 °C (L = 75.53). The best-generated model achieved an overall classification efficiency of 77%. Sweet potato dried at 90 °C proved to be completely distinguishable from other classes, among which classification efficiency varied between 61–83% depending on the class. This means that image analysis using deep convolutional artificial neural networks is a valuable tool in the context of assessing the quality of convective-dried sweet potato slices.
Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression
The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and aw = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted aw, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R2 = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.
Predictive assessment of mycological state of bulk-stored barley using B-splines in conjunction with genetic algorithms
Postharvest grain preservation and storage can significantly affect the safety and nutritional value of cereal-based products. Negligence at this stage of the food processing chain can lead to mold development and mycotoxin accumulation, which pose considerable threats to the quality of harvested grain and, thus, to consumer health. Predictive models evaluating the risk associated with fungal activity constitute a promising solution for decision-making modules in advanced preservation management systems. In this study, an attempt was made to combine genetic algorithms and B-spline curves in order to develop a predictive model to assess the mycological state of malting barley grain stored at various temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). It was found that the B-spline curves consisting of four second-order polynomials were sufficient to approximate the datasets describing fungal growth in barley ecosystems stored under steady temperature and humidity conditions. Based on the designated structures of B-spline curves, a universal parameterized model covering the entire range of tested conditions was developed. In the model, the coordinates of the control points of B-spline curves were modulated by genetic algorithms using values of storage parameters (aw and T). A statistical assessment of model performance showed its high efficiency (R2 = 0.94, MAE = 0.21, RMSE = 0.28). As the proposed model is based on easily measurable on-line storage parameters, it could be used as an effective tool supporting modern systems of postharvest grain treatment.
Rapid Detection of Tea Adulteration Using FT-NIR Spectroscopy Combined with t-SNE Analysis
Tea is one of the most popular non-alcoholic beverages internationally, and it is not uncommon to find commercial tea preparations mixed with leaves and parts of other plants to increase profit and production volume, which constitutes fraud. The aim of this study was to perform Fourier transform-near-infrared spectroscopic characterization of leaves and pieces (petioles and stems) of three types of medicinal plants (Chamomile, Ginseng, and Quebra-pedras) used in the preparation of teas. Cluster analysis methods were used to evaluate the ability of Fourier transform-near-infrared to identify plant types, with t-SNE presenting the best discriminatory power. The deconvolution of the spectra showed that 15 vibration bands allow a good characterization of the samples, all with R² greater than 0.99.
Empirical Modeling of the Drying Kinetics of Red Beetroot (Beta vulgaris L.; Chenopodiaceae) with Peel, and Flour Stability in Laminated and Plastic Flexible Packaging
Despite the high global production of beetroot (Beta vulgaris L.), its peel is often discarded. Transforming beetroot into flour can reduce waste, improve food security, and decrease environmental pollution. However, large-scale feasibility depends on understanding drying kinetics and optimal storage conditions. This study aimed to investigate the effects of different temperatures in the convective drying of whole beetroot and evaluate the influence of laminated flexible and plastic packaging on flour stability over two months. Drying kinetics were analyzed using five models, with the Page and Logarithm models showing the best fit (R2 > 0.99). Def values (1.27 × 10−9 to 2.04 × 10−9 m2 s−1) increased with rising temperatures while drying time was reduced (from 820 to 400 min), indicating efficient diffusion. The activation energy was 29.34 KJ mol−1, comparable to other plant matrices. Drying reduced moisture and increased ash concentration in the flour. The flour showed a good water adsorption capacity and low cohesiveness, making it marketable. Laminated packaging was more effective in controlling physicochemical parameters, reducing hygroscopicity, and maintaining quality over 60 days. In summary, the Page model can predict beetroot drying kinetics effectively, and laminated packaging can control flour stability.
A Review of Methods and Applications for a Heart Rate Variability Analysis
Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.
Integration of PCA, HCA, and KNN to Evaluate Packaging and Storage Conditions for Red Bell Peppers
ABSTRACTPeppers (Capsicum annuum L.) are a vegetable that is widely cultivated in various regions of the world. Despite the economic importance of peppers, their commercialization is hindered by their limited postharvest durability, primarily due to moisture loss during storage. This study evaluated the effectiveness of different packaging methods and storage conditions in preserving the physicochemical and morphological quality of peppers during 21 days. Six treatments were tested, combining two types of packaging (thermo‐sealable and macro‐perforated) with two storage conditions (8°C/95% RH and 25°C/60% RH), plus an unpackaged control. Variables assessed included color, soluble solids, pH, pigments, dimensions, and mass loss. Data were analyzed using principal component analysis (PCA), hierarchical cluster analysis (HCA), and Kohonen neural networks (KNN). The first three principal components (PCs) explained 67.2% of total variance (PC1—40.88%, PC2—15.11%, PC3—11.17%). PC1 was strongly associated with mass and size losses (up to 73%), whereas PC2 and PC3 explained 77.4% of h* and 84.9% of C*, respectively. HCA and KNN revealed similar groupings. Samples stored at 8°C clustered together regardless of packaging, indicating minimal quality loss. At 25°C, unpackaged and macro‐perforated samples showed similar degradation. Thermo‐sealable packaging at 25°C formed a distinct cluster, indicating improved protection. This treatment also showed reduced quality losses, though not as effective as refrigeration. The agreement among PCA, HCA, and KNN confirms the reliability of findings. These results highlight the value of combining conservation strategies with multivariate tools to guide efficient, sustainable postharvest practices and extend shelf life in the pepper supply chain.Practical ApplicationThis study proposes a solution for the horticultural industry by combining heat‐shrink packaging and refrigeration for pepper preservation. This method significantly reduces physical and biochemical losses, extends shelf life, and maintains quality. It has the potential to transform the logistics of production and distribution, delivering fresh, high‐quality peppers. The use of advanced techniques like PCA and neural networks enables more informed and efficient decision‐making, allowing for customized preservation strategies. This approach meets the growing demand for fresh food, offering a sustainable, cost‐effective alternative for postharvest preservation, and may provide a competitive advantage in the global market.
Application of multivariate analysis and Kohonen Neural Network to discriminate bioactive components and chemical composition of kosovan honey
Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry
Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.
Quantification of volatile compounds in mixtures using a single thermally modulated MOS gas sensor with PCA-ANN data processing
Quantitative Description of Isomorphism in the Series of Simple Compounds
The introduction of the notion of energy change resulting from the ion exchange in apatites leads to the question: how can some simple isomorphic series be described using the mentioned idea? We concentrated on the simple isomorphic series of compounds: apatite, bioapatite, calcite, aragonite, celestine, K-, Zn- and Cu-Tutton’s salts. It was demonstrated in all the series, except Tutton’s salts, that the change in energy and the change in the crystal cell volume are, in a simple way, dependent on the change in the ionic radii of the introduced ions. The linear relationships between the variations in energy and in the universal crystallographic dimension d were derived from the earlier equations and proven based on available data. In many cases, except the Tutton’s salts, linear dependence was discovered between the change in energy and the sinus of universal angle Θ, corresponding to the change in momentum transfer. In the same cases, linear dependencies were observed between the energy changes and the changes in the volumes of crystallographic cells, and mutually between changes in the crystallographic cell volume V, crystallographic dimension d, and diffraction angle Θ.