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.

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.

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.

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.

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 a_w = 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 a_w, 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.

Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed

This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.

Changes in contents of phenolic compounds (sinapic acid derivatives) in seeds of Brassica napus L.under adverse storage conditions

BACKGROUND

The quality of rapeseed oil depends to a considerable degree on raw material quality. Negli- gence in maintaining the appropriate conditions during long-term rapeseed storage (excessively high humid- ity and temperature) may contribute to a deterioration of seed quality, as a result of microbial growth and changes in native antioxidant contents. The aim of this study was to investigate the effect of inappropriate storage conditions on changes in sinapic acid derivative content, which is the main phenolic compound in rapeseeds.

METHODS

The material used for tests was canola cv. PR 46 W14. Seeds with a 13.5% moisture content were stored for 21 days in a thermo-hygrostat chamber, ensuring rapeseed storage under constant humidity and temperature conditions. In this study, the level of mould fungi was analysed using the plate method, while those of sinapic acid derivatives were determined using the HPLC-DAD method.

RESULTS

Intensive growth of mould fungi in the rapeseeds was observed after 6 days of storage. Changes were recorded in sinapic acid derivative contents, which are the main phenolic compounds in rapeseed. The level of phenolic compounds found in the bound form (sinapin; sinapic acid methyl ester; 1,2-disinapoyl- dihexoside; 1,2-disinapoyl-hexoside and 1,2,2’-trisinapoyl-dihexoside) decreased. At the same time, an in- crease was recorded in trans-sinapic acid content (by 63%).

CONCLUSIONS

Both qualitative and quantitative changes in phenolic compounds may be connected with the development of fungal microflora in stored rapeseeds. Only adequate storage conditions for the oil raw mate- rial, such as rapeseeds, may ensure good quality in the final product, in this case, rapeseed oil.

Kinetics of mould growth in the stored barley ecosystem contaminated with Aspergillus westerdijkiae, Penicillium viridicatum and Fusarium poae at 23-30 °C

BACKGROUND

Owing to the lack of a rapid method for determining fungi on cereals, the best way to enhance the safety and nutritive value of stored grain is to develop prognostic tools based on the relationship between easily measurable online parameters, e.g. water activity (a(w)) and temperature (t) of grain, and fungal growth. This study examined the effect of unfavourable temperature (23 and 30 °C) and humidity (0.80-0.94 a(w)) storage conditions on mould growth in the stored barley ecosystem with its adverse microbiological state provided by contamination with Aspergillus westerdijkiae, Penicillium viridicatum and Fusarium poae.

RESULTS

Among the applied storage parameters, a(w) turned out to be the main factor affecting mould development. The longest lag phase and period of fungal activation were observed for grain with 0.80 a(w), which was not threatened with fungal development for at least 30 days. However, in grain with 0.92 and 0.94 a(w), fungal activation occurred within 24-48 h.

CONCLUSION

The obtained data and the identification of critical points in mould growth may be used to develop a control system for the postharvest preservation of barley based on a(w) and temperature of grain, which are easy to measure in practice.

The Effect of Temperature and Moisture Content of Stored Rapeseed on the Phytosterol Degradation Rate

The effect of temperature (25 or 35 °C) and moisture content (10, 12.5, 15.5 %) on rapeseed phytosterol degradation was examined for 18 days. Statistical analysis showed that temperature, moisture and time of storage have a significant effect on phytosterol degradation. After 18 days of seed storage at a temperature of 25 and 30 °C losses of these compounds amounted to 11 and 13 % in seeds with moisture contents of 10, 12 and 16 % in seeds with a moisture content of 12.5 %, while they were 24 and 58 % in seeds with a moisture content of 15.5 %. Among all the identified sterols the greatest degradation rate was observed for stigmasterol and brassicasterol. Losses of stigmasterol and brassicasterol during storage of seeds with a 12.5 % moisture content at a temperature of 30 °C were 17 and 28 %, respectively, while in seeds with a moisture content of 15.5 % these losses increased to 73 and 63 %.