This study conducted a systematic analysis and investigation of near-infrared (NIR) spectroscopy for assessing tomato fruits’ quality. This was combined with the integration of multivariate techniques for analysis to improve existing methods for nondestructive quality assessment of tomato fruits. Variations in tomato characteristics were revealed by selecting spectral indices, leading to changes in reflection spectra at different wavelengths. To achieve more accurate predictions, the use of vectors containing various spectral indices became necessary. This study found that predicting key tomato characteristics relies more on the informativeness and predictive ability of vectors derived from spectral indices than on the method used to reduce data volume. Regression models were developed to automate the prediction of six tomato characteristics based on the spectral data. The developed models for vitamin C content, titratable organic acids, and lycopene showed the highest predictive efficiency (over 90%) among the investigated features. These results highlight the potential of regression models to be integrated into automated systems for assessing the quality of tomato fruits, providing sufficiently accurate information to enhance quality control processes.

The design and implementation of a measuring device for the determination of pigment content in plant leaves is a topic of essential importance in plant biology, agriculture, and environmental research. The timely and sufficiently accurate determination of the content of these molecules provides valuable insight into the health, photosynthetic activity, and physiological state of plants. This paper presents the key aspects and results of the development and implementation of such a measuring device. It makes it possible to measure a larger number of pigments per type compared with the devices for commercial use that are currently known to us, and the accuracy of measurements depends mostly on the specific type of plant that is being tracked. The developed device presents a measurement accuracy ranging between 72% and 97% compared with a reference method and between 87% and 90% compared with a reference technique. Also, by using the device, a significant reduction in time and required resources can be achieved in measuring the content of pigments and nitrogen in plant leaves. This is a prerequisite for the more effective monitoring of the growth and health of plants, as well as optimizing the process of growing and caring for them. The work will be continued with the focus of the research aimed at generalizing the models for determining pigments and nitrogen in plants.

This study introduces a developed environmental quality assessment system, detailing its hardware, software, and comparative analysis against a publicly available system. While showing larger deviations in particulate matter and air humidity parameters, the proposed system demonstrates sufficient accuracy in other environmental characteristics. It establishes a standardized operating procedure and evaluates uncertainty and quality assurance measures, ensuring reliability in environmental measurements. The system offers comprehensive capabilities, measuring parameters like total volatile organic compounds, carbon dioxide, temperature, humidity, particulate matter, noise, nitrogen oxides, sulfur oxides, ozone, and carbon monoxide, with real-time monitoring functions for detecting environmental changes. Its user-friendly interfaces, scalability, and potential integration with existing systems enhance its versatility and cost-effectiveness across diverse settings. This study underscores the need for future research to enhance accuracy, reliability, and real-time operability and explore integration with smart city initiatives and environmental management systems. Overall, the proposed system represents a promising advancement in environmental monitoring technology, facilitating comprehensive environmental quality assessment and management.

Existing methods for tracking changes in the quality of dairy products are characterized by difficulty, time consuming, a large number of calculation procedures and resources, which makes them unsuitable for “on-line” monitoring. In the present work, feature vectors containing color components, spectral indices and physicochemical parameters of the products are used. Methods for selection of informative features based on consistently improving assessments have been applied. Models by principal components and latent variables are derived. It has been proven that the models are adequate and can be used to predict the day of storage of yellow cheese and white brined cheese. The advantage of the proposed data processing procedures, comparing them with those reported in the available literature, is that they require less computational resources, which makes them suitable for use in “on-line” monitoring of the condition of dairy products during storage.

Electromechanical systems operating under viscoelastic loads require precise modeling due to the highly nonlinear behavior of the load. An automatic bread machine is a practical example where dough represents a dynamic viscoelastic load sensitive to hydration. As found in this paper, increasing the water content leads to a decrease in the torque and the required mechanical power. An integrated approach combining MATLAB/Simulink and Simscape modeling, experimental measurements, and a PCA-based regression model is presented. The tests were conducted with three types of flour (type 500, type 1850, and rye–wheat) at hydrations of 52%, 58%, and 63% with over 6000 measurements recorded for each combination. The regression models achieve moderate predictability (R² = 0.64–0.96) model performance that varies across flour types. Increasing the dough hydration from 52% to 63% reduces the torque by approximately 22–46% across the tested flour types, while the angular velocity rises slightly (from about 147.9 to 151.9 rad/s). A descriptive decrease in energy consumption of up to around 6% was observed within the sampled batches with the system efficiency remaining within a narrow range around η ≈ 0.67. Within the studied levels (52–63%), the minimum load was observed at 58%. The proposed integrated model reliably describes the interaction between the electric motor, the mechanical gear, and the viscoelastic load, and it offers a basis for energy optimization and the implementation of low-cost sensor systems for intelligent control in the bread-making process.

The growing demand for functional bakery products necessitates research on the enrichment of wheat bread with pigweed (Amaranthus spp.) and purslane (Portulaca oleracea) flour. Although these plant-based raw materials offer nutritional and environmental benefits, their inclusion in wheat bread formulations poses challenges in the creation of formulations that may compromise the sensory and structural qualities of the final product. The main objective of this work is to systematically determine the optimal amounts of these alternative flour using multimodal bread characterization techniques that include physicochemical, organoleptic, geometric, and optical evaluations, supported by advanced data reduction techniques and regression models. A total of 70 features were analyzed and reduced to 22 for pigweed flour and 15 for purslane flour informative features. Predictive models (R² = 0.85 for pigweed flour, R² = 0.84 for purslane flour) were developed to optimize the inclusion of alternative flour, resulting in appropriate concentrations of 3.69% for pigweed flour and 7.13% for purslane flour. These formulations balance improved nutritional profiles with acceptable sensory and structural properties. The results obtained not only complement the potential of pigweed and purslane as sustainable functional raw materials but also demonstrate the efficacy of an automated, image-based approach to formulating recipes in food manufacturing.