The quality assessment and grading of agricultural products is one of the post-harvest activities that has received considerable attention due to the growing demand for healthy and better-quality products. Recently, various non-destructive methods have been used to evaluate the quality of agricultural products, which are very desirable and faster and more economical than destructive methods. Optical methods are one of the most important non-destructive methods that use the high speed of light detection and computer data processing and are able to evaluate the quality and classification of products with high accuracy. Among the optical methods, visible–near-infrared (Vis/NIR) spectroscopy is considered one of the most accurate methods. In this research, Vis/NIR spectroscopy technology was used in the spectral range of 350–1150 nm for non-destructive detection of some quality parameters including pH, TA, SSC, and TP of two varieties of Red Delicious and Golden Delicious apples. Various pre-processing models were developed to predict the parameters, which brought the desired results with high accuracy so that pH prediction results were for yellow apples (RMSEC = 0.009, r_c = 0.991, SDR = 2.51) and for red apples (RMSEC = 0.005, r_c = 0.998, SDR = 2.56). The results for TA were also (RMSEC = 0.003, r_c = 0.996, SDR = 2.51) for red apples and (RMSEC = 0.001, r_c = 0.998, SDR = 2.81) for yellow apples. The results regarding SSC were for red apples (RMSEC = 0.209, rc = 0.990 and SDR = 2.82) and for yellow apples (RMSEC = 0.054, SDR = 2.67 and rc = 0.999). In addition, regarding TP, the results were for red apples (RMSEC = 0.2, r_c = 0.989, SDR = 2.05) and for yellow apples (RMSEC = 1.457, r_c = 0.998, SDR = 1.61). The obtained results indicate the detection of the mentioned parameters with high accuracy by visible/infrared spectroscopic technology.

Polyphenol oxidase (PPO) is the primary biochemical driver of browning and the subsequent decline of market quality in harvested fruit. In this work, a fully non-invasive analytical framework was built using Visible/Near-Infrared (VIS/NIR) spectroscopy coupled with chemometric modeling in order to estimate PPO activity in two commercially relevant plum cultivars (Khormaei and Khoni). A comprehensive comparative study was conducted utilizing multiple machine learning and linear regression techniques, including Support Vector Regression (SVR), Decision Tree (DT), and Partial Least Squares Regression (PLSR). After acquiring the full VIS/NIR spectra, a suite of metaheuristic feature selection strategies was applied to compress the spectral space to roughly 10–15 highly informative wavelengths. SVR, DT, and PLSR models were then trained and benchmarked using (a) the complete spectral domain and (b) the reduced wavelength subsets. The results consistently demonstrated that non-linear models (DT and SVR) significantly outperformed the linear PLSR method, confirming the inherent complexity and non-linearity of the relationship between the spectra and PPO activity. Across all tests, DT consistently produced the strongest generalization. Under full spectra inputs, DT reached RPD values of 4.93 for Khormaei and 5.41 for Khoni. Even more importantly, the wavelength-reduced configuration further enhanced performance while substantially lowering computational cost, yielding RPDs of 3.32 (Khormaei) and 5.69 (Khoni). The results show that VIS/NIR combined with optimized key-wavelength DT modeling provides a robust, fast, and field-realistic route for quantifying PPO activity in plums without physical destruction of the product.

Destructive methods, though traditionally used to evaluate fruit safety, frequently do not deliver complete and detailed information. Non-destructive methods, especially spectroscopy, provide an effective solution for fast, efficient, and non-invasive assessments of quality and safety. This study utilized visible and near-infrared (Vis-NIR) spectroscopy to quantify the nitrate content in three cultivars of bell pepper—orange, yellow, and red—across a spectral range spanning 350 to 1150 nanometers. The nitrate content was assessed destructively, and spectral data were examined through partial least squares regression (PLSR). Model efficacy was measured using the root mean square error (RMSE) and coefficient of determination (R²). The R² values, indicative of the model’s predictive efficacy, were determined to be 0.77, 0.85, and 0.81 for the yellow, red, and orange types, respectively. To optimize wavelength selection and improve model performance, a hybrid approach was utilized, integrating a support vector machine (SVM) with four meta-heuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), imperialistic competitive algorithm (ICA), and ant colony optimization (ACO). The SVM-PSO approach proved to be the most efficient in pinpointing 15 key wavelengths. Following this, three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural network (ANN)—were utilized with the identified wavelengths. Among these, ANN represented the best performance, achieving validation R² values of 0.99, 0.97, and 0.92 for the yellow, red, and orange varieties, respectively. Compared to traditional PLSR and MLR models, which reached validation R² values up to 0.93, the ANN model demonstrated a significant improvement in prediction accuracy. This quantitative improvement highlights the advantage of combining hybrid meta-heuristic wavelength selection with ANN modeling. The results underscore the promise of visible/near-infrared (Vis/NIR) spectroscopy, integrated with sophisticated modeling approaches, as an effective non-invasive method for estimating nitrate concentrations in bell peppers. This technique represents a significant advancement in supporting food safety measures and quality assurance processes.

Este artículo presenta el desarrollo de una aplicación para dispositivos Android, diseñada para asistir a personas con trastornos de ansiedad derivados de diversas fobias mediante un enfoque de Terapia Emocional Asistida especial mente para pacientes con Trastorno del espectro Autista (TEA). En su versión inicial, la aplicación se centra específicamente en la fobia social, aunque está concebida con la posibilidad de ampliarse en el futuro para abordar otros tipos de fobias. La aplicación utiliza tecnologías de Realidad Virtual (RV) y Realidad Aumentada (RA), implementadas mediante gafas RV, para ofrecer escenarios terapéuticos interactivos. Se desarrollaron 4 escenarios denominados como: “Habitación segura”, “Fuera de la casa”, “Autobús” y “Calle con público”. La investigación se desarrolló en el C.M.F. ISSSTE, donde se realizaron pruebas con pacientes bajo la supervisión de un especialista. Se evaluó el impacto de la aplicación en el tratamiento de la fobia social y se ajustaron las intervenciones de acuerdo a necesidades individuales de cada paciente. Los resultados evidencian el potencial de estas tecnologías para mejorar la gestión emocional y reducir la ansiedad asociada a las fobias.