Search Results (4,257)

Apples and tomatoes are among the most consumed products all over the world, as well as the natural juices prepared from each of them. The large quantities of resulting by-products should be reused in various directions within the circular economy. In this study, apple and tomato pomaces were tested as potential biofertilizers for agricultural crops. To this end, aqueous extracts of apple pomace and tomato pomace were prepared in two concentrations (0.05% and 0.5%) and used to treat wheat caryopses and sprouts. The following were evaluated: mitotic index, genotoxic index, caryopses germination rate, and wheat sprout growth. The biotic response of wheat to treatments with the apple and tomato pomace extracts consisted of reduced mitotic activity, i.e., cytotoxicity, and the formation of genetic abnormalities, i.e., genotoxicity. The cytotoxicity and the genotoxicity were reflected at the macro level in phytotoxic effects, manifested by a reduction in the germination rate of caryopses and a decrease in the length of wheat roots and shoots. Physiological parameters were positively correlated with the mitotic index and negatively correlated with the genotoxic index. The obtained results point us not to recommend the use of unprocessed apple and tomato pomaces as biofertilizers, but, on the contrary, as bioherbicides. Full article

The aim of the present study was to test the prediction ability of three different supervised chemometric algorithms, such as linear discriminant analysis (LDA), k-nearest Neighbor (k-NN) and artificial neural networks (ANNs), for fruit juice classification and differentiation, based on isotopic and multielemental content. To accomplish this, a large experimental dataset was analyzed using inductively coupled plasma mass spectrometry (ICP-MS) together with isotope ratio mass spectrometry (IRMS), and a low data fusion approach was applied. Three classifications were tested, namely the following: (i) fruit differentiation of different juice types; (ii) apple and orange juice differentiation; and (iii) distinguishing between processed versus directly pressed apple juices. The results demonstrated that ANNs can offer the most accurate results, compared with LDA and k-NN, for all three cases of classification, highlighting once again the advantages of deep learning models for modeling complex data. The work revealed the higher potential of advanced chemometric methods for accurate classification of fruit juices, compared with traditional approaches. This approach could represent a realistic tool for ensuring the juice’s quality and safety, along with complying with regulations and combating fraud. Full article

Background/Objectives: Neuropsychological assessments are valuable tools for evaluating the cognitive performance of older adults. Limitations associated with these in-person paper-and-pencil tests have inspired efforts to develop digital assessments, which would expand access to cognitive screening. Digital tests, however, often lack validity relative to gold-standard paper-and-pencil versions that have been robustly validated. Speech-to-text (STT) technology has the potential to improve the validity of digital tests through its ability to capture verbal responses, yet the effect of its performance on standardized scores used for cognitive characterization is unknown. Methods: The present study evaluated the accuracy of Apple’s STT engine relative to ground-truth transcriptions (RQ1), as well as the effect of the engine’s transcription errors on resulting standardized scores (RQ2). Our study analyzed data from 223 older adults who completed a digital assessment on an iPad that used STT to transcribe and score task responses. These automated transcriptions were then compared against ground-truth transcriptions that were human-corrected via external recordings. Results: Results showed differences between STT and ground-truth transcriptions (RQ1). Nevertheless, these differences were not large enough to practically affect standardized measures of cognitive performance (RQ2). Conclusions: Our results demonstrate the practical utility of Apple’s STT engine for digital neuropsychological assessment and cognitive characterization. These findings support the possibility that speech-to-text, with its ability to capture and process verbal responses, will be a viable tool for increasing the validity of digital neuropsychological assessments. Full article

Ascorbate oxidases (AAOs) are key regulators of extracellular redox homeostasis and plant stress responses, but their roles in grapevine defense remain unclear. Here, we performed a genome-wide analysis and characterization of the AAO gene family in grapevine Vitis amurensis, identifying 10 VaAAO genes that are unevenly distributed across six chromosomes, with notable clustering on chromosome 7. Promoter analysis revealed multiple phytohormone- and stress-responsive cis-elements (e.g., ARE, STRE, and TCA-element) and transcription factor binding sites (e.g., MYC/MYB, and WRKY), suggesting involvement in redox- and stress-related signaling pathways. Analysis of previously published transcriptomic data under Botrytis cinerea infection identified VaAAO7 as a key pathogen-responsive gene. VaAAO7 was rapidly induced by H₂O₂, and its transient ectopic overexpression in susceptible V. vinifera ‘Red Globe’ leaves significantly reduced lesion development. Together, these results demonstrate that VaAAO7 functions as a positive regulator of B. cinerea resistance and highlight its potential for genetic engineering to enhance systemic defense and develop disease-resistant grapevine cultivars. Full article

The aim of this study was to evaluate and compare dried apple (A), chokeberry (C), grape (G), raspberry (R), and red currant (RC) pomace as potential additives to food, beverages, and cosmetics. Their physicochemical properties and nutritional composition were examined. The fruit pomace was characterised by significant differences in acidity ranging 1.41 (G) to 7.96 g·100 g⁻¹_d.w. (R), water holding capacity (2.36–4.25 g·g⁻¹, C-A), and oil holding capacity (1.86–2.41 g·g⁻¹, C-G). The colour parameters of the pomace differed significantly. The highest lightness L* was recorded for the apple pomace (66.29). Samples RC and R were characterised by the highest redness (32.99; 26.76), while A, G, and R showed high b* values, amounting to 28.54, 22.84, and 20.40 (yellowness), respectively. The highest protein (13.01%), fat (6.82%), and fibre (67.38%) contents were recorded in the redcurrant pomace. The mineral analysis revealed high potassium, phosphorus, and calcium contents in all pomace samples, with the grape and redcurrant pomace containing the highest mineral content. These results highlight the potential of fruit pomace as a sustainable, nutritionally enriching ingredient, primarily for food products, and the potential to reduce food waste. Full article

Documenting cultural heritage sites through 3D reconstruction is crucial and can be accomplished using various geomatic techniques, such as Terrestrial Laser Scanners (TLS), Close-Range Photogrammetry (CRP), and UAV photogrammetry. Each method comes with different levels of complexity, accuracy, field times, post-processing requirements, and costs, making them suitable for different types of restitutions. Recently, research has increasingly focused on user-friendly and faster techniques, while also considering the cost–benefit balance between accuracy, times, and costs. In this scenario, photogrammetry using images captured with 360-degree cameras and LiDAR sensors integrated into Apple devices have gained significant popularity. This study proposes the application of various techniques for the geometric reconstruction of a complex cultural heritage site, the Church of Saint Andrew in Orani, Sardinia. Datasets acquired from different geomatic techniques have been evaluated in terms of quality and usability for documenting various aspects of the site. The TLS provided an accurate model of both the interior and exterior of the church, serving as the ground truth for the validation process. UAV photogrammetry offered a broader view of the exterior, while panoramic photogrammetry from 360° camera was applied to survey the bell tower’s interior. Additionally, CRP and Apple LiDAR were compared in the context of a detailed survey. Full article

Accurate tree health monitoring in orchards is essential for optimal orchard production. This study investigates the efficacy of a deep learning-based object detection single-step method for detecting tree health on multispectral UAV imagery. A modified Mask R-CNN framework is employed with four different backbones—ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer—on three image combinations: (1) RGB images, (2) 5-band multispectral images comprising RGB, Red-Edge, and Near-Infrared (NIR) bands, and (3) three principal components (3PCs) computed from the reflectance of the five spectral bands and twelve associated vegetation index images. The Mask R-CNN, having a ResNeXt-101 backbone, and applied to the 5-band multispectral images, consistently outperforms other configurations, with an F1-score of 85.68% and a mean Intersection over Union (mIoU) of 92.85%. To address the class imbalance, class weighting and focal loss were integrated into the model, yielding improvements in the detection of the minority class, i.e., the unhealthy trees. The tested method has the advantage of allowing the detection of unhealthy trees over UAV images using a single-step approach. Full article

This study reports the development and characterization of novel active edible films based on apple pectin and honey (80:20, w/w), incorporating raw propolis powder at 0.1%, 0.2%, and 0.3% (w/w, relative to honey) as a natural source of bioactive compounds for sustainable packaging of soluble coffee and matcha powders. The study aims to provide sustainable and functional packaging solutions capable of maintaining the stability and quality of these powdered beverages. The effects of honey and propolis incorporation on the physicochemical, mechanical, optical, and microbiological properties of the films were systematically evaluated. Propolis addition resulted in decreased tensile strength, elastic modulus, and elongation at break, but did not significantly alter the thermal stability of the films, as evidenced by differential scanning calorimetry and thermogravimetric analysis. Increasing propolis concentrations led to higher total phenolic content and significantly improved antioxidant activity, with the 0.3% formulation exhibiting the most pronounced effect. Application tests demonstrated that the honey–propolis-enriched pectin films effectively preserved the sensory attributes and physicochemical quality of soluble coffee and matcha powders. Overall, these results highlight the potential of pectin–honey–propolis films as bioactive carriers and functional materials for active packaging of powdered beverages. Full article

Insufficient fruit intake is a major contributor to the development of non-communicable diseases, as the global average of daily fruit consumption remains far below the recommended levels. Apples are among the most widely consumed fruits worldwide, making them an ideal target for nutritional enhancement. Enhancing the content of health-promoting compounds within apples offers a practical way to increase bioactive intake without requiring major dietary changes. This review evaluates which of the 41 biologically active compounds considered in this article can reach physiologically relevant intake levels at the current average daily consumption of cultivated and wild apples. Comparative analysis shows that wild apples consistently contain higher concentrations of phenolic compounds and organic acids than cultivated varieties, in some cases by more than tenfold. At the average daily fruit intake of 121.8 g, wild species provide effective doses of epicatechins, anthocyanins, chlorogenic acid, and malic acid. In contrast, cultivated apples reach this level only for chlorogenic acid. Notably, less than 50 g of wild apple is sufficient to supply physiologically relevant amounts of several polyphenols. These findings highlight the potential of wild apple species as donors of bioactive compounds and provide a framework for breeding future apple cultivars that combine consumer appeal with enhanced health benefits. Full article

The custard apple (CA) is a noble fruit in tropical regions worldwide. It has attracted a growing interest due to its organoleptic properties and nutritional value. With the expansion of international trade, both its cultivation and consumption have grown significantly in recent years. Previous researchers have sporadically investigated its nutritional composition and health benefits; however, existing information on its processing and utilization is highly fragmented and lacks a comprehensive overview of its constituents, biological activities, and potential applications. This review is a detailed summary of the nutritional and bioactive properties, safety evaluations, and potential applications of CA. Following PRISMA guidelines, peer-reviewed studies published between 2000 and 2025 were systematically searched in PubMed, Scopus, ResearchGate, and Web of Science. Inclusion criteria comprised studies reporting on nutritional composition, phytochemicals, bioactivities, health promotion, and applications of CA. In addition to primary nutrients like carbohydrates, protein, fatty acids, vitamins, and minerals, CA also contains a multitude of bioactive compounds, mainly including phenols, flavonoids, terpenoids, acetogenins, and alkaloids, which are attributed to a range of health benefits, such as antioxidant, anti-microbial, anti-tumor, blood sugar regulation, and cognitive function improvement. However, more clinical and toxicological profiles remain underexplored, and future research should focus on standardized extraction, safety evaluation, and translational applications. Additionally, the challenges and future perspectives in industrial applications are discussed, which are expected to offer comprehensive information for the utilization of CA. Full article

Significant depletion of natural resources, coupled with increased environmental pollution resulting from the constant evolution of global industrialization, poses a considerable problem. Therefore, it is unsurprising that sustainable “green” chemistry and technology are gathering the worldwide scientific community, whose common goal is to find applicable solutions for the abovementioned problems. This paper combined the ultrasonic extraction method (a form of “green” technology) with natural deep eutectic solvents (NADESs, a type of “green” solvent) for the production of extracts from an industrial by-product (discarded waste wild apple dust). Waste wild apple dust was pretreated with different NADESs in order to explore the pretreatment benefits regarding ultrasonic extraction of bioactive compounds. Among all solvents used, aqueous propylene glycol was chosen as the best system, which, combined with Reline NADES pretreatment, provided the highest TPC and TFC values, together with the best antioxidant activities. UHPLC-DAD-MS analyses of extracts revealed the presence of natural organic acids, quercetin and kaempferol derivatives, tannins, and flavones. Following this procedure, valorization of agro-industrial apple herbal waste resulted in obtaining extracts with high potential for utilization in different industrial branches (food and pharmaceutical industries), contributing to both cleaner production and reduced environmental impact. Full article

Apple pomace, a by-product of juice production, is a rich source of dietary fiber and bioactive compounds, making it a promising functional ingredient for bakery applications. This study evaluated the physicochemical and sensory properties of shortbread cookies enriched with apple pomace dried under different conditions, while also analyzing the drying process, focusing on drying kinetics and powder characteristics. Pomace dried by either contact drying or freeze-drying was ground and used to replace 20% of wheat flour in the cookie formulation. Drying kinetics were best described by the modified Page model, and freeze-dried pomace showed higher grindability than contact-dried samples. Cookies enriched with pomace exhibited similar overall composition, with differences mainly observed in fiber content (9.82–11.75%). Those containing freeze-dried pomace were lighter, with reduced red and increased yellow tones, and were firmer, requiring approximately 30% higher cutting force. Despite differences in physical properties, enriched cookies were consistently rated higher in overall acceptability than the controls. The results indicate that the drying method and temperature influence the physicochemical properties of apple by-product and the resulting cookies, while having mainly minor effects on sensory acceptance, confirming the potential of apple pomace as a functional ingredient in bakery products. Full article

Purpose: To investigate the efficacy of a novel test for diagnosing colour vision deficiencies using reflexive eye movements measured using an unmodified tablet. Methods: This study followed a cross-sectional design, where thirty-three participants aged between 17 and 65 years were recruited. The participant group comprised 23 controls, 8 deuteranopes, and 2 protanopes. An anomaloscope was employed to determine the colour vision status of these participants. The study methodology involved using an Apple iPad Pro’s built-in eye-tracking capabilities to record eye movements in response to coloured patterns drifting on the screen. Through an automated analysis of these movements, the researchers estimated individuals’ red–green equiluminant point and their equivalent luminance contrast. Results: Estimates of the red–green equiluminant point and the equivalent luminance contrast were used to classify participants’ colour vision status with a sensitivity rate of 90.0% and a specificity rate of 91.30%. Conclusions: The novel colour vision test administered using an unmodified tablet was found to be effective in diagnosing colour vision deficiencies and has the potential to be a practical and cost-effective alternative to traditional methods. Translation Relevance: The test’s objectivity, its straightforward implementation on a standard tablet, and its minimal requirement for patient cooperation, all contribute to the wider accessibility of colour vision diagnosis. This is particularly advantageous for demographics like children who might be challenging to engage, but for whom early detection is of paramount importance. Full article

Hyperspectral imaging (HSI) has emerged as a powerful tool for non-destructive phenotyping, yet fruit crop applications remain underexplored. We propose a methodological framework to enhance the spectral characterization of apple fruits by identifying robust vegetation indices (VIs) and interpretable endmembers. We screened 284 Vis, which were evaluated using four feature selection algorithms (Boruta, MI+Lasso, RFE, and ensemble voting), generalizing across red, yellow, green, and purple apple cultivars. An ensemble criterion (≥2 algorithms) yielded 50 selected VIs from the NDSI/DSI/RSI families, preserving > 95% classification accuracy and capturing cultivar-specific variation. Pigment-sensitive wavelength bands were identified via PLS-DA VIP scores and one-vs-rest ANOVA. Using these bands, we formulated a new normalized-difference, ratio, and difference spectral indices tailored to cultivar-specific pigmentation. Several indices achieved >89% classification accuracy and showed patterns consistent with those of anthocyanin, carotenoid, and chlorophyll. A two-stage spectral unmixing pipeline (K-Means → N-FINDR) achieved the lowest reconstruction RMSE (0.043%). This multi-level strategy provides a scalable, interpretable framework for enhancing phenotypic resolution in apple hyperspectral data, contributing to fruit index development and generalized spectral analysis methods for horticultural applications. Full article

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article