Drying is one of the most effective preservation methods for extending the shelf-life of perishable foods. Microbial safety of the final products has not been a concern until outbreaks reported over the past decade in products contaminated with bacterial pathogens, in particular Salmonella. There is now an urgent need to understand the influence of process conditions on the thermal inactivation of pathogens in various drying operations. This study aimed to develop a predictive model for Salmonella inactivation in diced apples during hot air drying and in high-humidity heating in closed environment. Fresh-cut apple cubes (6 mm) were inoculated with a cocktail of Salmonella enterica strains (Enteritidis PT30, Montevideo 488275, and Agona 447967). The samples were placed in a preheated sample treatment box within a convection oven under three conditions: 1) open air drying, the sample box was open in the oven pre-set at 90°C (Drying-90); 2) closed-box heating, the oven was also set at 90°C (Preheating-90); and 3) closed-box heating, the oven was at 70°C (Pre-heating-70). For open-air drying, hot air at the oven temperature was circulated through the samples, facilitating the heating and drying. In closed box heating, the sample box maintained a high-humidity environment, retaining moisture evaporated from the apple samples and allowing heat conduction through the box walls. In each test, air temperature, relative humidity (RH), and sample temperatures were monitored, and Salmonella survival was measured at multiple time points. After 10 minutes of heating, the air RH levels in the closed-box configurations were 66% (Preheating-90) and 74% (Preheating-70), much higher than in the Drying-90 condition (30%). Results indicated that in-box heating achieved a 5-log reduction Salmonella after 8.5 min at 90°C, compared to 28.7 min in open air drying at the same temperature. It took 14 min to achieve 5-log reduction at 70°C in in-box heating. A mathematical model was developed to estimate sample surface RH based on sample temper-ature and ambient absolute humidity, with further adaptation to predict microbial inactivation over time using surface temperature and RH profiles. This model accurately predicted Salmonella inactivation across all treatments (RMSE = 0.92 log CFU/g, R² = 0.86), with thermal death param-eters comparable to literature values from isothermal studies. This study highlights the im-portance of air humidity in enhancing bacterial inactivation during drying and suggests that a preheating step under high-humidity conditions may serve as an effective control measure, en-suring microbial safety before conventional drying. Additionally, this work demonstrates the potential of a nonlinear mathematical model to predict microbial inactivation under complex drying conditions with fluctuating temperature and humidity.

The aim of this study was to investigate and effect of application of different conditions of freeze-drying on the kinetics of process and the sorption properties of dried apples. Slices of apple were frozen and subjected to freezing-drying process with different combination of a shelf temperature (-20, 10, 20, 30°C) and pressure (37, 63, 103, 165 Pa). During the freeze-drying, the temperature in the centre of material was recorded. The moisture content in dried material and changes of water content dried apples stored at humidity of 75.3% were obtained. The Midilli et al. model was used to describe drying kinetics of the freeze-drying with good fit. Drying time decreased from 660 (variant with a constant shelf temperature of 30°C, pressure 63 Pa) to 1305 minutes (variant with temperatures −20: 10: 20: 30°C, pressure 63 Pa). For this reason, the most favourable experimental parameters were: temperature of 30°C and pressure of 63 Pa. However, application of these parameters caused higher absorption of water vapour during storage. Therefore, the selection of freeze-drying parameters should also be related to the expected properties of the final product.

This research was conducted to understand changes of physicochemical properties of fruits of three apple cultivars as influenced by stage of maturity and packaging types over storage period of three weeks. The research was designed to replicate practices by the producers and along the value chains and to assess the fruit quality under the conditions of the major markets in the region. All the measured physicochemical parameters significantly varied with the cultivars, maturity stage at harvest and packaging types. Higher firmness was recorded for the samples harvested about two weeks before the optimal maturity, usually practiced by significant number of producers to gain market advantage. Lower total soluble solids corresponded to the early harvested samples regardless of cultivars and packaging types. The firmness was observed decreasing over the storage periods whereas the total soluble solids increased, which is associated to improving sensorial quality for the early harvested cultivars as the soluble solids are mainly sugars. The early harvesting resulted in fruits of inferior desirability including extreme hardness, firmness and low total soluble solids that may have high sourness and less sweet taste. Awareness creation for the producers on the quality and advantages of harvesting their produces at optimal maturity and practicing good postharvest management is required.

This paper presents the validation of a numerical method for quantifying the physical quality parameters of apples through a comparative analysis with a traditional measurement method. The numerical method was applied to determine the parameters of batches of Kazakh apples according to standard requirements, using image analysis of apples. Five common varieties of Kazakh apples were selected: Aport Alexander, Ainur, Sinap Almatynski, Nursat and Kazakhski Yubileinyi. The geometric parameters of the apples and the percentage of red in the images were determined. The parameters of the 5 apple varieties were processed and measured both manually and digitally, revealing a close agreement between the obtained values. The developed digital method achieved high accuracy in determining the (diameters (d) and (D) in two perpendicular planes and height (h) of each apple), with maximum relative errors of 2.99% for (d), 3.03%, and 4.12% for the (h), and (D) parameters, respectively. Regression models were developed to determine and predict the mass and volume of apples via the digital method. The best results for the apple weight prediction were obtained for Sinap Almatynski variety by stepwise linear regression, and for the apple volume prediction were obtained for Nursat variety by linear regression. Regression equations for mass, volume and geometric dimensions constitute the basis for the development of a small instrument for automatically sorting apples by commercial variety.

The Robotics Vision Lab of Northwest Nazarene University has developed Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 seconds. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted with the lowest noise in the images. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94%. compared to 88% during the daytime operations.

Life Cycle Cost Analysis is a method used to assess long-term economic efficiency among equivalent competing processes or products. The purpose of the paper is to investigate the nature and level of costs for an organic orchard located in Southern Romania, using a complex approach covering the entire chain of production, through the life span. The research results, based on a dynamic analysis and an integrated evaluation of the orchard's performance, have been ranked on investment and operational costs, broken down on three categories (establishment and production, post-harvest and transport costs). The highest cost, representing 151726 euros/ha/20 years, about 52.72% of the total operational costs and 50.4% of the total farm costs/ha/20 years, was recorded in exploitation stage. The scenarios for the sensitivity analysis considered different levels of average yield (40, and, respectively 60 tons/ha) with different rate of sold production (85%, optimistic scenario; 70%, pessimistic scenario). The hotpoints identified at the production stage were use of agricultural machinery, several pesticides, the costs of seedlings, anti-hail net, plastic boxes and labor cost, while at the post-harvest stage, were those related to labor and energy consumption. The transport stage has had important costs with tractor operation and the track.

In this paper, we propose a system to create high-precision maps using UAV-LiDAR and to determine the location of individual fruit trees (apple trees) on the maps. The system is based on a UAV-LiDAR system that flies over an actual orchard. A UAV was flown over an actual orchard, and the point cloud of the onboard LiDAR and the location information of RTK-GNSS were obtained. The system records the LiDAR point cloud and RTK-GNSS position information. Automated software processes point cloud data offline and Automated software processes point cloud data offline and automatically segments each apple tree in the map. The RTK-GNSS position information is used for the segmented trees. The positional information obtained from RTK-GNSS was georeferenced to the segmented trees without using ground evaluation points. As a sample, location information was obtained from trees using the Quasi-Zenith Satellite System (QZSS) MICHIBIKI. The positional accuracy of the trees was evaluated using the positional information obtained from the Quasi-Zenith Satellite System MICHIBIKI as a reference. As a result, the alignment accuracy was sufficient to identify individual fruit trees.

The Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is a significant secondary pest of apple trees in Northeast Asia. To better understand its population dynamics, a population model based on temperature-developmental relationships was constructed. This model includes three sub-models: spring emergence, immature stage transition, and adult oviposition. Field data were collected from sex-pheromone baited traps in apple orchards in Andong, Korea, dur-ing 2015 and 2016 to validate the model. Simulations under six pesticide-natural enemy scenari-os showed that the population size of each generation was best simulated when weighted mor-tality factors for pesticides and natural enemies were applied. Using daily temperature inputs, the model demonstrated that P. ringoniella typically undergoes five generations per year, with peak times predicted within a seven-day margin of field data. Sensitivity analyses revealed that population size was influenced by total fecundity and the larval stage model, but peak times remained consistent despite parameter changes. Higher temperatures led to earlier adult peak dates, especially in summer generations. This model serves as a fundamental tool for estimating population dynamics and abundance changes of P. ringoniella and can guide the timings of pesti-cide application. Further validation is necessary to test the model's efficacy in controlling pests in apple orchards.

Reflected light carries ample information about biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress brought about affordable imaging hyperspectrometers (IH) providing spatially resolved spectral data on plants. The extraction of sensible information from hyperspectral reflectance images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded by IH under ambient sunlight. We aimed at obtaining a deeper insight into plant optics as perceived by IH since there is a high demand for algorithms for fruit harvesting and grading systems equipped with computer vision and robotic systems capable of working in orchard. We report on the characteristic changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy fruit, pigment breakdown during sunscald and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point” reflectance measured with a conventional spectrophotometer under controlled illumination conditions. Most of the spectral features and patterns of plant reflectance were evident in the IH-derived reflectance images. As a step forward, a novel index for highlighting tissue damages on the background of the anthocyanin absorption, BRI-M = (1/Rorange – 1/Rred + 1/RNIR), is suggested. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.

The main apple production areas of the Loess Plateau, China limited information on soil and leaf K, Ca, Mg, and micronutrients and their relationships in these orchards. Therefore, we collected soil and leaf samples from the main apple production areas of the Loess Plateau and analyzed them to assess soil and leaf K, Ca, Mg, Cu, Mn, Zn, and Fe as well as to visualize their relationships. The investigated apple orchards in the Loess Plateau exhibited low soil Mn, moderate soil K, Zn, Fe, and high Cu and deficiency of leaf Fe, Moderate of leaf K, Cu, and Zn, high of leaf Ca, Mg, and excessive in leaf Mn. Moreover, soil pH negatively correlated with soil K, Cu, Zn, and Fe and positively related to exchangeable Mg, leaf Mg, and leaf Ca. Exchangeable Ca was negatively related to soil Mg, Cu, Mn, and Fe. Meanwhile, exchangeable K also negative correlation with leaf Mg, indicating the importance of soil pH and exchangeable Ca as an indication of apple produc-tion. In addition, available Zn also negative correlation with leaf Ca and leaf Zn. Soil micronu-trient contents in Shaanxi province trend lower than in other provinces, suggesting that soil Mn and Zn deficiency may be a limiting factor for apple production. Consequently, it is suggested that the soil pH be reduced in the Loess Plateau, replenishing soil organic matter and micronutrients, controlling soil Ca and Mg, and the Loess Plateau must pay attention to controlling K fertilizer application to avoid soil exchangeable cation and enhancing organic fertilizer application to improve soil micronutrients (Zn, Mn, and Fe) are highly necessary and it is necessary to enhance leaf Fe.

In 1789, the Annonaceae family was cataloged by de Jussieu. It encompasses tropical and subtropical plants which are widespread in distribution across various continents such as Asia, South and Central America, Australia and Africa. The genus of Annona is one of the 120 genera of the Annonaceae family and contains more than 119 species of trees and shrubs. The majority of these species are found in tropical America with more than 105 species (26 of them are endemic). Due to its edible fruits and medicinal properties, Annona is the most important genus of Annonaceae family. Despite Annona having many species, only limited species of this family are economically important such as A. squamosa L. (sugar apple), A. cherimola Mill. (Cherimoya), A. muricata L. (guanabana or soursop), A. atemoya Mabb. (atemoya), a hybrid between A. cherimola and A. squamosa, A. reticulata L. (custard apple), A. glabra L. (pond-apple) and A. macroprophyllata Donn. Sm. (ilama). Phytochemically, several classes of secondary metabolites such as acetogenins, essential oils, alkaloids, terpenoids and flavonoids have been described in this genus. A variety of pharmacological activities have been reported from various parts of Annona species specially leaves and seeds including applications against antibacterial, anticancer, antidiabetic and anti-inflammatory.

Côte d’Ivoire has the main cashew tree culture area worldwide, but the cashew apple produced is still underutilized although its potential for industry. In the present study, the aim was to isolate and identify acetic acid bacterial strains, and to assess their potential use for cashew apple-based vinegar production. Vinegar mother from fermented juice was used to isolate acetic acid bacteria on a standard glucose-based medium. Physiological and biochemical tests followed by 16S rRNA gene analysis and phylogeny were used for isolate characterization. Moreover, their acetic acid production capacity was assessed. As results, five (05) strains of the Acetobacter genus were isolated. Phenotypic and phylogenetic analysis revealed that four of them, namely OYA2, OYA6, OYA9, and OYA10, belong to A. tropicalis / A. senegalensis species complex with 99,7% or 100% similarity. The last strain, OYA7, match to A. syzygii (99,7%). All the isolates were resistant to 15° alcohol (v/v) and grew well between pH 5.0 and 6.5. Their optimal growth temperatures varied between 27°C and 37°C, and only isolate OYA6 grows up to 40°C. They produced vinegar with yield (Yp/s , g/g) varying from 0.82 to 0.92, and acetic acid content (g/L) of 80.67 ± 2.1, 70.26 ± 0.9, 70.11 ± 1.7, 68.70 ± 1.5 and 67.22 ± 0.40 were obtained with OYA6, OYA7, OYA2, OYA10, and OYA9, respectively. Thus, the isolate OYA6 appeared as the best candidate for vinegar production, owing to its superior yield and thermotolerance ability that need to be further explored for industry use purpose.

Keywords: xylose reductase; cashew apple bagasse; bioprocesses; fluid dynamics; xylitol.

This study aimed to extract bioactive compounds from apple (Malus domestica) peel waste using supercritical CO2 extraction. The bioactive fractions from apple peels from the three Italian cultivars were extracted applying a temperature of 60 °C and a pressure of 250 bar for 15 min. 20% ethanol was used as a co-solvent. The extraction process was carried out at a flow rate of 2 mL/min and their total polyphenol (TP), anthocyanin (TA), ascorbic acid (AA), and antioxidant activity contents (TAC) were measured. The extracts from the Stark cultivar had the highest levels of polyphenols, anthocyanins, and ascorbic acid, while the Royal Gala cultivar had the highest total antioxidant activity. Chromatographic analyses were also performed to characterize polyphenols. The extracts were then tested for their effect on the mitochondrial NADH-ubiquinone oxidoreductase (Complex I) activity on mitochondria isolated from human embryonic kidney cells (HEK239) cells, with the Stark extracts showing the most positive response in terms of NADH oxidation. These findings highlight the potential of apple peel waste as a source of functional phytocompounds and suggest the Stark cultivar extracts may have pharmacological applications. This study supports the circular bioeconomy by promoting the use of waste products as a valuable resource.

Optical sensor data can be used to determine changes in anthocyanins, chlorophyll and soluble solids content (SSC) in apple production. In this study, visible and near-infrared spectra (729 to 975 nm) were transformed to SSC values by advanced multivariate calibration models i.e. partial least square regression (PLSR) in order to test the substitution of destructive chemical analyses through non-destructive optical measurements. Spectral field scans were carried out from 2016 to 2018 on marked ’Braeburn’ apples in Southwest Germany. The study combines an in-depth statistical analyses of longitudinal SSC values with horticultural knowledge to set guidelines for further applied use of SSC predictions in the orchard to gain insights into apple carbohydrate physiology. The PLSR models were investigated with respect to sample size, seasonal variation, laboratory errors and the explanatory power of PLSR models when applied to independent samples. As a result of Monte Carlo simulations, PLSR modelled SSC only depended to a minor extent on the absolute number and accuracy of the wet chemistry laboratory calibration measurements. The comparison between non-destructive SSC determinations in the orchard with standard destructive lab testing at harvest on an independent sample showed mean differences of 0.5 % SSC over all study years. SSC modelling with longitudinal linear mixed-effect (LME) models linked high crop loads to lower SSC values at harvest and higher SSC values for fruit from the top part of a tree.

The paper presents two procedures for creating 3D discrete models of apple using the finite element method (FEM). The study focused on a new Polish apple variety, Chopin at development, ripening, and senescence maturity stages. The analysis of the results allowed for the determination that, depending on the desired outcomes, the parenchymal tissue can be considered as a material with either elastic or elastoplastic properties. Model validation was performed using displacement-force relationships and changes in contact area as a function of loading force obtained from compression tests. Elastoplastic models reflect empirical studies much better. The fit of the elastic models, based on force vs. displacement curves, ranged from 79% to 83%, while the fit of the elastoplastic models to empirical data ranged from 91% to 96%. Elastic models, on the other hand, are well-suited to the modified force vs. displacement curves, which reflect the elastic behavior of apples under compression test. Contour plots of pressures for elastoplastic models align much better with the test results than those for elastic models. 4. Similarly, in the case of contact area values, the elastoplastic models outperformed the elastic models, achieving a fit between 92% and 96%, while the elastic models reached only 82% to 83% compliance.

Fresh-cut apple is commonly known as minimally processed agriculture products because of their convenience and ease of consumption. However, during storage, the quality of the apple rapidly changes after the cutting process due to enzymatic and non-enzymatic processes. This study aimed to monitor the quality changes of fresh-cut apples at various temperatures and different types of products in the application of a cold chain system during distribution and storage. The results showed that the total viable count in fresh-cut apples initially differed between apple only and mixed products (apple, orange, and tomato) during storage (p < 0.05). Moreover, there are no significant differences (p > 0.05) in physicochemicals except firmness, titratable acidity, and pH, which are highly correlated to microbial growth (total viable count and total coliform number). Furthermore, the optimal temperature for extending the shelf-life of fresh-cut apples was at 4°C.

The paper presents research results on biocomposites made from a combination of extruded apple pomace (EAP) and potato starch (SP). The products were manufactured by hot pressing using a hydraulic press with a mold for producing samples. The prepared biocomposites were subjected to strength tests, surface wettability was determined and color analysis was carried out. Thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR) and cross section observed in Scanning electron microscope (SEM) were also performed. The obtained test results showed that the combination of apple pomace (EAP) and starch (SP) enabled the production of compact bicomposite materials. At the same time, it was found that each increase in the share of starch in the mixture for producing biocomposites increased the strength parameters of the obtained materials. With the highest share of starch in the mixture, 40%, and a raw material moisture content of 14%, the material had the best strength parameters and was even characterized by hydrophobic properties. It was also found that materials with a high content of starch are characterized by increased temperature resistance. Analysis of SEM microscopic photos showed well-glued particles of apple pomace, pectin, gelatinized starch and a smooth external structure of the samples. Research and analyzes have shown that apple pomace reinforced only with the addition of starch can be a promising raw material for the production of simple, biodegradable biocomposite materials.

Apple (Malus x domestica) fruit size is dependent on cell division and cell expansion, processes which are subsequently regulated by plant hormones such as auxins, gibberellins, and cytokinins. In this study, we investigated the role of cell division and cell expansion in apple growth and identified which of the two was more deterministic to final fruit size. Three cultivars of different sizes were selected, namely, ‘Twenty Ounce’ (large-sized), ‘Royal Gala’ (medium-sized), and ‘Crabapple’ (small-sized). Gene expression and cell size analyses were conducted over the course of two consecutive seasons. The expression patterns of three classes of genes were markedly similar across all cultivars. Two cell division markers, namely, MdCDKB2;2 and MdANT2, were discovered to be correlatively expressed as both displayed initially high expression levels, which gradually declined from the early to late stages of growth time course. For cell expansion markers, MdEXP3 was upregulated as the cells expanded, while MdARF106 was expressed in both the cell division and expansion stages. Meanwhile, the ripening related gene MdACO1 was expectedly expressed only during the ending stages associated with ripening. Interestingly, the cell measurements taken regularly from each cultivar throughout the same eperimental timespan showed that cell sizes were unaltered and remained constant from initial pollination at the zeroth Days After Pollination (DAP), to ripening at 120 Days After Full Bloom (DAFB).

The aim of the study was to assess the effect of the addition of inulin and the replacement of part of inulin with apple fiber on the physicochemical and organoleptic properties. Moreover, the survival of Bifidobacterium animalis ssp. Lactis Bb-12 and Lactobacillus rhamnosus was studied in sheep milk ice cream. There was no effect of apple fiber and the type of bacteria on the number of bacteria of the probiotics after fermentation. As a result of freezing, mixture containing Bifidobacterium animalis ssp. Lactis Bb-12, there was a significant reduction in the bacterial from 0.39 log cfu g −1 to 0.46 log cfu g −1. In all ice cream on the 21st day of storage, it exceeded 10 log cfu g –1, which means that the ice cream retained the status of probiotic products. The Lactobacillus rhamnosus ice cream showed a lower yellow colour compared to the Bifidobacterium Bb-12 ice cream. The overrun of sheep's milk ice cream was within a range from 78.50% to 80.41%. The appearance of sheep's milk ice cream is influenced considerably by the addition of fiber and the type of bacteria and the interaction between the type of bacteria and the addition of fiber and storage time and fiber.

In the study, a new correction method was applied to reduce error during detection on mixed pesticide residue in apples by using Raman spectra. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples can be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which interferes the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method is proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 10-1 g•kg-1 to 10-6 g•kg-1 and Lagrangian interpolation are applied in the correction method. And a smooth surface is applied to describe the correction ratio of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction ratio will be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide will be acquired by the correction method. Correlation coefficient of model validation exceeds 0.88 generally and Root Mean Square Error also decreases obviously after correction, which proved the reliability of the method.

In this study, a new approach to pesticide permeation through the apple peel into the pulp is discussed. The tested compounds can be classified, based on mode of action, as systemic (boscalid, cyprodinil, pirimicarb, propiconazole and tebuconazole) or contact (captan, cypermethrin and fludioxonil) pesticides. The barrier effect was assessed using a Franz flow-type vertical diffusion cell system. Residue analysis was performed using a modified quick, easy, cheap, efficient, rugged and safe (QuEChERS) extraction method coupled to gas chromatography with tandem mass spectrometry (GC-MS/MS). The limits of detection (LODs) ranged between 2.6 µg kg-1 (pirimicarb) and 17 µg kg-1 (captan), with the coefficient of variability (CV) lower than 6%, while recoveries ranged from 85% (boscalid) to 112% (captan) at 0.1 and 1 mg kg-1 spiked levels. The highest peel penetration was observed for pirimicarb, captan and cyprodinil, with cumulative permeation of 90, 19 and 17 µg cm-2, respectively. The total absorption was in the range from 0.32% (tebuconazole) to 32% (pirimicarb). Only cypermethrin was not quantitatively detected in the pulp and its use can be recommended in crop protection techniques. The obtained results indicate that molecular weight, octanol-water partition coefficient and water solubility are important parameters determining the process of pesticide absorption.

Consumer rhythm-monitoring devices, such as the Apple Watch, are becoming more readily available. Irregular pulses can be detected using an optical sensor built into the wearable device. The Apple Watch (Apple Inc., Cupertino, CA, USA) is a class II medical device with pulse rate and electrocardiography (ECG) monitoring capabilities. Here we report a case in which an arrhythmia that was conventionally perceived but undiagnosed was identified as atrial fibrillation by self-acquisition of ECG data using an Apple Watch.

In this paper, PEN3 electronic nose was used to detect and recognize fresh and moldy apples (inoculated with Penicillium expansum and Aspergillusniger) taken Golden Delicious apples as model subject. Firstly, the apples were divided into two groups: apples only inoculated with different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined, which can simplify the analysis process and improve the accuracy of results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM) and radial basis function neural network (RBFNN), were then applied to analyze the data obtained from the characteristic sensors, respectively, aiming at establishing the prediction model of flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W and W2W in the PEN3 electronic nose exhibited strong signal response to the flavor information, indicating were most closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors was used for modeling. The results of the four pattern recognition methods showed that BPNN presented the best prediction performance for the training and validation sets for both the Group A and Group B, with prediction accuracies of 96.29% and 90.00% (Group A), 77.70% and 72.00% (Group B), respectively. Therefore, it first demonstrated that PEN3 electronic nose can not only effectively detect and recognize the fresh and moldy apples, but also can distinguish apples inoculated with different molds.

The agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. Grad-CAM, or gradient-weighted class activation mapping, creates heatmaps that highlight significant regions for prediction, which enhances the interpretability of the model.To increase robustness and accuracy, techniques such as spectral-shifted adversarial perturbation for data augmentation and spectrally-weighted global average pooling for feature aggregation are used. This technique provides 99.75% accuracy with minimal processing needs, making it acceptable for real-time applications in agricultural situations. This considerably improves apple disease management.

Apple replant disease (ARD) is a complex issue caused by various fungal, oomycetes pathogens and parasites which can differ in their abundance between sites within a geographic region. Knowledge of the specific etiology of ARD at a particular site is required in order to develop commercially viable soil management strategies to combat specific/individual components of the disease. In this study we analyzed the soil from six ARD sites for the presence of fungal, bacterial and oomycetes communities and plant parasitic nematodes. Five fungal, and 17 bacterial classes were differentially represented in the microbiomes from different locations. Mortierellomycetes was the most abundant fungal taxa represented followed by Sordariomycetes. Mortierella exigua a fungal endophyte was the most abundant fungal amplicon sequence variant (ASV) in the core microbiome. Proteobacteria was the most prevalent phylum identified in orchard soils. Several potential phytopathogenic fungi involved in ARD and endophytes including Fusarium oxysporum, F. solani, Nectria ramulariae, Ilyonectria robusta and Nectriaceae were identfied in the orchard soil. Pythium attrantheridium (Globisporangium attrantheridium), P. monospermum and P. ultimum (Globisporangium ultimum) were the most abundant oomycete taxa identified in these soil samples. Six different groups of plant-parasitic nematodes were found across the six orchards soil. Root-lesion nematodes, Pratylenchus spp., which are commonly associated with ARD, were identified in all orchards soil at population densities ranging from 12 to 33/100 cm3 soil. This research contributes valuable understanding of the ARD complex as the apple industry needs alternative approaches to combat the disease.

Apple red flesh coloration is a result of the performance of biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis have been identified so far. In this study, we investigated previously unrecognized genes involved in the constitution of red-flesh apple phenotype. These genes could be concerned as molecular markers for early selection of new apple cultivars. Based on comparative transcriptome analysis of apples with different fruit flesh coloration, we successfully identified and characterized ten genes from plant hormone transduction pathway of: auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). In presented studies an analysis of their expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. Generally, we have uncovered, the set of the novel genes, mediating regulation of the abscisic acid, salicylic acid, cytokinins and jasmonic acid signaling and preliminarily described their role in anthocyanin biosynthesis, accumulation and degradation. Their expression was not characterized in apple fruits so far. Presented study, allowed to underline the relationship of genes from hormone signal transduction pathway with UFGT gene, directly responsible for anthocyanin color transformation as well as confirmed their crucial role of plant hormone regulation and anthocyanin accumulation during apple fruit ripening.

Apple latent spherical virus (ALSV) is widely used as a virus-induced gene silencing (VIGS) vector for function genome study. However, the application of ALSV in soybean is limited by the resistance of many varieties. In this study, the genetic loci linked to the resistance of a resistant soybean variety Heinong 84 was mapped by high-throughput sequencing‒Bulk Segregation Analysis (HTS‒BSA) using a hybrid population crossed from Heinong 84 and a susceptible variety Zhonghuang 13. Results showed that the resistance of Heinong 84 to ALSV is controlled by two genetic loci located on chromosome 2 and 11, respectively. Cleaved amplified polymorphic sequences (CAPS) markers were developed for identification and genotyping. Inheritance and biochemical analyses suggest that the resistance locus on chromosome 2 plays a dominant dose-dependent role, while the other locus contributes a secondary role in resisting ALSV. The resistance locus on chromosome 2 might encode a protein that can directly inhibit viral proliferation, while the secondary resistance locus on chromosome 11 may encode a host factor required for viral proliferation. Together, these data reveal novel insights on the resistance mechanism of Heinong 84 to ALSV, which will benefit for the application of ALSV as a VIGS vector.

Fire blight, caused by Erwinia amylovora, is a devastating bacterial disease that threatens world-wide production of apples and pears. In Canada and the US, streptomycin and kasugamycin are commonly used for the control of this pathogen. There is an urgent need for development of biocontrol agents (BCAs) that can be easily incorporated into integrated pest management pro-grams with high efficacy against streptomycin sensitive and resistant strains of E. amylovora. In addition, excessive use of antibiotics in agriculture has increased awareness of the possibility of antibiotic resistance gene transfer to human, animal and environmental microbes. To address this issue, a phage-carrier system (PCS) that combines Pantoea agglomerans as an antagonistic bacterial carrier of Erwinia phages as a double-pronged treatment for fire blight have been de-veloped. The low viability of P. agglomerans cells following spray drying challenged the industrial scale production of this PCS. In the present research, a spray drying protocol was developed for P. agglomerans by modifying the growth medium and the bacterial cell formulation using D(+)-trehalose and maltodextrin. The developed protocol is amenable to industrial scale pro-duction of the BCA/PCS. The P. agglomerans viability was greater than 90% after spray drying, had a prolonged shelf life of 4 months at 4 C, and the reconstituted cells showed an approximate 3 log reduction in E. amylovora counts with a green pear disc challenge assay.

Many studies have been conducted using pectin for modified drug delivery. In this regard, the goal of the work was to obtain interpolyelectrolyte complexes based on citrus or apple pectin and the Eudragit® EPO and study properties from the standpoint of using these carriers for oral drug delivery. Turbidity, gravimetry, viscosity, elementary analysis and FTIR spectroscopy, DSC analysis were used for physicochemical characterization. Diffusion-transport characteristics were studied to assess the swelling ability of the matrices and the release of diclofenac sodium. Mathematical modeling of the release parameters was carried out using the Krosmayer-Peppas equation. During the turbidity study, stoichiometry compositions were selected for the studied IPECs EPO/PeсA and EPO/PeсC at pH values = 4.0, 5.0, 6.0, 7.0. FTIR spectra of the complexes are characterized by an increase in the intensity of the bands at 1610 cm-1 and 1400 cm-1. According to the DSC analysis IPEC has a certain Tg = 57.3°C. The highest release rates are shown by IPEC EPO/PecC_1 and EPO/PecC_4. The mechanism of drug transport from the matrices IPEC EPO/PecC, IPEC EPO/PecA_3 and EPO/PecA_4 can be characterized as Super Case II. Anomalous (non-Fickian release) is typical for IPEC EPO/PecA_1 and EPO/PecA_2. Thus, the resulting systems are suitable for colon-specific drug delivery systems.

Development of high nutritious bakery products with optimum utilization of food waste is a major challenge for food industries. Food waste is major challenge in an each country. The optimum utilization of food waste for sustainable development goal of country is important ant for growth of the nation. The aim of the present work is to prepare value-added composite flour mixed bread from wastes of fruit and vegetables. The composite flour was prepared in four formulations of Peel and Pomace Wheat Flour (PPWF), as PPWF1, PPWF2, PPWF3 and PPWF4. Compositing flour was blended by a mix of vegetable and fruits pomace powders and whole wheat flour. Indian gooseberry pomace powder, apple pomace powder, bottle gourd peel powder, and potato peel powder were used with whole wheat flour to make pomace and whole wheat flour compositions as PPWF1, PPWF2, PPWF3 and PPWF4. Broadly this work was divided into three phases, first phase has conducted nutritional and functional analysis on peel and pomace powders individually, which shows the significance of using them for our research work. The second phase works in the creation of composite flours with different composition ratios as discussed above as PPWF. This phase shows the nutritional and functional analysis of four composite flours. Out of these four flours, PPWF3 contained a good amount of fiber 8.16%, crude protein 3.18%, total phenolic content 14.48%, moisture 9.5%, vitamin C 13.64mg/100g, and total phenolic compound 14.48(mg/GAE/g) which are maximum and acceptable range values as compared to other three composite flours and the wheat flour-control group flour. Then in the third phase, the PPWF3 is used as a partial replacement ratio of wheat flour in wheat flour due to its high phenolic content, Vitamin C, and the richness of fibers. This composite flour is used to make bread dough, two samples G1 and G2 are made; out of which G2 offers better nutritional, functional, and sensory evaluations in comparison with refined wheat four bread is taken as a control group. Thus, such utilization of food wastes in bread making can generate a value of wastes and can improve nutritional attributes of bread, which may improve an individual’s health.

Various therapeutic approaches have been developed for neglect. Many studies have demonstrated the effect of Digital therapeutics(DTx) on neglect. By contrast, only a few studies have reported on the effects of DTx on egocentric and allocentric neglect. In this article, seven studies on DTx of egocentric and allocentric neglect were reviewed. DTx, which was employed in these studies, could be classified as follows: 1) software adaptation in traditional treatment, 2) VR game using the head-mount display as treatment, and 3) development of new digital program like ReMoVES. In addition, more studies and effective results were reported on egocentric neglect than allocentric neglect. In future studies, each effect on egocentric and allocentric neglect should be identified in detail through the appropriate use of differential evaluation and long-term application of independent digital therapeutics.

Apple chlorotic leaf spot virus (ACLSV) is a widespread, deleterious and the most damaging pathogen of fruit tree plants including domesticated apple (Malus domestica) ―a great threat to apple production worldwide. The positive-sense single-stranded RNA genome of ACLSV (7.5 Kbp) encodes three proteins: RNA polymerase (Rep), movement protein (MP) and coat protein (CP). RNA interference (RNAi)-mediated antiviral innate immunity is a key sequence-specific biological phenomenon in eukaryotes to control plant viruses. The aim of this study was to analyze apple (M.domestica) locus-derived microRNAs (mdm-miRNAs) with predicted potential for targeting the ACLSV +ssRNA-encoded mRNAs, using ‘four algorithms’ approach. The ultimate goal in this research is to mobilize the in silico endogenous predicted mdm-miRNAs to trigger RNAi catalytic pathway experimentally and generate apple tree varieties for evaluating potential antiviral resistance monitoring capability and capacity for ACLSV. Experimentally validated mature apple (M.domestica, 2n = 2X = 34) mdm-miRNAs (n = 322) were acquired from miRBase database and tested for alignment with the ACLSV genome. Of the 322 targeting mature locus-derived mdm-miRNAs investigated, nine apple mdm-miRNA homologs (mdm-miR395k, mdm-miR5225c and mdm-miR7121 (a, b, c, d, e, f, g, h) were predicted by all ‘four algorithms’. Only fifty eight mdm-miRNAs were predicted consensus binding sites by union of consensus between two algorithms. The miRanda, RNA22, TAPIR algorithms predicted binding of mdm-miR395k at unique nucleotide position 4691, as the most effectively interacting mdm-miRNAs in targeting the ORF1 sequence. In order to validate target prediction accuracy, whether the apple mdm-miRNAs might bind predicted ACLSV mRNA target(s), we created an integrated Circos plot. Genome-wide in-silico-predicted miRNA-mediated target gene regulatory network validated interactions that warrant in vivo analysis. The current work provides valuable evidence and biological material for generating ACLSV-resistant apple varieties.

This study was carried out to determine the effects of apple vinegar and sodium diacetate addition on the aerobic stability of fermented high moisture maize grain (HMM) silage after opening. In the study, the effect of three different levels (0, 0.5, 1.0%) of apple vinegar (AV) and sodium diacetate (SDA) supplementation to fermented HMM at two different storage conditions (27-29°C, 48% Humidity; 35-37°C, 26% Humidity) were investigated. The material of the study was fermented rolled maize grain with 62% moisture content stored for about 120 days. Silage samples were subjected to aerobic stability test with 3 replicates for each treatment group. Wendee and microbiological analyses were made at 0, 2, 4, 7, and 12 days. Meanwhile, samples were displayed in the T200 IR brand thermal camera. According to the thermogram results, 1% SDA addition positively affected HMM silages at the second and fourth days of aerobic stability at both storage conditions (p<0.05). Aerobic stability and infrared thermography analysis indicated that 1% AV, 0.5%, and 1% SDA additions to HMM silages had promising effects. Due to our results, we concluded that thermal camera images might be used as an alternative quality indicator for silages in laboratory conditions.

Apple replant disease (ARD) is a severe problem in orchards and tree nurseries caused by yet unknown soil biota that accumulate over replanting cycles. This study tested the contribution of nematodes to ARD, and cultivation of Tagetes as a control option. In a pot experiment, Tagetes patula or Tagetes tenuifolia were grown in ARD soil, incorporated or removed. Nematodes extracted from untreated ARD soil and washed on 20 µm-sieves induced ARD symptoms when inoculated to apple saplings growing in a sterile substrate. In contrast, nematodes from Tagetes treated ARD soil did not reduce root growth compared to uninoculated plants, irrespective of Tagetes species and incorporation. In plots of five apple tree nurseries or orchards, either Tagetes or grass was grown on ARD soil. Nematodes extracted from the grass plots and inoculated to apple saplings significantly reduced plant growth compared to nematodes from Tagetes plots for all five farms. Apple rootstocks showed overall a significantly higher increase in shoot base diameter when grown on Tagetes-treated plots compared to grass plots, while this effect differed among farms. Plant-parasitic nematodes were too low in abundance to explain plant damage. In conclusion, the free-living nematodes involved in ARD can be controlled by Tagetes.

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, we investigated the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, we collected a total of 1,160 leaf samples. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. Results show, that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches.

Commercially cultivated Prunus species are commonly grown in adjacent or mixed orchards, and can be infected with unique or commonly shared viruses. Apple (Malus domestica), another member of the Rosacea and distantly related to Prunus, can share the same growing regions and common pathogens. Pollen can be a major route for virus transmission, and analysis of the pollen virome in tree fruit orchards can provide insights into these virus pathogen complexes from mixed production sites. Commercial honey bee (Apis mellifera) pollination is essential for improved fruit set and yield in tree fruit production systems. Here we describe the metagenomics-based detection of plant viruses through bee and pollen samples collected during the spring bloom period from a mixed Prunus and Malus orchard. Twenty-one unique viruses were detected in samples collected during apricot (Prunus armeniaca), sweet cherry (Prunus avium), peach (Prunus persica), and apple blooms. Ilarviruses prune dwarf virus (PDV) and prunus necrotic ringspot virus (PNRSV), Secoviridae family members tomato necrotic ringspot virus, tobacco necrotic ringspot virus, and prunus virus F, and Capillovirus cherry virus A (CVA) were detected in all time points, while other viruses were detected with restricted ranges. These results demonstrate the benefits of area-wide monitoring through bee pollination activities, and provide new insights into the diversity of viruses in tree fruit pollination ecosystems.

Background: Sugar laden foods and carbohydrate rich diets with high glycaemic index intake are associated with weight gain, increased incidence of diabetes and is linked with increased cardiovascular risk. A blend of mulberry leaf extract and apple peel extract (Glubloc™) may reduce postprandial blood glucose surge and insulin response following dietary carbohydrate intake by limiting its digestion and uptake from the gut. Methods: In this randomised, placebo-controlled, crossover study, thirty healthy male and female subjects were supplemented with either placebo or Glubloc™, 10 min before a carbohydrate and sugar rich meal intake. Changes in postprandial blood glucose (upto120 minutes) and insulin levels (at 60 minutes) were measured as the primary endpoints. Changes in blood glucose incremental area under the concentration curve (iAUC), maximum concentration (Cmax) and peak time (Tmax) were compared. Results: Thirty adults (mean age 31±7.2 years, BMI 24±3.8 kg/m2) completed the study and the Glubloc™ blend relative to placebo significantly reduced glucose iAUC at 0.5 h (− 38%, p < 0.0003), 1.0 h (− 30%, p = 0.0007), 1.5 h (− 29%, p = 0.0008), and 2.0 h (− 26%, p = 0.002). A statistically significant reduction in 1 h insulin levels (− 35%, p = 0.029) was observed. Conclusion: A single dose supplementation of Glubloc™ significantly reduced the postprandial blood glucose spike and serum insulin levels. The observation of a significant glucose reduction paralleled with a significant lower insulin response supports a reduced gastrointestinal glucose absorption. None of the subjects experienced any major gastrointestinal side effects. This study builds a body of evidence that the combination of white mulberry leaf and green apple peel extracts (Glubloc™) can be a useful supplement to manage postprandial glucose levels, further studies are necessary to evaluate its potential in type 2 diabetes and the regulation of dysglycaemia.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Copper (Cu)-based fungicides are widely used in agriculture, but excessive Cu accumulation in soils threatens crop performance, particularly in perennial fruit crops like apples. This study investigated the effects of varying Cu concentrations on the physiological responses of three apple varieties—Braeburn, Fuji, and Cripps Pink—using a callus-media system. Callus cultures were exposed to Cu concentrations ranging from 0 to 20 mg Cu kg⁻¹ to assess traits such as growth, fresh and dry weight, copper accumulation, and bioconcentration factor (BCF). Results revealed that media Cu concentration had a stronger effect than variety on most traits, especially above 10 mg Cu kg⁻¹, where copper stress reduced genetic variability. At lower Cu concentrations, varietal differences were more apparent, with Cripps Pink showing greater copper tolerance. These findings suggest that high Cu levels suppress varietal differences, leading to a uniform stress response. This study provides valuable insights into copper toxicity in apple varieties and highlights the need to maintain soil Cu levels below critical thresholds to preserve varietal performance, with potential implications for soil-plant systems.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.