Red Apple Research Articles

Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System.

Nowadays, Convolution Neural Network (CNN) based deep learning methods are widely used in detecting and classifying fruits from faults, color and size characteristics. In this study, two different neural network model estimators are employed to detect apples using the Single-Shot Multibox Detection (SSD) Mobilenet and Faster Region-CNN (Faster R-CNN) model architectures, with the custom dataset generated from the red apple species. Each neural network model is trained with created dataset using 4000 apple images. With the trained model, apples are detected and counted autonomously using the developed Flying Robotic System (FRS) in a commercially produced apple orchard. In this way, it is aimed that producers make accurate yield forecasts before commercial agreements. In this paper, SSD-Mobilenet and Faster R-CNN architecture models trained with COCO datasets referenced in many studies, and SSD-Mobilenet and Faster R-CNN models trained with a learning rate ranging from 0.015-0.04 using the custom dataset are compared experimentally in terms of performance. In the experiments implemented, it is observed that the accuracy rates of the proposed models increased to the level of 93%. Consequently, it has been observed that the Faster R-CNN model, which is developed, makes extremely successful determinations by lowering the loss value below 0.1.

Preparation and Characterization of a Nano-Inclusion Complex of Quercetin with β-Cyclodextrin and Its Potential Activity on Cancer Cells.

Quercetin (QRC), a flavonoid found in foods and plants such as red wine, onions, green tea, apples, and berries, possesses remarkable anti-inflammatory and antioxidant properties. These properties make it effective in combating cancer cells, reducing inflammation, protecting against heart disease, and regulating blood sugar levels. To enhance the potential of inclusion complexes (ICs) containing β-cyclodextrin (β-CD) in cancer therapy, they were transformed into nano-inclusion complexes (NICs). In this research, NICs were synthesized using ethanol as a reducing agent in the nanoprecipitation process. By employing FT-IR analysis, it was observed that hydrogen bonds were formed between QRC and β-CD. Moreover, the IC molecules formed NICs through the aggregation facilitated by intermolecular hydrogen bonds. Proton NMR results further confirmed the occurrence of proton shielding and deshielding subsequent to the formation of NICs. The introduction of β-CDs led to the development of a distinctive feather-like structure within the NICs. The particle sizes were consistently measured around 200 nm, and both SAED and XRD patterns indicated the absence of crystalline NICs, providing supporting evidence. Through cytotoxicity and fluorescence-assisted cell-sorting analysis, the synthesized NICs showed no significant damage in the cell line of MCF-7. In comparison to QRC alone, the presence of high concentrations of NICs exhibited a lesser degree of toxicity in normal human lung fibroblast MRC-5 cells. Moreover, the individual and combined administration of both low and high concentrations of NICs effectively suppressed the growth of cancer cells (MDA-MB-231). The solubility improvement resulting from the formation of QRC-NICs with β-CD enhanced the percentage of cell survival for MCF-7 cell types.

Physicochemical and microbiological characteristics of fruit-based kombucha

Kombucha is a fermented drink made out of tea leaves, although recently other alternatives were considered as substitutes, such as fruits. Using different types of fruit could affect the characteristics of fruit-based kombucha. This study investigated the physicochemical and microbiological characteristics of fruit-based kombucha through a Randomized Block Design with the type of fruit (red dragon fruit, apple, snake fruit, strawberries, grapes, pear, red guava, and citrus) as the factor. The produced kombucha drinks were analyzed and compared for pH, total sugar, total phenolic compounds, total flavonoids, antioxidant activity, and total microbes. Statistical tests such as Analysis of Variance (ANOVA) and Least Significance Different (LSD) were employed (α = 5%). Results revealed that physicochemical and microbiological characteristics of kombucha were significantly related to the type of fruit with snake fruit kombucha showing the optimal results of each characteristics as follows: total acetic acid bacteria and yeasts of 1.53×109 CFU/mL, pH of 3.07, total sugar of 2.41%, total phenol of 1006.85 μg/mL GAE, total flavonoids of 1.75 mg QE/mL, and IC50 of DPPH scavenging activity of 5.46 μg/mL. Fermented fruit-based kombuchas are regarded as healthy substitutes to traditional kombucha as they offer rich source of nutrients that enhance human well-being.

MYTHICAL BASIS AND POETIC MEANING OF MOTIFS IN ABAY’S POEM «MASGUT»

In the article Abay’s poem «Masgut» is considered in the mythopoetic aspect. The meaning of gifting an apple as the main mythical motif of this poem is analyzed in detail. The motifs associated with the apple in world mythology and Kazakh folklore were collected and compared in the study. Thus, the meaning of the motif in Abay’s poem is taken into account. One of the mythical motifs of the poem «Masgut» is to see harbinger in a dream. The poet attaches importance to the symbols in the core of knowledge, taking the motifs of ancient times as a basis, and takes these symbolic details as an object in representing his artistic purpose. Among the most popular motifs associated with apples, we can mention the fruit of paradise as special apples that grow in the Garden of the Hesperides and the island of Avalon. The scientists emphasize the dual nature of the apple, saying that the word «apple» is given as «fruit» in many motifs. The apple, which is seen as a symbol of love and knowledge, youth and beauty, on the other hand, is a symbol of enmity, misfortune, temptation, evil and other unpleasant and bad deeds. In Abay’s poem apples are given in three colors, and the red apple chosen by the main character has its own characteristics. Another motif in the poem is dreaming. The dream foretells an upcoming event. In Abay’s artistic knowledge these motifs are important because they describe eternal human ideals, humanism, and social behavior with the subtext. The conclusion in the article is made in this context.

Developing neural-based hardware and soft ware complex with a mobile application for monitoring apple fruits on tree canopy

The paper presents a soft ware and hardware complex with a mobile application based on a neural network, designed to identify apple fruits on tree canopy, to count their number, to determine the quantity of fruits affected by diseases, as well as to estimate the growth rate of apple fruits and, thus, to calculate the total yield during the growing season. The developed soft ware and hardware complex consists of a photo (image) collection unit with client soft ware (a mobile application, a digital camera), a unit for processing the obtained images, which includes a database and a neural network, and a unit for interpretation of the obtained data. A neural network based on VGG-16 and SSD architecture was developed to identify apple fruits on the tree canopy for evaluating apple fruits and distinguishing sound fruits and those affected by disease. Training of the neural network was based on the selected classes of sound red and green apple fruits, and apple fruits affected by diseases – scab, powdery mildew, fruit rot, as well as mechanical damage. The soft ware runs and operates on Ubuntu operating system, a mobile application – on Android. The soft ware package and mobile application are capable of processing incoming photos (images) online, as well as to use previously captured photos. The generated database collects structured information about all field measurements and calculations of the number of apple fruits on the planting rows under study. The experiments conducted on an industrial apple plantation showed that the accuracy of estimating the total number of fruits on the tree canopy compared to the true value was 94.7%, the accuracy of calculating the number of affected fruits was 90.4%. When technical requirements for the server and requirements for images are met, the average recognition rate does not exceed 0.6 seconds per image, the average segmentation rate of the apple fruits from the background does not exceed 0.8 seconds per image, the average speed of analyzing one image and obtaining the recognition result does not exceed 1.5 seconds.

Ethanol fermentation- and ethylene physiology-related gene expression profiles in Red Delicious apples stored under variable hypoxic conditions and protocols

Dynamic Controlled Atmosphere (DCA) is beneficial in maintaining specific quality parameters but, due to the extreme oxygen levels applied, can cause adverse effects on the fruit by inducing excessive anaerobic metabolism and the production of off-flavors. The metabolic adaptation and responses of apples (Malus domestica Borkh.) cv. Red Delicious to static or dynamic oxygen concentrations (0.3 and 0.8%, with sequential shifts) during cold storage for 7 months were studied by monitoring quality parameters and the expression of genes involved in sugar, fermentative metabolism, and ethylene physiology. Ethanol content reached the highest levels (around 400 mg/kg FW) under 0.3% oxygen concentration and fruit firmness appeared to be reduced in samples accumulating the highest levels of ethanol. Oxygen switch was effective in reducing the ethanol concentrations with timing-dependent variable effects. The expression of fermentative (alcohol dehydrogenase, lactate dehydrogenase, pyruvate decarboxylase) and sugar metabolism (β-amylase; phosphofructokinase; sucrose synthase) genes resulted to be differently affected by the hypoxic conditions imposed, in particular during the early stages of storage. Sucrose synthase expression appeared to be highly sensitive to changes in low oxygen concentration. Ethylene biosynthesis (ACC synthase and oxidase) genes showed marked differences in their expression in relation to the static and dynamic protocols and the hypoxic conditions, as well as six Ethylene Responsive Factors (ERF) genes, some of them possibly involved in the oxygen sensing mechanism operating in fruit tissues.

Estreno absoluto: Concert Version of Felipe Pedrell’s La Celestina (1902) in Madrid’s Teatro de la Zarzuela (2022)

<i>Estreno absoluto</i>: Concert Version of Felipe Pedrell’s <i>La Celestina</i> (1902) in Madrid’s Teatro de la Zarzuela (2022)

Magnesium improved fruit quality by regulating photosynthetic nitrogen use efficiency, carbon-nitrogen metabolism, and anthocyanin biosynthesis in 'Red Fuji' apple.

Both nitrogen (N) and magnesium (Mg) play important roles in biochemical and physiological processes in plants. However, the application of excessive N and insufficient Mg may be the factor leading to low nitrogen utilization rate (NUE) and fruit quality degradation in apple production. In this study, we analyzed the effects of different application rates of Mg (0, 50, 100, 150, 200 kg/ha) on the photosynthetic nitrogen use efficiency (PNUE), the accumulation and distribution of carbon (C), N metabolism, anthocyanin biosynthesis and fruit quality of the 'Red Fuji' apple in 2018 and 2019. The results showed that the application of Mg significantly increased the 15NUE and increased the allocation rate of 15N in the leaves whereas the 15N allocation rate in the perennial organs and fruits was decreased. With the increase in Mg supply, the activities of N metabolism enzymes (NiR, GS, and GOGAT) were significantly promoted and the content of intermediate products in N metabolism ( , , and free amino acid) was significantly decreased. Furthermore, an appropriate rate of Mg significantly promoted the net photosynthetic rate (Pn) and photosynthetic nitrogen use efficiency (PNUE), enhanced the enzyme activities of C metabolism (SS, SPS, S6PDH), and increased the contents of sorbitol and sucrose in leaves. In addition, Mg upregulated the gene expression of sugar transporters (MdSOT1, MdSOT3, MdSUT1, and MdSUT4) in fruit stalk and fruit fresh; 13C isotope tracer technology also showed that Mg significantly increased the 13C allocation in the fruits. Mg also significantly increased the expression of anthocyanin biosynthesis genes (MdCHS and MdF3H) and transcription factors (MdMYB1 and MdbZIP44) and the content of anthocyanin in apple peel. The comprehensive analysis showed that the appropriate application of Mg (150 kg/ha) promoted PNUE, C-N metabolism, and anthocyanin biosynthesis in apple trees.

Sustainable Yield Prediction in Agricultural Areas Based on Fruit Counting Approach

A sustainable yield prediction in agricultural fields is a very critical task that aims to help farmers have an idea about agricultural operations. Generally, we can find a variety of applications proposed for this purpose that include fruit counting. These applications are based on Artificial Intelligence, especially Deep Learning (DL) and Machine Learning (ML) approaches. These approaches give reliable counting accuracy, but the problem is the use of a large database to achieve the desired accuracy. That makes these approaches limited. For this reason, in this work, we propose a low-complexity algorithm that aims to count green and red apples based on our real dataset collected in the Moroccan region, Fes-Meknes. This algorithm allowed us to further increase sustainability in agricultural fields based on yield prediction. The proposed approach was based on HSV conversion and the Hough transform for fruit counting. The algorithm was divided into three blocks based on image acquisition and filtering for the first block. The second block is the conversion to HSV and the detection of fruits. Finally, the counting operation for the third block. Subsequently, we proposed an implementation based on the low-cost Raspberry system and a desktop. The results show that we can reach 15 fps in the case of the Raspberry architecture and 40 fps based on the desktop. Our proposed system can inform agricultural policy by providing accurate and timely information on crop production, which can be used to guide decisions on food supply and distribution.

НАПИТОК ИЗ МОРКОВИ, КРАСНОЙ СВЕКЛЫ И КРАСНОГО ЯБЛОКА ОПТИМИЗИРОВАННОГО СОСТАВА

Raw materials carrot, red beet and red apple, rich in biologically active compounds and secondary metabolites (polyphenols), were studied to participate in the development of experimental samples of vegetable-based beverages.Model test samples of mono-component, two-component and multi-component drinks from the listed raw materials were developed by cold pressing. Characterization of the developed test samples was carried out on the basis of the physicochemical, sensory and spectral tests carried out on the day of their receipt. As a result of the obtained mathematical models for the content of total polyphenols, antioxidant capacity, evaluated by determining the radical scavenging ability (DPPH-test) and sensory evaluation, the composition of developed trial samples of vegetable-based drinks was optimized and the following composition was recommended: carrot drink — 50 %, red beet — from 75 to 100 % andred apple — from 0 to 40 %.

Analysis of biofertilization impacts on Pisum sativum

Nutrient degradation in soil is becoming a prominent agricultural issue due to widespread unsustainable farming practices such as overgrazing and over-cultivation. As agriculture is such an integral aspect of society, developing methods to increase crop yield is imperative for feeding the future generations of humankind. The United States spends billions of dollars each year to combat nutrient loss in soil, but soil productivity and crop yields are still decreasing. In this study, we explored the various effects of three different produce-based biofertilizers on pea plant growth, using red apple, pear, strawberry, and control treatments. To apply the fruit biomatter, we peeled, powdered, and mixed the fruit with distilled water to form a solution. We watered the pea plants, Pisum sativum, with the different solutions for the duration of the 18-week experiment. We hypothesized that the application of fruit biomatter would increase the growth of pea plants, with the application of strawberry biomatter having the most significant effect due to strawberries containing a higher nutrient content compared to pears and apples. Upon analysis, we observed that the fruit biomatter with strawberry peels significantly increased the growth of the pea plants, while the application of red apple biomatter and pear peel biomatter had no significant effect. The application of strawberry biomatter could prove to be an effective way to increase plant growth in commercial agriculture. Further research is needed to determine if the application of apple and pear biofertilizers increases the plant growth of Pisum sativum.

Polyhydroxyalkanoate Production from Fruit and Vegetable Waste Processing.

Traditional plastics represent a tremendous threat to the environment because of increases in polluting manufacturing as well as their very extended degradation time. Polyhydroxyalkanoates (PHAs) are polymers with similar performance to plastic but are compostable and synthesizable from renewable sources and therefore could be a replacement for fossil-based plastics. However, their production costs are still too high, thus demanding the investigation of new and cheap substrates. In this sense, agricultural wastes are attractive because they are inexpensive and largely available. Specifically, fruit and vegetables are rich in sugars that could be fermented into PHAs. In this work two strains, Cupriavidus necator DSM 545 and Hydrogenophaga pseudoflava DSM 1034, well-known PHA-producing microbes, were screened for their ability to grow and accumulate PHAs. Ten different fruit and vegetable processing waste streams, never before reported in combination with these strains, were tested. Residues from red apple and melon were found to be the most suitable feedstocks for PHA production. Under specific selected conditions, C. necator DSM 545 accumulated up to 7.4 and 4.3 g/L of 3-hydroxybutyrate (3HB) from red apple and melon, respectively. Copolymer production was also obtained from melon. These results confirm the attractiveness of food processing waste as a promising candidate for PHA production. Ultimately, these novel substrates draw attention for future studies on process optimization and upscaling with C. necator.

Effect of Preharvest and Postharvest Application of 1-MCP on the Quality of Gala Schniga® SchniCo Red(s) Apples during Long-Term Storage

Fruit growers are looking for effective solutions to preserve the high quality of apples after storage. Therefore, the aim of this study was to assess the impact of pre- and postharvest use of 1-MCP on the quality of ‘Gala Schniga® SchniCo Red(s)’ apples harvested at the optimal harvest date and at a delayed harvest date and stored for 9 months under conditions similar to DCA (0.6% CO2 and 0.6% O2). Four treatments: control (1-MCP untreated), 1-MCP treated 7 days before harvest (Harvista™), 1-MCP treated 7 days after harvest (Smart-Fresh™), and 1-MCP treated before and after harvest (Harvista™ + SmartFresh™) were used, respectively, for each of the above harvest dates. After 5, 7, and 9 months of storage and after an additional 7-day shelf-life period, the following properties were determined: flesh firmness, soluble solids content, titratable acidity, and ethylene production rate. The harvest date and the 1-MCP application date affected ethylene production rate which was reflected in fruit flesh firmness and acidity (p &lt; 0.05). Apples collected at the optimal harvest date from trees sprayed with Harvista™ before harvest retained high firmness (&gt;55 N) after 7 days of shelf-storage at 20 °C, even if previously stored for 9 months. Apples collected later showed firmness similar to the best preferred by consumers only if treated with 1-MCP after harvest.

Mycotoxin contamination of fresh berries and fruits marketed in the central region of Russia

New emerging strains of toxigenic molds in agricultural areas and insufficient data on levels of their toxic metabolites occurring in domestic horticultural fruits and berries require risk assessment of MT contamination for this plant group of mass consumer products. This study concentrated on samples of fresh fruits and berries sold on the consumer market (185 samples, including 127 intact and 58 with signs of deformation and molding). We applied our own developed technique for quantification of mycotoxins based on HPLC-MS/MS. In this study, we were the first in the RF to examine contamination of garden strawberries, raspberries, currants, huck-leberries, blueberries, gooseberries, dogwood, plums, blackthorn, apples, pears) with 27 MT including poorly studied emergent MT (EMT), produced by Aspergillus, Penicillium, Fusarium and Alternaria. Strawberries, gooseberries, black currants and raspberries turned out to be the most contaminated with MT; red currants, apples and pears were less contaminated. The greatest variety of MT and EMT species was found in strawberries (23 MT), gooseberries (8 MT), black currants (7 MT) and raspberries (6 MT). Among the regulated MT, fumonisins B1 and B2, deoxynivalenol, zearalenone, T-2 toxin, ochratoxin A and aflatoxin B1 were detected in intact strawberries; patulin, in raspberries; deoxynivalenol and zearalenone, in black currant. As for damaged and moldy berries and fruits, the list of detectable toxins was expanded, primarily due to the detection of several types of unregulated EMTs. EMT tenuazonic acid was mainly detected in moldy berries; its levels increased manifold in almost all species, except for strawberries in which penicillic acid prevailed. These new data on MT contamination in fruits and berries indicate the necessity to perform in-depth hygienic assessment of such products sold on the Russian market to identify MT, EMT and their producers. The obtained results will be used to identify hazards at the first stage in risk assessment with its focus on MT and EMT contamination of fresh fruits and berries.

Оценка потенциальной опасности влияния риск-индуцирующих факторов образовательного процесса и среды обитания на соматическое здоровье учащихся школ различного типа

New emerging strains of toxigenic molds in agricultural areas and insufficient data on levels of their toxic metabolites occurring in domestic horticultural fruits and berries require risk assessment of MT contamination for this plant group of mass consumer products. This study concentrated on samples of fresh fruits and berries sold on the consumer market (185 samples, including 127 intact and 58 with signs of deformation and molding). We applied our own developed technique for quantification of mycotoxins based on HPLC-MS/MS. In this study, we were the first in the RF to examine contamination of garden strawberries, raspberries, currants, huck-leberries, blueberries, gooseberries, dogwood, plums, blackthorn, apples, pears) with 27 MT including poorly studied emergent MT (EMT), produced by Aspergillus, Penicillium, Fusarium and Alternaria. Strawberries, gooseberries, black currants and raspberries turned out to be the most contaminated with MT; red currants, apples and pears were less contaminated. The greatest variety of MT and EMT species was found in strawberries (23 MT), gooseberries (8 MT), black currants (7 MT) and raspberries (6 MT). Among the regulated MT, fumonisins B1 and B2, deoxynivalenol, zearalenone, T-2 toxin, ochratoxin A and aflatoxin B1 were detected in intact strawberries; patulin, in raspberries; deoxynivalenol and zearalenone, in black currant. As for damaged and moldy berries and fruits, the list of detectable toxins was expanded, primarily due to the detection of several types of unregulated EMTs. EMT tenuazonic acid was mainly detected in moldy berries; its levels increased manifold in almost all species, except for strawberries in which penicillic acid prevailed. These new data on MT contamination in fruits and berries indicate the necessity to perform in-depth hygienic assessment of such products sold on the Russian market to identify MT, EMT and their producers. The obtained results will be used to identify hazards at the first stage in risk assessment with its focus on MT and EMT contamination of fresh fruits and berries.

Using Popular Foods Consumed to Inform Development of Digital Tools for Dietary Assessment and Monitoring.

Knowing the type and quality of the most popular foods consumed by a population can be useful in the design of technologies for monitoring food intake and interventions. The aim of this research was to determine the most frequently consumed foods and beverages among the Australian population and provide recommendations for progressing the design of dietary assessment technologies. Analysis of the first 24 h recall of the most recent Australian National Nutrition and Physical Activity Survey was conducted. The most popular foods and beverages consumed by energy (kJ) and by frequency were calculated. There were 4515 separate foods and beverages reported by 12,153 people. Overall, the top 10 foods that contributed most energy included full fat milk, beer, white rice, white bread, red wine, cola soft drinks, bananas, red apples, wholewheat breakfast cereal and white sugar. The five most frequently reported foods and beverages were tap water, black tea, full fat milk, instant coffee, and sugar. Understanding the most popular foods and beverages consumed can support innovations in the design of digital tools for dietary surveillance and to reduce under-reporting and food omissions. These findings could also guide the development of more tailored and relevant food databases that underpin these technologies.

KLASIFIKASI BUAH SEGAR DAN BUSUK MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DENGAN TFLITE SEBAGAI MEDIA PENERAPAN MODEL MACHINE LEARNING

CNN (Convolutional Neural Network) is one of the type of Deep Learning algorithm that can accept input in the form of images, or objects such as fruit, animals, office equipment, and so on. This CNN works is inspired by the neuron system in humans based on visual perception. The CNN model can be implemented into the Android system using TFLite. TFLite is a Machine Learning library specially designed for Android devices to create Machine Learning models in small binary and low latency. This can make it easier for Machine Learning researchers to use smartphones to develop the research system. Current technological advances make the fruit industry no longer use the manual fruit sorting process carried out by humans. The process of sorting fruit that is fit for consumption (fresh) and unfit for consumption (rotten) can reach hundreds and thousands, so it needs the help of machines to increase work speed. This fruit sorting process can be circumvented by creating a Machine Learning model that can classify fresh and rotten fruit which will then be processed by the system based on the desired output requirements. If the model can perform the classification well, then the fruit industry can take steps to make an automatic sorter by implementing the model into its system. This study aims to create a model with the CNN (Convolutional Neural Network) Algorithm and design a prototype system that can classify fresh and rotten fruit (red apples, bananas, and mandarin oranges) and apply the resulting model using TFLite on an Android Smartphone. The results indicated that the resulting prototype model can be used by users in the Android system. The results of the training model obtained an accuracy value of 98.20%. Meanwhile, the results of testing showed that the model can classify images from the same or different sources by 91.65% from 1090 images. These results indicate that the model is included in the excellent classification category.

Non-Destructive Measurement of Quality Parameters of Apple Fruit by Using Visible/Near-Infrared Spectroscopy and Multivariate Regression Analysis

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, rc = 0.991, SDR = 2.51) and for red apples (RMSEC = 0.005, rc = 0.998, SDR = 2.56). The results for TA were also (RMSEC = 0.003, rc = 0.996, SDR = 2.51) for red apples and (RMSEC = 0.001, rc = 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, rc = 0.989, SDR = 2.05) and for yellow apples (RMSEC = 1.457, rc = 0.998, SDR = 1.61). The obtained results indicate the detection of the mentioned parameters with high accuracy by visible/infrared spectroscopic technology.

The difference in consumer attitudes of locally grown apples with imported apples. the case of Korca Region, Albania

What motivates consumers to act differently from each other has been studied and will continue to be studied in order for marketers and consumer attitudes researchers to better understand their needs and, consequently, to satisfy them as best as possible. Figuring out why someone likes a red apple and someone likes a green apple can be a difficult process, but it's still easier than understanding why people choose an imported apple over a domestic apple when no verifiable qualities distinguish them. In attempt to analyze this invisible element and to evidence it, along with other factors such as familiarity with the product, knowledge of the product, physical characteristics, etc. in this study we will prove that there is another evident element and measurable such as the credence attribute for locally produced apples in the Korca area. For the study, 335 random interviews were conducted with apple consumers in the Korca region, providing demographic data, data on consumption, data on knowledge about apples, the attitudes of the interviews about issues related to apples and especially about cultivated apples in Korca. Full analysis was done for descriptive statistical data. Exploratory factor analysis and confirmatory factor analysis were done to group attitudes into fewer factors. The attitudes of the interviewees were grouped into 3 factors, attitudes about the physical qualities of the apple, subjective knowledge and credence attributes of the locally grown apple. Quantitative analysis was done using statistical models binary logistic equation SPSS 26 programs. From this analysis and examination of the relationships of the factors with each other and with other variables in the study, the raised hypotheses of this study were confirmed, Consumers who buy imported apples have a low rating for the Credence attribute "Apple cultivated in the Korca region" and for Subjective knowledge regarding apples.

Apple Polyphenol Diet Extends Lifespan, Slows down Mitotic Rate and Reduces Morphometric Parameters in Drosophila Melanogaster: A Comparison between Three Different Apple Cultivars.

Plant-derived polyphenols exhibit beneficial effects on physiological and pathological processes, including cancer and neurodegenerative disorders, mainly because of their antioxidant activity. Apples are highly enriched in these compounds, mainly in their peel. The Tuscia Red (TR) apple variety exhibits the peculiar characteristic of depositing high quantities of polyphenols in the pulp, the edible part of the fruit. Since polyphenols, as any natural product, cannot be considered a panacea per se, in this paper, we propose to assess the biological effects of TR flesh extracts, in comparison with two commercial varieties, in a model system, the insect Drosophila melanogaster, largely recognized as a reliable system to test the in vivo effects of natural and synthetic compounds. We performed a comparative, qualitative and quantitative analysis of the polyphenol compositions of the three cultivars and found that TR flesh shows the highest content of polyphenols, and markedly, anthocyanins. Then, we focused on their effects on a panel of physiological, morphometrical, cellular and behavioral phenotypes in wild-type D. melanogaster. We found that all the apple polyphenol extracts showed dose-dependent effects on most of the phenotypes we considered. Remarkably, all the varieties induced a strong relenting of the cell division rate.