Green Apple Research Articles

SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard.

Because of the unstructured characteristics of natural orchards, the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture. Therefore, an innovative fruit segmentation method based on deep learning, termed SE-COTR (segmentation based on coordinate transformer), is proposed to achieve accurate and real-time segmentation of green apples. The lightweight network MobileNetV2 is used as the backbone, combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features. In addition, joint pyramid upsampling module is optimized for integrating multiscale features, making the model suitable for the detection and segmentation of target fruits with different sizes. Finally, in combination with the outputs of the function heads, the dynamic convolution operation is applied to predict the instance mask. In complex orchard environment with variable conditions, SE-COTR achieves a mean average precision of 61.6% with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales. Especially, the segmentation accuracy for small target fruits reaches 43.3%, which is obviously better than other advanced segmentation models and realizes good recognition results. The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard.

Implement Improved Process Design Using a Lightweight Deep Learning Model to Reduce Hardware Computational Load in Instance Segmentation: Using Apple Dataset

The use of deep learning using image data has already made a lot of progress. The main function is to track or classify related objects with the model of the data set in which the learning has been conducted in the image and it is used in various fields. Therefore, the accuracy has improved a lot, but the proportion of hardware use increases rapidly because the depth of the model is increased and utilized. In this paper, we propose a process to improve results by using the lightweight model and additional procedures in the program in systems using deep learning. In the proposed system, an additional image processing process is performed to increase accuracy in the results after deep learning processing, unlike other systems. It uses residual CPU usage in desktop or embedded environments with GPU, maintains processing speed, and introduces additional image processing processes to mitigate graphic memory usage using lightweight deep learning models. As a result, a system that improves the accuracy of the object area by 6% based on red apples and 16% based on green apples can be designed to reduce hardware costs and increase performance.

Novel green-fruit detection algorithm based on D2D framework

In the complex orchard environment, the efficient and accurate detection of object fruit is the basic requirement to realize the orchard yield measurement and automatic harvesting. Sometimes it is hard to differentiate between the object fruits and the background because of the similar color, and it is challenging due to the ambient light and camera angle by which the photos have been taken. These problems make it hard to detect green fruits in orchard environments. In this study, a two-stage dense to detection framework (D2D) was proposed to detect green fruits in orchard environments. The proposed model was based on multi-scale feature extraction of target fruit by using feature pyramid networks MobileNetV2 +FPN structure and generated region proposal of target fruit by using Region Proposal Network (RPN) structure. In the regression branch, the offset of each local feature was calculated, and the positive and negative samples of the region proposals were predicted by a binary mask prediction to reduce the interference of the background to the prediction box. In the classification branch, features were extracted from each sub-region of the region proposal, and features with distinguishing information were obtained through adaptive weighted pooling to achieve accurate classification. The new proposed model adopted an anchor-free frame design, which improves the generalization ability, makes the model more robust, and reduces the storage requirements. The experimental results of persimmon and green apple datasets show that the new model has the best detection performance, which can provide theoretical reference for other green object detection. Keywords: green-fruit detection, D2D framework, automatic harvesting, MobileNetV2+FPN, binary mask prediction, anchor-free DOI: 10.25165/j.ijabe.20221501.6943 Citation: Wei J M, Ding Y H, Liu J, Ullah M Z, Yin X, Jia W K. Novel green-fruit detection algorithm based on D2D framework. Int J Agric & Biol Eng, 2022; 15(1): 251–259.

Study of life cycle of green apple aphid (aphis pomi de geer) infesting apple orchards in Kashmir valley

Keeping in view the havoc caused by green apple aphid on Apple industry in last couple of years and observing the resistance against insecticides in this pest. Severe infestation causes curling, stunting and weakening of terminals and increase risk of winter mortality. A common strategy to control this pest in conventional apple orchards in Kashmir is the use of one or more application of insecticides. But wide spectrum insecticides have led to resistance and resurgence in this pest. It also upsets natural biodiversity and affects population abundance of predators This study was devised and aimed at determining the life history of this devastating pest during May to November months on apple host. The data on total life period (pre-reproductive, reproductive and post-reproductive periods) was recorded. The total progeny produced and total nymphal period was also calculated. The detailed biology of the pest has been recorded for the first time in Apple orchards of Kashmir province of J&K. This study will be of immense importance in carving out a sustainable future IPM strategy against this pest.

Efficiency of insecticides against the sucking phyllophages in apple orchards on drip irrigation in the Eastern Forest-Steppe of Ukraine

The effect of insecticides on the main pests of apple leaves has been studied. Nine species of phyllophagous insects have been identified. The green apple aphid (Aphis pomі (De Geer, 1773)) and the apple leaf midge (Dasineura mali (Kieffer, 1904)) were of economic importance. Insecticides of different chemical classes (neonicotinoids and ketoenols) were used against the pests. The studied chemical preparations had a high protective effect against the aphids and the gall midge larvae. Movento 100SC, CS (2.0 l/ha) was the most effective insecticide, it had a technical efficiency ranges from 92.6 to 100.0% against the aphid and 86.5–99.1% against the gall midge larvae. The effect of insecticides on the pests varied by year and apple variety. The following averaged data on technical efficiency against the aphid have been obtained: Mospilan, SP (0.3 kg/ha) — 83.3–100.0%, Tamer, SP (0.3 kg/ha) — 90.0–96.4%, Koginor, SL (0.3 l/ha) — 91.3–98.5%, Konfidor 200 SL (0.3 l/ha) — 91.4–97.1%. These insecticides was effectively of 81.1–98.2, 88.9–97.8, 82.8–97.0, and 83.3–97.1% respectively against the larvae of the apple leaf gall midge.

Parsing Intra- and Inter-Individual Covariation Between the Sensory Attributes and Appeal of E-Cigarettes: Associations and Gender Differences.

Perceived sensory attributes of e-cigarettes may associate with their appeal. However, limited studies have accounted for individuals' variability in sensory attributes or have addressed how associations of sensory attributes with appeal may differ by gender. Individuals (n = 119, 32.8% female) who currently used combustible cigarettes and/or e-cigarettes attended one laboratory session in which they completed a standardized e-cigarette puffing procedure according to a 10 Flavor (green apple, strawberry, chocolate, vanilla, menthol, koolada, peppermint, spearmint, subtle tobacco, and full-flavored tobacco) × 2 Nicotine Formulation (free-base, salt) double-blind factorial design. The mean nicotine concentration was 23.4 (SD = 0.9) mg/mL in the nicotine salt formulations and 23.8 (SD = 1.7) mg/mL in the free-base formulations. Following each trial, participants completed ratings of sensory attributes (sweet, smooth, cool, bitter, harsh) and appeal (mean of liking, disliking [reverse-scored], and willingness-to-use-again ratings). Sensory attributes were partitioned into between-person and within-person variables. Gender was tested as a moderator of associations of sensory attributes with appeal. Sweet, smooth, and cool sensory attributes positively associated with appeal at the between- and within-person level (ps < .001). Bitter and harsh negatively associated with appeal at the between- and within-person level (ps < .001). The associations of between-person sweet, smooth, and cool ratings with appeal was larger in males compared to females. The associations of within-person smooth, bitter, and harsh with appeal was larger in females compared to males. This study showed important gender differences in associations of sensory attributes and appeal. While evidence suggests that sensory attributes may contribute to the appeal of e-cigarettes, there is little experimental evidence accounting for individual variability in sensory attributes and whether sensory attribute-appeal associations differ by gender. The current study provides evidence that average sweet, cool, and smooth ratings positively associated with appeal and that these associations were larger in males. Within-person bitter, harsh, and smooth ratings significantly associated with appeal in both genders, but these associations were larger in females compared to males. Data from the current report reinforces the need for researchers to study gender stratified effects in tobacco regulatory science.

Sensory Specific Desires. The Role of Sensory Taste Exposure in Desire for Food with a Similar or Different Taste Profile.

The present study investigated how the sensory taste profile of a meal altered the subjective desire, wanting and liking of foods with a sweet, salty, sour, bitter, fatty, and spicy sensory profile, respectively. Participants (n = 85) ate a meal with a pronounced sensory taste profile: (1) sweet, (2) salty, or (3) sweet and salty combined. Self-reports of appetite, pleasantness, and sensory specific desires (SSD) were evaluated over the course of the meal using VAS-scales. SSDs were further studied through alterations in liking and desire for food samples with the main sensory profile being sweet (peach), salty (pretzel), sour (green apple), bitter (dark chocolate), fatty (whipped cream), and spicy (chilli nut), respectively. Consumption of food with a pronounced sensory taste profile was found to suppress the desire for food with a similar sensory taste profile, while the desire for different sensory profiles were enhanced or not affected. Further, when exposed to two pronounced tastes within the same meal, suppression of sensory desires was not only specific for the exposure tastes but tended to go beyond the sensory exposure. The findings suggest that taste variation within a meal holds the potential to create more satisfying meals, which can hinder additional desires after a meal and thus, lower additional calorie intake.

The effects of inhaled flavors on intravenous nicotine.

Menthol is the only available flavor in combusted tobacco cigarettes; however, e-cigarettes are available in thousands of flavors. Research on flavors and rewarding properties of nicotine is limited. The present study sought to examine the acute rewarding effects of flavors inhaled from an e-cigarette, in combination with intravenous (IV) nicotine among cigarette smokers. In the present study, 24 menthol-preferring young adult (aged 18 to 30) cigarette smokers were tested under 3 different e-cigarette flavor conditions (menthol, green apple, or menthol + green apple) in a within-subject cross-over design. During each test session, each participant received 3 IV infusions (saline, 0.25 mg/70 kg nicotine, 0.5 mg/70 kg nicotine) administered 1 hr apart. The main outcome measures assessed cardiovascular, subjective, and cognitive domains. Compared with green apple or green apple + menthol, menthol produced higher ratings of "cooling" (ps < 0.01). Craving was rated higher following administration of green apple and the combined menthol + apple flavor compared to menthol alone (ps < 0.05). As expected, IV-nicotine dose-dependently increased the ratings of subjective liking/disliking and peak heart rate, improved cognitive performance, and reduced smoking urges (all ps < 0.05). These subjective, cognitive, and physiological effects of nicotine were not affected by any flavor condition. The present findings did not support an interaction between IV-nicotine dose and inhaled flavor for acute effects of nicotine. Green apple flavor, alone or in combination with menthol, could result in higher craving or insufficiently alleviate craving, relative to menthol flavor alone. Additional research is warranted to examine extended exposure to inhaled flavors on the rewarding and addictive effects of nicotine. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Speaking My Mind: Responding to diversity with the green apple strategy

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Efficient Fruits Classification Using Convolutional Neural Network

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

FoveaMask: A fast and accurate deep learning model for green fruit instance segmentation

In the process of agricultural automation production, efficient and accurate segmentation of target fruit is the basis and guarantee for numerous applications including crop growth monitoring, yield prognosis, and machine picking. Green fruit is apt to affect by complicated scenes such as occlusions and overlaps, as well as the homo-chromatic background, which brings great challenges to the recognition and segmentation. In this paper, we propose a novel methodology named FoveaMask for improving the robustness and generalization of green fruit segmentation framework. Features of input images are firstly extracted by ResNet and fused by FPN. The classification and bounding-box regression of each spatial position on the feature maps are then carried out directly by the way of full convolution. RoI Align layer is applied to fix feature region of proposals to the same size yet preserve exact spatial locations. Finally, instance-level fruit segmentation is realized by pixel-level classification on each proposal using embedded mask branches. The whole network architecture of the new model does not involve the related design and operation of anchor, which greatly improves the generalization ability for different shape fruits, alleviates the computing and storage resources, and balances the contradiction between accuracy and efficiency simultaneously. Additionally, a position attention module(PAM) is introduced into the embedding mask branch to aggregate the effective information pixels, which can improve the robustness of the segmentation model in the actual complex environment of the orchard. We test the model on green apple and immature persimmon data sets and the experimental results show that FoveaMask performs best in terms of both recognition accuracy and model complexity compared with other 11 different types of detection and segmentation models.

Characterization of Unmalted Barley Treated with Aspergillus oryzae

Koji is a complex of substrate and the filamentous fungus Aspergillus oryzae. It is used for saccharification in fermented foods and beverages. Its role in co-fermentations is known, but its application in beer brewing contexts is unclear. The goal of this study was to determine what effect A. oryzae has on the characteristics of unmalted barley in brewing contexts, if it might bypass traditional malting, and what sensory attributes a koji beer might exhibit. Koji barley was prepared with pearled barley and a red rice koji strain and compared to untreated pearled barley and malted barley control groups. The total production time of koji barley was nine days and the process consumed 4 ± 2 L water per kg of grain. Coarse grind extract in koji barley was three times greater than pearled barley while approximately ten percent less than malted barley. Diastatic power was over three times greater in malted barley versus koji barley, while no significant difference (p < 0.05) was found between koji barley and untreated pearled barley. Koji barley congress wort exhibited three times more glucose but less than half the maltose content of malted barley wort. Analysis of wort organic acids showed increased concentrations of citric, succinic, and kojic acid in koji barley versus pearled barley. Qualitative sensory analysis of fermented koji barley wort identified moderate tropical, citrus, and sour flavors as well as green apple and tropical fruit aromas. The results demonstrated that koji barley shows characteristics similar to specialty malts and may favor hazy, fruited, and sour beer styles.

A novel HPLC method for simultaneous quantification of eleven phenolic antioxidants in commercial personal care and food products which contain extracts of green apple, pomegranate and argan oil

Abstract In this study, a simple, fully validated and rapid reversed-phase HPLC with photodiode array detector method was developed for the simultaneous determination of 11 selected phenolic antioxidants over 33 min in personal care and food samples containing extracts of green apple, pomegranate (Ponica granatum) and argan oil (Argania spinosa). The method was performed using NUCLEODUR C18 column 5 µm particle size and 12.5 cm length. The HPLC mobile phase was prepared as follows, solution A: 1% aqueous acetic acid and solution B: Acetonitrile. The method was gradient at flow rate 1.0 mL/min with a simple mobile phase, minimal sample preparation, and diminished organic solvent usage (3% acetonitrile for almost 90% of the run time). The detection was carried out at 278 nm. The method presented good precision and accuracy with RSD% values ranged between 0.33 and 1.94% and wide linear range. The developed method was successfully applied on 67 personal care and food products present in Egyptian market and can be used for routine screening in laboratory for the regular quality control of the antioxidant content for products containing the mentioned extracts.

The use of green apple and beet-root as natural radio-protectors against health effects of ionizing radiation

Objective: Evaluation of the antioxidant protective role of beetroot and apple to reduce the impact of ionizing radiation on the blood components. Subjects and Methods: Sixty adult white male rats were evenly divided into four experimental groups, Group 1 (n= 15) was used as a control group. Groups 2, 3, and 4 (n=15 each) were injected intravenously with radioactive material Tc-99m 130 ± 5 Mega Becquerel )MBq( (5 ± 0.5 Micro Curry )mCi)( as a source of ionizing radiation (gamma rays). Groups 2 and 4 were fed with apple fruit and beet seeds in addition to a small amount of commercial food. Rats in G3 were fed with commercial food pellets only and received the same volume of water. Rats in groups G1, G2, and G3 were sacrificed after7 days while those of G4 were sacrificed after 30 days. Osmotic fragility of RBCs and hematological parameters were investigated for each group. The structure characteristics of the blood cells for all groups were studied by recording the UV -Vis spectra. Results: The study revealed statistically significant changes regarding RBCs indices along with increased osmotic fragility among rats of group 3 compared to those in groups 1, 2, and 4. The U.V. spectrum revealed a partial irregularity in the RBCs. Conclusion: Our study pointed out the protective role of natural products namely, beetroots and green apple extracts, against the adverse impact of ionizing radiation on blood indices.

Expression analysis of MYB genes in different apple cultivars with distinct coloration pattern during fruit maturation

Abstract Anthocyanins are water-soluble pigments responsible for red, pink, purple or blue coloration in the flesh and/or skin of apple fruit. Since consumers prefer anthocyanin rich fruits due to their health benefits, anthocyanin content is an important trait for marketability of apples. Synthesis and accumulation of anthocyanins are controlled by environmental and genetic factors such as transcription factors (TFs). Two-repeat (R2R3) MYB TFs are involved in the regulation of anthocyanin biosynthesis in plants. In this study, the expression of the MdMYBA and MdMYB3 genes encoding R2R3 type MYB TFs were analyzed in apple cultivars with fruit skin color from green to dark red at different growth stages. Fruit samples were collected from “Scarlet Spur”, “Galaxy Gala”, Golden Delicious” “Granny Smith” apple cultivars with dark red, light red, yellow and green fruits, respectively, at four different time periods after full-bloom. Total RNA was isolated from the peel of the collected fruits and the expression of the MdMYBA and MdMYB3 genes was analyzed by real-time RT-PCR. The expression of the MdMYBA gene started to increase at 92 DAFB and thereafter in red-skin apples; however, no expression was observed at any time points in yellow or green-skin apple cultivars. On the other hand, the MdMYB3 gene was expressed in red-skin and yellow-skin apple cultivars starting at 55 DAFB, but no expression was observed in green apple cultivar at any time of fruit maturation. The expression levels of the MdMYBA and MdMYB3 genes varied in apple fruits with different skin coloration indicating that they play a role regulation of the anthocyanin biosynthesis in apple cultivars during fruit maturation.

Influence of menthol and green apple e-liquids containing different nicotine concentrations among youth e-cigarette users.

E-cigarettes are popular among adolescents. Given that flavors enhance e-cigarette appeal, this study examined the influence of flavors on nicotine in e-cigarettes. Youth e-cigarette users (average 26.2 days [SD = 3.6] in past 28 days) were randomized to use e-cigarettes containing 6 or 12 mg/mL of freebase nicotine and completed 4 test sessions. During the first 3 test sessions, participants completed 3 fixed puffing bouts (1 puffing bout = 10 puffs, 3 s each, 30-s interval), using menthol, green-apple, and unflavored e-liquids (50 propylene glycol [PG]/50 vegetable glycerin [VG]) with their assigned nicotine concentration in a random order using a ∼5.5-W V2 e-cigarette device. After each puffing bout, participants assessed subjective effects of nicotine and flavor. In the 4th test session, participants used any of the e-liquids they had tried in the earlier sessions, ad libitum for 60 min and the amount of e-liquid used for each flavor and the number of puffs was assessed. Participants (n = 49; 6 mg/mL [n = 24]; 12 mg/mL [n = 25]) were 63.3% male, 65.3% non-Hispanic White with an average age of 18.7 (SD = 0.9). Mixed models analysis revealed that green apple and 6 mg/mL of nicotine independently increased liking of e-cigarette taste. In addition, green apple produced higher ratings of fruitiness, sourness, sweetness, and menthol produced higher ratings of coolness. We did not observe any interactions between nicotine and flavor. Youth liked the taste of e-liquids containing green-apple flavor or low nicotine concentration which highlights the appeal of fruit flavors in e-cigarettes to adolescents. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Metabolome and transcriptome profiling provide insights into green apple peel reveals light- and UV-B-responsive pathway in anthocyanins accumulation

BackgroundIn nature, green apple are associated with the accumulation of chlorophyll, while red apple varieties are associated with anthocyanins accumulation. Notably, in this study, the green skin color apple variety ‘white winter pearmain’ treated with ultraviolet-B (UV-B) exhibited red skins and marked anthocyanin accumulation, while visible light could not. But there are few reports on the biosynthesis difference of anthocyanins in green apple by visible light and UV-B-treatment. Here, we explored the difference of metabolites and genes expression level in green apple by transcriptomic and metabolic.ResultsThe metabolic analysis revealed that there were 152 and 178 significantly changed metabolites in the visible light and UV-B-treated green apple, respectively, compared to the control, and flavone, flavonol, and anthocyanin were the most significantly increased; and transcriptomic analysis showed that 37,110 and 37,709 differentially expressed genes, including 382 and 475 transcription factors (TFs) were detected in light and UV-B-treatment fruit, respectively. Quantitative reverse transcription PCR (qRT-PCR) results confirmed changes in the expression levels of genes encoding metabolites involved in the flavonoid synthesis pathways. The flavonoid metabolic flux in the UV-B treatment increased the accumulation of cyanidin 3-glucoside and cyanidin 3, 5-diglucoside compared to under the light-treatment. Furthermore, we performed qRT-PCR analysis of anthocyanin biosynthesis genes and predicted the gene of MD00G1134400 (a UDP glucose-flavonoid 3–0-glucosyltransferase) may be a candidate gene for anthocyanins accumulation and highly expressed in UV-B-treatment fruit. Expression profiles of several transcription factors of the families MYB, bHLH, NAC were highly correlated with the content of the anthocyanin.ConclusionsThe composition and contents of anthocyanins in green apple in UV-B-treatment very greatly. A series of metabolites and candidate genes were revealed through combined analysis of metabolome and transcriptome. These results provide an important data for dissecting candidate genes and molecular basis governing green apple color formation in response to visible light and UV-B light.

Species-Specific Induction of Plant Volatiles by Two Aphid Species in Apple: Real Time Measurement of Plant Emission and Attraction of Lacewings in the Wind Tunnel

Upon damage by herbivores, plants release herbivory-induced plant volatiles (HIPVs). To find their prey, the pest’s natural enemies need to be fine-tuned to the composition of these volatiles. Whereas standard methods can be used in the identification and quantitation of HIPVs, more recently introduced techniques such as PTR-ToF–MS provide temporal patterns of the volatile release and detect additional compounds. In this study, we compared the volatile profile of apple trees infested with two aphid species, the green apple aphid Aphis pomi, and the rosy apple aphid Dysaphis plantaginea, by CLSA-GC–MS complemented by PTR-ToF–MS. Compounds commonly released in conjunction with both species include nonanal, decanal, methyl salicylate, geranyl acetone, (Z)-3-hexenyl acetate, (Z)-3-hexenyl butanoate, (Z)-3-hexenyl 2-methyl-butanoate, (E)-β-caryophyllene, β-bourbonene and (Z)-3-hexenyl benzoate. In addition, benzaldehyde and (E)-β-farnesene were exclusively associated with A. pomi, whereas linalool, (E)-4,8-dimethyl-1,3,7-nonatriene were exclusively associated with D. plantaginea. PTR-ToF–MS additionally detected acetic acid (AA) and 2-phenylethanol (PET) in the blends of both trees attacked by aphid species. In the wind tunnel, the aphid predator, Chrysoperla carnea (Stephens), responded strongly to a blend of AA and PET, much stronger than to AA or PET alone. The addition of common and species-specific HIPVs did not increase the response to the binary blend of AA and PET. In our setup, two host-associated volatiles AA + PET appeared sufficient in the attraction of C. carnea. Our results also show the importance of combining complementary methods to decipher the odor profile associated with plants under pest attack and identify behaviourally active components for predators.

Electronic Nicotine Delivery Systems (ENDS) Flavorants Act As Non‐Competitive Antagonists of nAChRs and Contribute To Reinforcement‐Related Behavior

Significance Approximately 42% of the American population who use ENDS are between the ages of 12 and 18. The dramatic rise in adolescent ENDS use is likely attributed to the continuously growing flavor market, with more than 15,000 options available. In an attempt to combat this rise in vaping, it is crucial we determine the pharmacological impact of ENDS flavorants on nicotinic acetylcholine receptors (nAChRs) in the addiction-related circuitry of the brain. Methods We utilized cultured neuroblastoma-2a cells transiently transfected to contain α4β2 and α6β2β3 nAChRs and Ca2+ influx assays to examine the pharmacology of ENDS flavorants on nAChR function. In parallel, flavorants were also examined for their impact on nAChR upregulation using confocal microscopy. Lastly, we utilized vapor self-administration assays with male and female mice to study how these flavorants alter reinforcement-related behaviors. To determine significance, we used one- and two-way ANOVAs with appropriate post-hoc analyses. Results Menthol and two green apple flavorants (farnesol and farnesene) exhibited pharmacology consistent with the designation of NCAs on α4β2 nAChRs (IC50 values of 50–100μM). Further, both menthol and farnesol induced nAChR upregulation, whereas farnesene uniquely induced a stoichiometric shift towards high-sensitivity nAChRs. In self-administration assays, we observed that male mice self-administered menthol + nicotine, green apple + nicotine, or green apple alone, and both green apple and menthol enhanced nicotine reinforcement-related behavior. However, only green apple flavorants contributed to reinforcement-related behavior in the absence of nicotine. Conclusions Although ENDS flavors have been deemed “generally recognized as safe” by the FDA, we have identified several chemical flavorants that act as NCAs of α4β2 nAChRs and exert an impact on reinforcement-related behavior in a mouse model of vaping-related behavior. Given the constant rise in flavored ENDS use, further studies on flavors, ENDS devices, and the underlying mechanisms that take place while using these products is necessary to contribute in the regulation of ENDS use.

Effect of the Addition of Non-Saccharomyces at First Alcoholic Fermentation on the Enological Characteristics of Cava Wines

Background: Cava is considered to be a high-quality wine internationally. Hence, it has undergone consistent improvement and/or the preservation of its aromatic qualities, bouquet, color, and foamability, throughout its elaboration and aging. Methods: This study investigates the use of different Saccharomyces and non-Saccharomyces yeasts strains (Torulaspora delbrueckii and Metschnikowia pulcherrima) in Chardonnay and Xarel.lo cava wines. The usual enological parameters, the volatile composition, the protein contents, and foamability were determined, and sensory analyses were also performed for all of the vinifications (both before tirage and after 18 months of aging on the lees). Results: the protein and foamability results show that there is a direct relationship between both parameters, with better foam persistence achieved in some non-Saccharomyces fermentation. M. pulcherrima base wines showed a high protein content, improving foamability and foaming persistence. In addition, the results of the aromatic composition and the sensory analysis showed that the use of T. delbrueckii at first fermentation produced interesting cavas from an aromatic perspective. These cavas showed the highest values of ethyl isovalerate (120–126 µg/L), providing aromatic fruity notes, especially fresh green apple. Conclusions: the use of non-Saccharomyces yeasts in the base wine fermentation can be an alternative to produce cavas with differentiated aromatic characteristics and interesting foaming ability.