Recent advances in the study of epitopes,allergens and immunologic cross-reactivity of edible mango

Mango(Mangifera indica L.)is a tropical fruit that is widely consumed as both fresh fruits and processed products around the world.The high incidence of mango allergy,on the other hand,has sparked widespread concern.Therefore,a summary and analysis of the current status and issues in mango allergen research can guide in-depth study on the mechanism of mango allergy and reveal effective desensitization methods.We described the incidence of fruit allergy,as well as the mechanism and clinical symptoms of mango allergy,in this review.We also looked into the structural properties of mango allergens,the effect of processing methods on mango allergens,prediction methods for mango allergen epitopes,and the current state of research on mango cross-reactive allergens and preventive measures.Finally,the research directions and ideas for the future are proposed and discussed.

Technical-Economic Performance, Carcass and Meat Quality of COBB500 Broilers Fed Rations Incorporating Mango By-Product Feeds in Ivory Coast (West Africa)

The study evaluated the growth performance and carcass quality of COBB500 broilers fed two experimental rations incorporating feed and mango fine ED10 for a batch of 83 birds, ED20 (batch of 83 birds) in comparison with a commercial control ration CCD (84 birds). For this purpose, 250 day-old chicks of this breed were purchased in Korhogo and transported to Tengréla in Ivory Coast for rearing. The results show that substituting mango provender ans and mango seed flour for maize resulted in rations with costs per kgMS of 215.35FCFA, 2016.93FCFA and 320FCFA for the ED10, ED20 and CCD rations respectively. The production costs per kg of chicken (FCPkgLW) of the CCD ration were significantly higher than those of the ED10 and ED20 (p 0.05). ADGs were significantly different and decreasing from CCD, ED20 to ED10 (p 0.05). Average weights at 42nd day for ED20 and CCD were homogeneous (p > 0.05) and significantly higher than for ED10 (p 0.05). By day 49th the average weight of ED20 had reached and exceeded that of CCD by day 42nd. PSLW, HCW and CCW were significantly different and decreasing from CCW, ED20 to ED10 (p 0.05). Average hot (HCY) and cooled (CCY) carcass yields of ED20 and CCD rations were comparable (p > 0.05) and significantly higher than those of ED10 ration (p = 0.009) and 0.003). Average drumstick and neck weights were significantly higher and lower for CCD, ED20 and ED10 (p 0.05). For all other parts of the cut, the average weights of the CCD ration were significantly higher (p 0.05) than those of the ED20 and ED10 rations, which were homogeneous (p 0.05). The incorporation of mango provender and mango seed flour in broiler rations offers the prospect of reduced production costs, less competition between man and animals over cereals, and lean meat production that is more appreciated than that of chickens fed conventional feed. As it stands, the ED20 ration can be recommended as a growth diet for broilers. It would be necessary to determine the levels of anti-nutritional substances in mango feeds and to assess the in vivo digestibility of mango ingredients and the diets incorporating them.

Study on Compound Stabilizer Formulation for Mango Juice with Skin

[Objectives]The effects of stabilizers such as sodium carboxymethyl cellulose,pectin,sodium alginate and xanthan gum on the stability of mango juice with skin were discussed using mango as the raw material.[Methods]The formula and stability of mango beverage with skin were studied using mango juice with skin as the raw material.Four stabilizers,including pectin,sodium alginate and sodium carboxymethyl cellulose,were added to improve the stability of the juice.Based on single factor experiments and a response surface experiment,the stability model of mango juice was established to determine the best compound stabilizer.[Results]According to an orthogonal experiment on the stability of mango juice with skin,the optimum technical parameters were as follows:sodium carboxymethyl cellulose 0.20%,xanthan gum 0.08%,sodium alginate 0.18%,and pectin 0.14%,with which the optimal suspension stability was 69.12%.The optimum technical parameters from the response surface experiment were as follows:sodium carboxymethyl cellulose 0.199%,pectin 0.1185%,sodium alginate 0.163%,and xanthan gum 0.077%,with which the suspension stability was 70.32%.It was found that the stability of mango juice with skin obtained by the response surface experiment was better than that by the orthogonal experiment,and the order of factors affecting the stability of mango was sodium alginate(C)>sodium carboxymethyl cellulose(A)>pectin(D)>xanthan gum(B).[Conclusions]The formula of compound stabilizer for mango juice was optimized by the orthogonal experiment and the response surface method,providing a theoretical basis for the actual production of mango juice with skin.

Hexavalent Chromium Cr (VI) Removal from Water by Mango Kernel Powder

Metal trace elements (MTE) are among the most harmful micropollutants of natural waters. Eliminating them helps improve the quality and safety of drinking water and protect human health. In this work, we used mango kernel powder (MKP) as bioadsorbent material for removal of Cr (VI) from water. Uv-visible spectroscopy was used to monitor and quantify Cr (VI) during processing using the Beer-Lambert formula. Some parameters such as pH, mango powder, mass and contact time were optimized to determine adsorption capacity and chromium removal rate. Adsorption kinetics, equilibrium, isotherms and thermodynamic parameters such as ΔG˚, ΔH˚, and ΔS˚, as well as FTIR were studied to better understand the Cr (VI) removal process by MKP. The adsorption capacity reached 94.87 mg/g, for an optimal contact time of 30 min at 298 K. The obtained results are in accordance with a pseudo-second order Freundlich adsorption isotherm model. Finally FTIR was used to monitor the evolution of absorption bands, while Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) were used to evaluate surface properties and morphology of the adsorbent.

An Application of Machine Learning Methods to Detect Mango Varieties

The mango, a fruit of immense economic and dietary significance in numerous tropical and subtropical regions, plays a pivotal role in our agricultural landscape. Accurate identification is not just a necessity, but a crucial step for effective classification, sorting, and marketing. This study delves into the potential of machine learning for this task, comparing the performance of four models: MobileNetV2, Xception, VGG16, and ResNet50V2. These models were trained on a dataset of annotated mango images, and their performance was evaluated using precision, accuracy, F1 score, and recall, which are standard metrics for image classification. The Xception model, with its exceptional performance, outshone the other models on all performance indicators. It achieved a staggering accuracy of 99.47%, an F1 score of 99.43%, and a recall of 99.43%, showcasing its remarkable ability to accurately identify mango varieties. MobileNetV2 followed closely with performances of 98.95% accuracy, 98.85% F1 score, and 98.86% recall. ResNet50V2 also delivered satisfactory results with 97.39% accuracy, 97.08% F1 score, and 97.17% recall. VGG16, however, was the least effective, with a precision rate of 83.25%, an F1 score of 83.25%, and a recall of 85.47%. These results confirm the superiority of the Xception model in detecting mango varieties. Its advanced architecture allows it to capture more distinguishing features of mango images, leading to greater precision and reliability. Xception’s robustness in identifying true positives is another advantage, minimizing false positives and contributing to more accurate classification. This study highlights the promising potential of machine learning, particularly the Xception model, for accurately identifying mango varieties.

Integrated Approaches of Mechanical Barrier, Insecticide and Botanicals against Mango Mealybug (Drosicha mangiferae)

An experiment was conducted to test the efficacy of a mechanical barrier around the tree trunk with one spray of Cypermethrin @ 1 ml/l and turmeric powder extract @ 15 g/500ml of water applied in integrated approaches against mango mealybug on mango tree. There were 7 treatments including a control. The treatments were Mechanical barrier, Cypermethrin spray @ 1 ml/l, Turmeric powder spray @ 15 g/500ml of water, Cypermethrin spray + Turmeric spray, Cypermethrin spray + Mechanical barrier, Turmeric + Mechanical barrier and untreated control. Three-year-old mango plants grafted from a 5-year-old mango tree (Variety Amropali) and planted in pots were used in the experiment. The treatment, Cypermethrin spray + Mechanical barrier gave the highest reduction of mango mealybug population 87.06% to 93.90% followed by Turmeric spray + Mechanical barrier and showed 81.13% to 86.04% and Mechanical barrier alone showed 81.13% to 84.59% population reduction. Turmeric powder extract showed only (8.42% to 23.77%) population reduction of mango mealybug which was lowest compared to other treatments.

Melatonin treatment alleviates chilling injury in mango fruit 'Keitt' by modulating proline metabolism under chilling stress

Mangoes often suffer from low temperature-induced chilling injury (CI) during postharvest cold storage.Therefore,advanced techniques are crucial and in high demand to solve the chilling stress of mango fruit for a higher value.This study addresses chilling stress modulation by investigating the effects of melatonin treatment on CI,proline metabolism,and related gene expressions of ‘Keitt’ mango during cold storage after dipped in 0 (control),0.1 (MT1),and 0.2 mmol L^(-1)(MT2) melatonin solution for 30 min.The results revealed that melatonin treatment in MT1 significantly reduced CI development and increased proline content in mango fruit during cold storage compared to the control.These changes were along with increases in the activity of critical enzymes as well as the expression of encoding genes involved in proline biosynthesis,such as pyrroline-5-carboxylate synthetase (P5CS),pyrroline-5-carboxylate reductase (P5CR),ornithine D-aminotransferase (OAT),P5CS2,P5CR2,and OAT3.Additionally,proline dehydrogenase (PDH) activity and the expression of the PDH3 gene associated with proline dehydrogenation were lower in MT1-treated mangoes than the controlled group.Thus,melatonin treatment has regulated proline metabolism resulting in the accumulation of proline,subsequently contributing to enhancing the chilling tolerance of‘Keitt’mango fruit.

Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.

Influence of Various Hydrocolloids on Suspension Stability of Chia Seeds (Salvia hispanica L.) in Mango Beverage and Mango Flavored Beverage

With the developing of juices and beverages industry, the processors need to bring new nutritional fortified products to capture the market as per the demand of the consumer who needs healthier product. Thus, this study aimed to the production of mango beverage and mango flavored beverage fortified with chia seeds;evaluate the chemical properties of chia seeds and study the effect of adding carboxymethyl cellulose, xanthan gum and low acyl gellan gum at the concentrations of 0.05% and 0.1% on the stability of suspension chia seeds in beverage during storage at ambient temperature for six months. Viscosity, color, zeta potential, suspension of seeds and organoleptic properties were evaluated. Results showed that chia seeds were rich in protein, omega-3 fatty acids and dietary fiber. The addition of 0.05% gellan gum led to improved appearance attributes and the highest stability of suspension of chia seeds for six months, and was the sample which scored highest for overall acceptability compared to the other samples of beverages. Results indicated that using chia seeds with the addition of 0.05% low acyl gellan gum led to the production of fortified mango beverage and mango flavored beverage with high stability for six months.

Production of Vinegar Mango Using Acetobacter tropicalis CRSBAN-BVA1 and CRSBAN-BVK2 Isolated from Burkina Faso

Production and quality of vinegar from mango juice was evaluated using a two steps production procedure. The first step of fermentation was done using Saccharomyces cerevisae KVL013 for 7 days. The second step, an acetic fermentation was realized using two acetic acid bacteria: A. tropicalis CRSBAN-BVA1 and A. tropicalis CRSBAN-BVK2 for 21 days. Several parameters of the vinegar produced such as physico-chemical and sensory properties were determined. Microbial density during each step was monitored. Results showed that pH, alcoholic and acetic acid contents of vinegar were respectively 2.97%, 7% and 4.54% g/ml respectively by using A. tropicalis CRSBAN-BVA1 and 3.02%, 7% and 4.32% g/ml with A. tropicalis CRSBAN-BVK2. Sensory evaluation revealed that the vinegar was acceptable to the panellists. Results of microbial density showed that the maximum concentration of cell biomass produced was 4.32 × 108 and 4.25 × 108 CFU/ml respectively for CRSBAN-BVA1 and CRSBAN-BVK2.

Phytosanitary Practices and Pesticide Levels in Fresh and Dried Mangoes Produced in Burkina Faso

Pesticides are used to control fruit flies and other potential crop pests, particularly mango in Burkina Faso. Pesticides are a potential health hazard for users and consumers and are a major constraint to the export and commercialization of mangoes. The objective of this study was to evaluate phytosanitary practices in orchards and pesticide residue levels in four varieties of fresh and dried mangoes (Amelie, Brooks, Kents and Lippens) produced in four localities in Burkina Faso in order to determine their health impact on consumers. Surveys on pesticide use and monitoring of phytosanitary practices were carried out among 16 orchard owners in the localities of Bobo-Dioulasso, Orodara, Toussiana and Banfora, a high mango production area. Also, a total of 120 samples, including 60 samples of fresh mangoes and dried 60 samples of dried mango were collected. A multi-residue method was developed to detect pesticide levels in the collected samples by gas chromatography with a micro-electron capture detector. The study revealed the use of unauthorized pesticides, often specifically for other crops, and a monitoring trapping network in the orchards against insects. 34 chemical pesticides were detected in 120 samples of all mango varieties collected. Pesticide residues were detected in 72% of the samples, and of the positive samples, 48.52% contained concentrations above the maximum residue limit permissibility. Organochlorine pesticide residues were present in fresh mangoes (60.5%) and a total of fifteen active compounds were detected in dried mangoes. The very poor management and use of pesticides found in these orchards could pose a threat to the productivity of natural ecosystems and the health of producers, processors and consumers. Awareness raising and training of producers on the knowledge of the risks linked to the use of pesticides and good practices are necessary to preserve the health of all.

Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.

Influence of Sorbent Type on Drying Efficiency, Production Costs and Nutritional Values of Mango By-Products Feeds for Livestock

The aim of the study was to determine the best food absorbents between wheat, rice and maize bran and palm kernel cake, from a technical and economic point of view, in order to make a better recommandation for their use in the production process of food based on mango by-products (peels, peels + pulp). To this end, series of 18 kg of fresh feed were prepared and spread out in a stall for sun-drying using a randomized Fisher system. Preparations were made using either mango peels (75%) + absorbent (25%) or pulp + mango peels (67%) + sorbent (33%). The results show that mango Peel feed loses more water (WL) with lover production yields (PY), higher mango incorporation rates into dry feed (MRI), longer drying times (UDT) and lower production costs (CPkgPD) than mango peel + pulp. The average DM, MAT, ADF and NDF fiber contents were almost equal to those of mango peels + pulp feed. The average Crude Fiber (CF) (25.13%) and DEp (2839 kcal/kgDM) contents were higher for mango skin feed than for mango skin + pulp feed, at 8.59% and 2536 for MAT and DEp respectively. Mango peels + wheat bran (PSB25) and whole mango (MESB33) feeds recorded the highest and almost equal levels of TCP, NDF and MM. Production costs per kg of feed dry mater (CPkgDM) for feed produced at 25% were on average 33% higher than for whole mango (WM) feed. Excluding mango raw material, palm kernel meal (PK), rice bran (RB), maize bran (MB) and wheat bran (WB) can be ranked 1st, 2nd, 3rd and 4th in terms of cumulative performance of production parameters. Producers can then choose the type of sorbent they wish to use according to this ranking and the local availability of the sorbent. These feeds can be used for both ruminants and monogastrics, but are better suited to ruminant feeding due to their high fiber content.

Effectiveness of Four Integrated Pest Management Approaches in the Control of Fruit Flies (Diptera: Tephritidae) in Mango Agro-Ecosystems in the South-Sudanian Zone of Burkina Faso

This study evaluated the effectiveness of four Integrated Pest Management (IPM) approaches in the control of Bactrocera dorsalis (Hendel) [Diptera: Tephritidae] and Ceratitis cosyra (Walker) [Diptera: Tephritidae] during two consecutive mango fruiting seasons (2018 and 2019) in the south-Sudanian zone of Burkina Faso. These approaches, including sanitation + M3 bait station (SM), sanitation + protein GF-120 bait (SG), sanitation + Timaye + M3 bait station (STM) and sanitation + Timaye + GF-120 bait (STG), were implemented in 12 mango orchards in three provinces of the country. In each province, one mango orchard was used as control. Flies per trap per week (FTW) and damage indices were assessed in treated orchards compared to the control orchards. The efficacy rate of each IPM approach in protecting mango against fruit fly attacks was also determined. The STG approach was the most effective in reducing both B. dorsalis and C. cosyra FTW with the best efficacy rate. Further research should emphasize indigenous and affordable attract-and-kill tools for resource poor farmers.

氯吡苯脲对贵妃杧果果实产量、品质和采后贮运特性的影响

氯吡苯脲(forchlorfenuron,CPPU)是杧果生产上使用的膨大剂,由于使用不规范,造成了品质下降、果实畸形等诸多问题。本研究选用12 a树龄的贵妃杧果树为试材,在盛花期后15 d,喷施不同质量浓度(10、20、60 mg/L)的CPPU,花后15、25、35d各喷1次,共喷3次,生理成熟期后采摘,研究CPPU对贵妃杧果果实产量、品质和采后贮运特性的影响。结果表明:幼果期经10~20 mg/L CPPU处理后能够增加杧果的果形指数、促进果实膨大和产量增加,当CPPU浓度增加至60 mg/L时,果实膨大明显,但会导致产量下降。CPPU处理后,果实采摘时(生理成熟期)果实的叶绿素、类红萝卜素含量明显升高,花色苷明显降低,果皮红度值(a^(*))偏小,其颜色偏绿;完熟期杧果的叶绿素、类红萝卜素含量高,花色苷含量低,高浓度CPPU处理果实果面红色偏少,黄色偏多,果皮a^(*)值偏小,部分果实偏绿色;完熟期的可溶性固形物含量降低,总酸增加,固酸比下降,口感甜度下降,低浓度影响较小,高浓度CPPU处理对内在影响较大。CPPU处理可降低杧果采后病害的发生,果实采后保持较高的硬度,明显延缓杧果储藏期果实软化,明显延长了杧果的贮藏时间,提高了其采后贮运特性。综合以上结论,幼果期喷施CPPU的适宜浓度为10~20 mg/L,盛花期后15~50d果实细胞旺盛分裂期效果最佳,可显著增加单果重和产量,对果实品质和果实转色影响不显著,并能延长贮藏时间,更利于采后贮运保鲜。

氯氟醚菌唑在香蕉和芒果果实中的残留分布

采用超高效液相色谱-串联质谱仪(UPLC-MS/MS),建立了香蕉、芒果果实中氯氟醚菌唑残留的检测方法,并依据NY/T 788—2018《农作物中农药残留试验准则》,开展了400 g/L氯氟醚菌唑悬浮剂(SC)1年6地的田间残留试验,研究了氯氟醚菌唑在香蕉、芒果果实中的消解动态和残留分布情况。样品经乙腈提取,GCB和C18组合吸附剂净化,UPLC-MS/MS检测。结果表明:在0.001~1 mg/kg添加水平下,氯氟醚菌唑在香蕉全果和果肉中的平均回收率为83%~93%,相对标准偏差(RSD)为2.3%~5.9%;在芒果全果和果肉中的平均回收率为82%~91%,RSD为2.9%~6.0%。定量限(LOQ)均为0.001 mg/kg。田间试验结果显示:400 g/L氯氟醚菌唑SC按有效成分133.3 mg/kg施药3次,施药间隔7~10 d,距末次施药后21、28 d,氯氟醚菌唑在香蕉全果中的残留量为0.03~0.09 mg/kg,在香蕉果肉中的残留量为<0.001~0.004mg/kg;按有效成分160 mg/kg施药3次,施药间隔7~10 d,距末次施药后14、21 d,氯氟醚菌唑在芒果全果中的残留量为0.04~0.28 mg/kg,在芒果果肉中的残留量为0.001~0.003 mg/kg。该研究结果可为指导氯氟醚菌唑在香蕉、芒果上的科学合理使用及制定其在香蕉、芒果上的最大残留限量(MRL)提供参考。

不同时期放梢和不同生长阶段喷施多效唑对福建地区爱文杧果成花着果的影响

调查了福建地区爱文杧果在9月上旬至11月下旬3个月内不同时期放梢对结果枝成花着果的影响,分析了结果枝的矿质营养状况,研究了在嫩梢期、转绿期、老熟期3个不同生长阶段喷施不同浓度多效唑(PP333)控、放梢效果及其对成花着果的影响。结果表明:爱文杧果结果枝适宜放梢期范围较大,除当年气候条件能直接影响爱文杧果成花和着果外,结果枝的长度、粗度、叶片数和叶片矿质营养状况也是影响成花和着果的重要因素,在正常年份爱文杧果放梢期最好在9月下旬,这个时期放梢结果枝最长,叶片N元素含量最高,现穗率达100%,平均单枝挂果数最多。多效唑处理时间早、浓度低有利于杧果提早开花,而处理的时间迟、浓度高则比对照延迟开花。在嫩梢期多效唑低质量浓度处理效果优于高质量浓度处理;老熟期则相反,多效唑低质量浓度处理效果低于高质量浓度处理。

植物源保鲜剂在芒果采后抑菌保鲜上的研究进展

植物源保鲜剂因其来源广、安全、高效、不存在抗药性等特点,在芒果采后抑菌保鲜的研究和应用中具有广阔前景。整理了近年来对芒果采后抑菌保鲜效果显著的植物种类,并从中草药提取物、精油、多糖、生物碱及黄酮类和酚类物质等植物源保鲜剂的单独使用和多种植物源保鲜剂的配合使用两个方面总结了近年来我国植物源保鲜剂在芒果采后抑菌保鲜中的应用研究现状;对目前植物源保鲜剂在芒果保鲜中存在的问题进行分析并提出了解决策略,对植物源保鲜剂在芒果保鲜中的应用前景进行了展望,以期为植物源保鲜剂在芒果采后抑菌保鲜的开发利用提供参考。

不同培养基组分对“台农一号”杧花粉萌发和花粉管生长特性的影响

为筛选“台农一号”杧花粉体外培养最适培养基和培养时间,探讨不同培养基组分和培养时间对其花粉体外萌发及花粉管生长的影响,以15%蔗糖+25%聚乙二醇-4000+0.015%硼酸+0.03%钙+0.02%镁+0.01%钾为基础培养基,分别设置1、2、3、4、5、6 h培养,不同蔗糖、硼酸、聚乙二醇-4000、钙、镁、钾浓度梯度单因素及蔗糖、硼酸、聚乙二醇-40003个因素正交试验,统计比较不同处理的花粉萌发率和花粉管生长长度。结果表明,培养时间,培养基中蔗糖、聚乙二醇-4000、硼酸对花粉萌发及花粉管生长均有显著影响,而钙、镁、钾影响不显著。培养2~3 h即可统计花粉萌发率及测量花粉管长度。随着培养基中蔗糖、聚乙二醇-4000、硼酸浓度增加,花粉萌发率及花粉管长度均呈现先增加后降低的趋势,适宜浓度的3个组分能够促进花粉萌发及花粉管生长,浓度过高则起抑制作用。3个因素之间存在明显的互作效应,蔗糖与聚乙二醇-4000的互作效应突出。说明培养基中蔗糖、聚乙二醇-4000、硼酸是花粉体外萌发的必需成分,钙、镁、钾为非必需成分。“台农一号”杧花粉体外萌发及花粉管生长的最适培养基为15%蔗糖+20%聚乙二醇-4000+0.015%硼酸+0.03%钙+0.02%镁+0.01%钾,适宜培养时间为3 h,此条件下花粉萌发率达到83.40%。

基于注意力机制残差神经网络的近红外芒果种类定性建模方法

现代光谱检测技术的飞速发展与深度学习紧密相关,作为一种端到端的模型,深度神经网络可以从光谱中得到更多信息,从而提升模型鲁棒性。为探究近红外光谱结合深度学习对芒果种类定性预测的可行性,提出一种基于卷积注意力机制(CBAM)的一维残差神经网络(1D-AD-ResNet-18)模型。为降低光谱中冗余信息的干扰,在传统一维残差神经网络(1D-ResNet-18)中嵌入CBAM卷积注意力模块,该模块可重点关注光谱局部有用信息;为避免梯度消失、过拟合情况发生,使用解决网络“退化”问题的ResNet-18。对于186个芒果样本,采用70%的样本进行训练,30%的样本进行测试,采用准确度(Accuracy)、精确率(Precision)、召回率(Recall)、F1值(F1-score)、宏观平均值(Macro-average)以及加权平均值(Weighted-average)作为模型评价指标。建立传统1D-ResNet-18、SNV-SVM和PCA-KNN三种对比模型,与上述三种方法作对比,所建立的1D-AD-ResNet-18模型取得最优预测结果,四种定性分析模型的准确率分别为96.42%,80.35%,76.78%和67.85%。结果表明,1D-AD-ResNet-18模型实现了对芒果种类的准确识别与分类,为近红外光谱定性分析芒果种类提供了新思路。