Cyanogenic,carotenoids and protein composition in leaves and roots across seven diverse population found in the world cassava germplasm collection at CIAT,Colombia

The objective of this study was to characterise the nutritional potential of leaves and identify a diversity centre with low cyanide and high nutrient content among 178 Latin American cassava genotypes. This field-based collection represents the seven diversity centres, held at The International Center for Tropical Agriculture (CIAT Palmira, Colombia) by the Cassava Program. The cyanide, all-trans-β-carotene and lutein concentrations in cassava leaves ranged from 346 to 7484 ppm dry basis (db), from 174–547 μg g⁻¹ db and 15–181 μg g⁻¹ db, respectively. Cassava leaves also showed significant levels of essential amino acids leucine, lysine, phenylalanine, valine and threonine, and average total protein content of 26.24 g 100 g⁻¹ db. Among seven diversity centres, South American rainforest group showed low cyanide and high carotene content in leaves. In addition, VEN77 and PAN51 genotypes stood out for having low cyanide in leaves and roots and high carotene in leaves. This genetic diversity can be used to select high potential progenitors for breeding purposes.     

气相色谱法测定新鲜茶叶中有机氯农药残留量

对新鲜茶树叶中六六六和滴滴涕共八种异构体的残留量进行测定,与同地点成品茶叶农药残留量相比较基本无差异。本法检测限为>1.8ng/g,回收率为78.2％-99.0％,相对标准偏差<10.0％。     

制丝线FT651型叶片垂直分片机控制系统的改进方案

针对叶片垂直分片机使用过程中出现的劈坏烟箱、纵向移动刀架失控和系统无报警功能等问题,分析故障产生的原因,并对其控制系统进行改进。     

陈化过程中不同部位烤烟烟叶香味成分变化的研究

采用SDE-GC/MS法对陈化过程中不同部位烤烟烟叶的香味成分进行分析检测,并按照香味物质的产生来源对分析结果进行了分类研究。结果表明,陈化过程中不同部位烟叶的香味物质的变化情况差别明显。从香味成分的变化情况看,各个部位适宜的陈化时间有所不同:上部烟叶2年以上,中部烟叶1.5~2年,下部烟叶1年左右。     

构树皮丙酮抽出物的DEAE—Sephadex分离及GC—MS分析

利用ＤＥＡＥ—Ｓｅｐｈａｄｅｘ分离柱及ＧＣ—ＭＳ对构树皮丙酮抽出物 进行了分离和分析．在丙酮抽出物中中性组分占抽出物的５９．７％，弱酸性 组分和强酸性组分分别占２５．９％和１０．３％．中性组分中不皂化物占 ８９．２％，皂化物仅占８．４％．不皂化物的成分比较复杂，所检出的主要有 甾醇和甾酮类．皂化物中亚油酸占５０％，软脂酸占２１％．弱酸性组分中 ５０％以上是脂肪酸（亚油酸和软脂酸），而没有松香酸．     

银杏叶中黄酮类化合物的提取工艺研究

研究了乙醇-水体系浸取银杏叶中黄酮类化合物的工艺条件,通过分光光度法测定黄酮的含量并计算它的浸出率。根据黄酮类化合物浸出率的计算结果,选用70%乙醇溶液为提取剂,并通过正交试验给出了黄酮类化合物的最佳浸出条件:浸取温度70℃,固液比1∶20,浸取剂pH=8,银杏叶中黄酮类化合物的浸出率可达到92.2%。     

Antioxidant activity in leaves of some mediterranean plants

Hexane and methanol leaf extracts of sixteen Mediterranean plant species were obtained by percolation. Higher yields were observed for methanol (16.4–47.8%) than for hexane extracts (1.3–13%). Antioxidant activity of these extracts was determined by a rapid spectrophotometric method involving the combined oxidation of beta-carotene and linoleic acid. Leaf extracts were thus characterized by an Antioxidant Activity Coefficient (AAC) ranging from 0 to 1000. Hexane extracts gave much higher AAC values than the corresponding methanol extracts. In both cases, myrtle (Myrtus communis) showed the best antioxidant effectiveness.     

Solid/liquid extraction and isolation by molecular distillation of hydroxytyrosol from Olea europaea L. leaves

Molecular distillation, or short‐path distillation (SPD), is particularly appropriate for processing of low‐volatility compounds, which are easily altered at high temperature. Olea europaea L. leaves constitute an olive tree by‐product very interesting for their natural antioxidants content. In this research, molecular distillation technology has been applied to obtain high‐value‐added compounds by the SPD fractionation of an olive tree leaf extract. The process consists of two stages: (a) ethanolic extraction of the olive leaves, followed by incorporation of the extract into glycerine and (b) molecular distillation of the glycerine enriched in olive leaf extract compounds (terpenic and phenolic compounds). Four molecular distillation tests under different conditions were carried out. Results showed that 80.9% 3,4‐dihydroxy‐phenylethanol (hydroxytyrosol) was recovered from the glycerine admixture under a pressure of 1.50–2.00 mbar, a temperature of 190 °C and a feed rate of 15 mL/min.     

基于卷积神经网络的霉变烟叶图像识别方法研究

针对人工在线精选霉变烟叶时,存在效率低下、容易漏检等缺点,提出了一种基于卷积神经网络模型对霉变烟叶图像进行筛选、分类识别的方法。首先建立烟叶数据集,然后搭建卷积神经网络模型,利用卷积神经网络先初步提取特征,再筛选提取主要特征,然后进行各部分的特征汇总;最后实现图像的分类,从而实现了快速、准确的识别霉变烟叶图像和正常烟叶图像。实验结果表明,与人工挑选霉变烟叶的方法和烟叶传统图像分类算法相比较,搭建的卷积神经网络不仅具有较高的识别准确率,也简化了人工提取图像特征的复杂过程。     

基于TACNN的玉露香梨叶虫害识别

为了解决玉露香梨叶虫害种类多、扩散速度快、人工识别梨叶虫害耗时长的问题,提出了能够在自然环境下对玉露香梨叶虫害图像自动识别的Tiny-Alexnet卷积神经网络（Tiny-Alexnet Convolution Neural Network,TACNN）的虫害识别模型。分析了Alexnet模型的网络结构,并将实地采集的玉露香叶片虫害图像进行统一处理,为避免全连接层卷积核参数过大而产生的过拟合现象,通过优化全连接层,设置不同神经元节点和实验参数,得出了Mid-Alexnet、TACNN两种虫害识别模型。实验结果表明：TACNN较Alexnet和Mid-Alexnet模型有较高的识别准确率,该模型能够有效地提取梨叶虫害特征,类别平均准确率为81.18%,实现了对金龟子、梨木虱、梨瘿蚊三种虫害的准确区分。该模型在玉露香梨叶虫害识别方面具有良好的性能,可实现自然环境下玉露香梨叶虫害的精准识别。