超声提取-离子色谱法测定芹菜中氯量

研究了超声提取-离子色谱法测定芹菜中氯含量的方法。芹菜经超声提取20 min后,以浓度4.5 mmol/L Na₂CO₃和0.8 mmol/L NaHCO₃为淋洗液,经IonPacAG23分离测定。本方法具有快速、灵敏、准确度高,适合于芹菜中氯量的测定。     

大千生果胶质提取工艺研究

本试验以果胶质作为考察指标，采用超声提取法，通过单因素试验和正交试验，研究对大千 生种子中果胶质提取的影响。实验结果表明:大千生种子中果胶质最佳提取工艺为提取功率200w， 料液比1:20，提取时间20min，提取温度60℃ 。在此最佳工艺下果胶质成分的含量最高为4.51％。     

响应面法优化香水莲花甾醇的超声提取工艺

为了分析香水莲花的植物甾醇含量并优化其超声提取工艺,采用高效液相色谱（HPLC）法测定香水莲花中菜籽甾醇、菜油甾醇和β-谷甾醇的含量,再以上述3种甾醇提取量为评价指标,对液料比、超声提取温度和超声提取时间进行单因素试验,在此基础上,再采用Box-Behnken响应面法优化香水莲花甾醇提取工艺并进行验证。结果表明,影响香水莲花甾醇提取量的主次因素是液料比>提取时间>提取温度,其最优工艺条件为以95%乙醇为提取溶剂、超声功率100 W、液料比30∶1（mL/g）、超声提取温度60 ℃、超声提取时间30 min;在最优工艺条件下进行验证实验,得出香水莲花中甾醇含量为菜籽甾醇64.61 mg/100 g、菜油甾醇40.77 mg/100 g、β-谷甾醇99.04 mg/100 g,总甾醇204.42 mg/100 g,综合评分与预测值相比差异不显著（P<0.05）。研究结果为香水莲花资源的开发和利用提供参考。     

超声波辅助提取荷叶多酚工艺及其动力学分析

以微山湖荷叶为试材,用超声波辅助提取荷叶多酚,在单因素试验基础上,采用Box-Behnken响应面法优化提取工艺,并建立4种数学模型对提取过程的动力学进行研究,采用决定系数(R2)、精准因子(Af)、偏差因子(Bf)和根平均方差(RMSE)4种模型评价参数对模型的适用性进行分析。结果表明:当乙醇体积分数68%、料液比1∶25 g·mL-1、提取温度40℃、提取时间38 min时,荷叶多酚得率为3.231%;2种非线性增长函数模型,即Sgompertz模型和Slogistic1模型能很好地拟合荷叶多酚提取的动力学过程。超声波辅助提取的工艺参数及所建立的动力学数学模型,均可用于荷叶多酚提取过程的工程放大和优化控制。     

超声波处理对粤晶丝苗生理特性及产量、品质的影响

以粤晶丝苗为材料,通过大田试验,研究了超声波和种子包衣处理对水稻生理特性及产量、品质的影响。结果表明,超声波处理可以提高水稻产量,以"超声波+种子包衣"处理的产量最高,主要是由于超声波处理增加了水稻的有效穗数、每穗粒数和结实率;超声波处理可以提高水稻叶片的SOD、POD活性,增加游离脯氨酸含量,降低MDA含量,提高水稻抗性;超声波处理还可改善稻米的加工品质和外观品质。     

基于DSP与超声波测距的农业机器人定位与避障控制

随着新兴电子集成技术和自动化技术的发展,控制系统已逐渐向数字化转变,高集成芯片广泛应用于自动化控制领域,体积小、运算能力强的嵌入式系统慢慢开始取代计算机。为此,首先分析了超声波测距功能及其优越性,采用三球定位技术,设计研究了一种基于DSP和超声波的全局定位系统;然后,采用多路超声波收发模块设计了基于超声波的自主避障控制系统,并提出一种新的模糊推理方法,实现机器人的避障和路径规划功能;最后,采用Visual C++可视化程序设计软件对避障系统进行仿真和场地试验,验证了系统的可靠性和可行性,为机器人的研究和发展提供了更加宽广的空间。     

超声波辅助溶剂法提取金线莲总黄酮工艺优化

旨在研究金线莲总黄酮的最佳提取工艺条件。采用超声波辅助溶剂法,在单因素实验的基础上,采用响应面法优化了金线莲总黄酮的提取工艺。结果表明:当金线莲颗粒度为100目、乙醇体积分数为40%、料液比为1:40(mg:m L)、超声波功率为250 W、超声波时间为30 min时,所得金线莲总黄酮提取率为2.13%。超声波辅助溶剂法强化了金线莲总黄酮的提取效果。     

不同产地树头菜中维生素C含量的测定

以2%草酸溶液为提取溶剂，超声提取维生素C。在254 nm波长下，以0.05 mol/L磷酸二氢钾∶甲醇(90∶10)为流动相，采用HPLC法，检测出石屏、普洱、西双版纳和建水共4个产地树头菜嫩叶中维生素C含量分别为0.79、1.4、1.5和1.6 mg/g，其中建水产地的维生素C含量最高，石屏产地的含量最低。该方法准确可靠，可作为树头菜中维生素C含量的测定。     

美味牛肝菌中油脂提取工艺的研究

采用超声波辅助法提取美味牛肝菌中的油脂，在单因素（提取时间、液料比和温度）实验考察的基础上，以油脂提取率为指标，通过响应面法对美味牛肝菌油脂的提取工艺进行优化。实验结果表明：响应面回归模型显著，拟合性好，可用于预测油脂提取率；优化后的油脂提取工艺为：液料比34.2 mL/g，温度36℃，提取时间4.5 h，此条件下油脂提取率为3.54826 %，与模型预测值相符，表明优化的油脂提取工艺合理。     

Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

Nitrogen losses from intensive vegetal production systems are commonly associated with contamination of water bodies. Sustainable and optimal economic N management requires correct and timely on-farm assessment of crop N status to detect N deficiency or excess. Optical sensors are promising tools for the assessment of crop N status throughout a crop or at critical times. We evaluated optical sensor measurement of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop N status weekly throughout a muskmelon crop. The Crop Circle ACS 470 was used for reflectance measurement, the SPAD 502 for leaf chlorophyll, and the DUALEX 4 Scientific for leaf chlorophyll and flavonols. Four indices of canopy reflectance (NDVI, GNDVI, RVI, GVI), leaf flavonols and chlorophyll contents and the nitrogen balance index (NBI), the ratio of chlorophyll to flavonols contents, were linearly related to crop N content and to crop Nitrogen Nutrition Index (NNI) throughout most of the crop. NBI most accurately predicted crop N status; in five consecutive weekly measurements, R² values were 0.80–0.95. For NDVI during the same period, R² values were 0.76–0.87 in the first three measurements but R² values in the last two measurements were 0.39–0.45. Similar relationships were found with the three other reflectance indices. Generally, the relationships with NNI were equal to or slightly better than those with crop N content. These optical sensor measurements provided (i) estimation of crop N content in the range 1.5–4.5%, and (ii) an assessment of whether crop N content was sufficient or excessive for optimal crop growth for NNI ranges of 0.8–2.0. Composite equations that integrated the relationships between successive measurements with the optical sensors and crop N content or NNI for periods of ≥2 weeks (often 2–3 weeks) were derived for most indices/parameters. Overall, these results demonstrated the potential for the use of these optical sensor measurements for on-farm monitoring of crop N status in muskmelon.