Development and validation of a cellular biosensor detecting pesticide residues in tomatoes

Two of the most important categories of pesticides used in agricultural practice are organophosphates and dithiocarbamates. Their extensive and inappropriate use has rendered their reliable monitoring at trace levels more and more necessary. This study presents the construction of a rapid and sensitive cellular biosensor test based on the measurement of changes of the cell membrane potential of immobilized cells, according to the working principle of the Bioelectric Recognition Assay (BERA). The cells were immobilized by entrapment in a sodium alginate bead and directly applied in different pesticide dilutions and agricultural samples. The pesticides used were the organophosphate insecticide diazinon and the dithiocarbamate fungicide propineb. Two different cell types, N2a (neuroblastoma) and Vero (fibroblast) were used as the biosensory elements in order to investigate their differential response against the pesticides. In this way, we hoped to increase the selectivity of the assay. Based on the observed patterns of response, we demonstrate that the sensor can be used for the qualitative and, in some concentrations, quantitative detection of the pesticides with a high degree of reproducibility. The lowest detected concentration was 3 nM. Finally, for the investigation of the effects of different pesticides on the accumulation of cytosolic Ca²⁺, we conducted a fluorescent assay on N2a cells treated with tomato sample extracts, which were replicates of the E.U. proficiency test sample. The tomato samples were either organically grown or contained 14 different pesticides. The experimental results showed a higher increase of the intracellular Ca²⁺ concentration in cells treated with non-organic samples compared to the cells treated with organic samples.     

Radial basis network analysis of color parameters to estimate lycopene content on tomato fruits

With the purpose of estimating the lycopene concentration in tomato food samples, in an non-destructive way, several types of linear models of color parameters have been tested using individual values of L*, a* and b* values, (a*/b*), (a * ²/b * ²) and chroma parameters from tomato juice and fresh tomato fruits obtained with two different apparatus (Minolta CR-200b triestimulus colorimeter and HunterLab LabScan XE). Lycopene concentrations of fresh tomato and tomato juice (used as an input) were analyzed by UV-Vis spectroscopy. For all linear methods applied, the best one to estimate the lycopene concentration in tomato was the L*, a* and b* values of tomato juice measured with Hunter colorimeters (adjusted correlation coefficient, and mean prediction error, MPE < 6.59%). Four different RBEF models were designed firstly using three color parameters (L*, a* and b*) designated as “Lab case”, and secondly individually by the (a*/b*), (a * ²/b * ²) and chroma parameters. The lycopene concentration estimations were carried out with the lowest MPE and highest values possible. In order to test the reliability of the non-linear models, external validation process was also performed. From the testing of the all non-linear models applied, the RBEF Lab case model was the best to estimate lycopene content from color parameters (L*, a* and b*) using Minolta or Hunter equipments (MPE lower than 0.009 and higher than 0.997). This was a simple non-destructive method for predicting lycopene concentration in tomato fruits and tomato juice, which was reproducible and accurate enough to substitute chemical extraction determinations, and may be a useful tool for tomato industry.     

Ultrasonication--a complementary 'green chemistry' tool to biocatalysis: a laboratory-scale study of lycopene extraction

Lycopene is bequeathed with multiple bio-protective roles, primarily attributed to its unique molecular structure. The concomitant exploitation of two of the green chemistry tools viz., sonication and biocatalysis is reported here for the laboratory scale extraction of lycopene from tomato peel. The coupled system improved the extraction by 662%, 225% and 150% times over the unaided, only cellulase ‘Onozuka R-10’ treated and only sonication treated samples respectively. The sonication parameters (duration, cycle and amplitude) during the coupled operation were optimized using response surface methodology (RSM). Derivative UV-visible spectra (i.e., dA/dλ and d²A/dλ² against λ), FTIR analysis, and DPPH scavenging test suggested that the reported extraction protocol did not affect the molecular structure and bioactivity of the extracted lycopene. The influence of sonication on the probable structural modulation (through UV-visible spectral analysis) and activity of the enzyme were also analyzed. A plausible mechanism is proposed for the enhanced extraction achieved via the coupled system.     

微波溶样-ICP-AES法测定番茄原料及其萃余物中的微量元素

采用微波溶样，等离子体原子发射光谱（ICP－AES）法测定番茄原料及其萃余物中多种微量元素，选择了最佳样品预处理条件和测定的最佳仪器参数，用国家技术监督与GBW08513茶树叶和GBW07602（GVS－1）灌木枝叶标准样作为质控样，11种元素的测得值与标准值较好地吻合，试样分析结果满意。     

基于多维随机森林的番茄灰霉病高光谱图像早期检测

植物病害的自动早期检测对于作物精确保护至关重要。提出了一种基于多维光谱序列（multi-dimensional spectral series, MDSS）和加权随机森林（weighted random forest, WRF）的番茄灰霉病早期诊断与鉴别方法。目的是利用叶片多个观测维度的光谱曲线整体变化趋势建立作物病害检测模型,以期在肉眼明显可见叶面病斑前对作物病害实现诊断。将健康叶片接种灰霉病菌第3天作为叶片成功染病第1天。试验首先采集番茄健康叶片和染病叶片7天内每天的高光谱图像,提取感兴趣区域并计算平均光谱作为初始光谱数据,经筛选共得到(156×7)组有效样本。将样本数据按时间顺序拆分成分别包含1～7个维度的光谱数据形成多维原始光谱序列,为增加维度间差异性,相邻原始光谱序列相减构成多维关联光谱序列。分别采用符号聚合近似估计（symbolic aggregate approximation, SAX）和符号傅里叶近似估计（symbolic Fourier approximation, SFA）两种符号化方法将光谱序列离散成局部辨别性特征。基于多维光谱序列的局部辨别性特征建立加权随机森林（MDSS-SAX-SFA-WRF）分类模型,实现病害早期检测。相应地,基于单维光谱序列（single-dimensional spectral series, SDSS）的番茄灰霉病识别模型被作为基准模型与MDSS-SAX-SFA-WRF模型比较。试验结果显示,MDSS-SAX-SFA-WRF检测模型在包含2至7个光谱序列维度的56个测试样本数据中均获得90%以上识别准确率,在包含5个光谱序列维度测试集中得到最高99%的识别准确率,较SDSS-SAX-SFA-MRF检测模型在染病第5天的识别率高8.2个百分点。另外受随机干扰的影响,SDSS-SAX-SFA-MRF模型准确率在染病5～7 d出现大幅度回落至最低84%,MDSS-SAX-SFA-WRF模型识别率在肉眼可见病斑阶段依然保持超过98%的较高检测水准,未过度回落。因此,提出的基于多维光谱曲线整体变化趋势和加权随机森林（MDSS-SAX-SFA-WRF）的分类模型能够有效实现番茄灰霉病早期检测,并具有较强的鲁棒性,为染病初期的番茄灰霉病鉴别提供新思路。     

转光农膜的光谱特性研究

通过太阳光谱以及菊花、番茄作用光谱的测试分析 ,讨论了植物生长与太阳光谱的关系 ,表明太阳光谱中 2 80～ 380nm的紫外光 ,5 0 0～ 6 0 0nm的绿黄光及 72 0nm以上近红外光植物利用率较低 ,4 30～ 4 80nm的蓝紫光和 6 30～ 6 90nm的红光有利于增强光合作用。依据植物光合作用和太阳光谱特征 ,设计出CaS∶Cu ,Cl-蓝光膜 ,利用CaS∶Eu2 ,Mn2 ,Cl-,设计出绿光转红光的红光膜。讨论了红蓝复合双峰增益膜及紫外光转红光的稀土有机配合物的光谱特性。农用光能转换剂研制面临新的突破 ,利用反Stocks位移技术研制开发近红外光转红光膜值得关注     

不同波长提取方法的高光谱成像技术检测番茄叶片早疫病的研究

提出了基于连续投影算法(successive projections algorithm,SPA)、载荷系数法(x-loading weights,x-LW)和格拉姆-施密特正交(gram-schmidt orthogonalization,GSO)提取特征波长的高光谱成像技术检测番茄叶片早疫病的方法。首先获取380～1 023 nm波段范围内70个健康和70个染病番茄叶片的高光谱图像信息,然后提取健康和染病叶片感兴趣区域(region of interest, ROI)的光谱反射率值,建立番茄叶片早疫病的最小二乘-支持向量机(least squares-support vector machine,LS-SVM)鉴别模型,建模集和预测集的鉴别率都是100%。再通过SPA 、x-LW和GSO提取特征波长(effective wavelengths,EW),并建立EW-LS-SVM和特征波长-线性判别分析(ew-linear discriminant analysis,EW-LDA)鉴别模型。结果显示,每个模型的鉴别效果都很好,EW-LS-SVM模型中预测集的鉴别率都达到了100%,EW-LDA模型中预测集的鉴别率分别是100%, 100%和97.83%。基于SPA, x-LW和GSO所建模型的输入变量分别是4个(492,550,633和680nm),3个(631,719和747 nm)和2个(533和657 nm),较少的特征波长便于实时检测仪器的开发。结果表明,高光谱成像技术检测番茄叶片早疫病是可行的,SPA,x-LW和GSO都是非常有效的特征波长提取方法。     

近红外光纤光谱技术检测西红柿果浆总酸及可溶性糖的研究

为了探索一种简捷、快速、高效的西红柿品质检测方法,应用近红外光谱技术与光纤传感技术相结合的新方法,快速测量西红柿果浆样品中营养成分的含量。实验所用的主要仪器为近红外光纤光谱仪,波长范围为900～2 500 nm。以164个西红柿样品为标准样品,进行了光谱采集及相应的化学值测定。实验数据采用偏最小二乘法(PLS)进行回归,建立西红柿果浆中总酸及可溶性糖含量的数学模型,并对回归方法进行统计分析。结果为：西红柿果浆中总酸验证集的决定系数(R2)为0.967,均方根误差(RMSEC)为0.133,预测均方根误差(RMSEP)为0.103;总糖验证集的决定系数(R2)为0.976,均方根误差(RMSEC)为0.463,预测均方根误差(RMSEP)为0.460。均达到了较好的预测结果,表明该方法对定量分析西红柿果浆中多组分含量是可行的。基于该方法快速、简便及可对同一样品多组分含量同时分析的优点,它是一种极具发展前途的传感器,正在逐渐成为国际传感器领域的研究热点。     

温室番茄缺素叶片偏振反射特征分析

为提高番茄植株营养胁迫定量分析模型的精度,探究偏振检测在植物单叶尺度进行无损检测的优势,利用自行研制的偏振反射光谱系统检测不同生长期温室番茄缺素叶片偏振反射特征。对影响番茄单叶偏振反射的主要因素：方位角、入射天顶角、探测天顶角、光源偏振片起偏角度、探测器偏振片起偏角度进行了讨论,通过正交试验的极差分析获取光谱仪各测量角度参数的优水平,并通过实验进一步验证,最终分析得到偏振光谱系统检测番茄缺素叶片的角度组合及主次排序为：入射天顶角60°、光源起偏角度0°、探测器起偏角度45°、探测天顶角45°、方位角180°,在此基础上对不同生长期的缺氮、缺磷、缺钾叶片以及不同缺素程度的叶片进行分析比较,结果显示偏振反射比随番茄叶片的生长周期呈现正相关关系,缺素和营养过量均能导致偏振反射比的下降,偏振反射比在结果期和采收期的降幅较为明显。对于偏振反射光谱在植物单叶尺度营养快速检测的深入研究具有一定的理论和实践意义。     

玻璃温室环境下番茄叶绿素含量敏感光谱波段提取及估测模型

为了快速、准确估测番茄叶片叶绿素含量,利用光谱分析技术研究了玻璃温室环境下番茄叶绿素含量敏感光谱波段提取及其估测模型。番茄以基质方式栽培,在结果期使用ASD FieldSpecTM HH型便携式光谱辐射仪采集叶片光谱,并采用752型紫外-可见分光光度计测定其叶绿素含量。从原始光谱、吸光度光谱、一阶微分光谱、去除包络线光谱出发,进行光谱预处理,分析了净化图谱信息、突出作物叶绿素含量光谱特征的有效性。其中,吸光度光谱在可见光部分增强了光谱响应特征,去除包络线光谱和一阶微分光谱均具有较强的蓝光、红光吸收谷和绿光反射峰。又结合波段间自相关分析和多重共线性诊断提取了番茄叶绿素含量敏感光谱波段,原始光谱特征波段为639,672,696,750,768 nm;吸光度光谱特征波段为638,663,750,763 nm;去包络线光谱特征波段为436,564,591,612,635,683,760 nm;一阶微分光谱特征波段为516,559,778 nm。最后,应用4种预处理下的番茄叶绿素含量敏感光谱波段分别建立多元线性回归模型,模型精度由高至低分别为去包络线、吸光度、原始、一阶微分,其中去包络线模型校正集决定系数R2_c为0.88,验证集决定系数R2_v达到0.82,具有较好的预测能力。