Synthesis of Fluorescent Carbon Dots and Their Application in Ascorbic Acid Detection

Water-soluble fluorescent carbon dots (CDs) were synthesized by a hydrothermal method using citric acid as the carbon source and ethylenediamine as the nitrogen source. The repeated and scale-up synthetic experiments were carried out to explore the feasibility of macroscopic preparation of CDs. The CDs/Fe³⁺ composite was prepared by the interaction of the CDs solution and Fe³⁺ solution. The optical properties, pH dependence and stability behavior of CDs or the CDs/Fe³⁺ composite were studied by ultraviolet spectroscopy and fluorescence spectroscopy. Following the principles of fluorescence quenching after the addition of Fe³⁺ and then the fluorescence recovery after the addition of asorbic acid, the fluorescence intensity of the carbon dots was measured at λex = 360 nm, λem = 460 nm. The content of ascorbic acid was calculated by quantitative analysis of the changing fluorescence intensity. The CDs/Fe³⁺ composite was applied to the determination of different active molecules, and it was found that the composite had specific recognition of ascorbic acid and showed an excellent linear relationship in 5.0–350.0 μmol·L⁻¹. Moreover, the detection limit was 3.11 μmol·L⁻¹. Satisfactory results were achieved when the method was applied to the ascorbic acid determination in jujube fruit. The fluorescent carbon dots composites prepared in this study may have broad application prospects in a rapid, sensitive and trace determination of ascorbic acid content during food processing.     

化学计量学二阶校正方法结合高效液相色谱用于蜂蜜中10种酚酸类物质的快速定量分析

利用化学计量学二阶校正方法结合高效液相色谱对枣花蜜中10种酚酸类物质的快速定量分析进行了研究。首先通过验证样本研究了所建立模型的准确性。结果显示：10种酚酸类物质的线性相关系数（R）为0.9982~0.9999,平均回收率为97.6%~101.1%,说明所建立的模型稳定可靠。其次,通过模拟蜂蜜试验,确定了固相萃取柱的种类及操作条件（HLB柱,酸化水淋洗,甲醇洗脱）。最后,利用模拟蜂蜜得到的最优条件结合化学计量学二阶校正方法,测定了枣花蜜中10种酚酸类物质的含量,并测得其加标回收率为62.1%~93.8%,考虑到目标分析物的种类较多,且蜂蜜基质极为复杂,该结果基本满足要求。另外,还利用统计与品质因子验证了试验方法的可靠性,结果令人满意。该方法具有简单、快速等优点,可用于复杂基质中多种目标分析物的同时定量分析。     

骏枣优良单株指标性状的主成分分析

运用主成分分析与SPSS统计软件,对山西省交城骏枣优良单株进行定量综合评价.主成分分析表明决定优良单株的基本指标为:丰产性状、VC、含糖量和可食率,其结论有一定的参考价值.     

外源性加硒法增加枣中硒含量的研究

以不同深度硒溶液对枣树进行不同次数的叶面喷施,得到了不同硒含量的枣样品,用荧光光度法测定了样品中硒的含量。结果表明,与对照组相比,用外源性加硒法可明显提高枣中硒的含量;并且可通过调节喷施液浓度和喷施次数,得到硒含量适宜的成熟小枣。     

高光谱数据对损伤长枣的检测判别

灵武长枣作为宁夏优势特色枣果,具有重要的经济社会价值和科学研究意义。利用可见近红外(Vis/NIR)高光谱成像系统采集60颗完整长枣光谱图像,然后利用损伤装置对60颗完整长枣进行损伤实验,最终得到60颗损伤（内部瘀伤）长枣,高光谱成像系统采集损伤后五个时间段（损伤后2,4,8,12和24 h）长枣的光谱图像。对采集的长枣光谱图像用ENVI软件提取感兴趣（ROI）区域,并计算完整长枣和每个时间段长枣的平均光谱值。原始光谱利用Savitzky-Golay平滑的一阶导数（SG-1）和二阶导数（SG-2）、标准正态变换（SNV）和去趋势（Detrending）、以及SNV-SG-1、SNV-SG-2、Detrending-SG-1、Detrending-SG-2算法进行预处理,原始光谱和预处理光谱建立偏最小二乘判别分析（PLS-DA）分类模型。选择最优的预处理光谱数据,利用连续投影算法（SPA）、间隔随机蛙跳（IRF）、无信息消除变量（UVE）、变量组合集群分析法（VCPA）、区间变量迭代空间收缩法（IVISSA）和IRF-SPA、UVE-SPA、IVISSA-SPA等算法进行特征变量选择,对选择的特征变量建立PLS-DA、线性判别分析（LDA）和支持向量机（SVM）分类判别模型。结果表明,在原始光谱建立的PLS-DA模型中,模型校正集和预测集准确率分别为82.96%和90%。光谱经过预处理后得到SNV-SG-2-PLS-DA为最优分类判别模型,模型校正集和预测集准确率分别为91.11%和96.67%。在特征变量建立的分类模型中,SNV-SG-2-UVE-PLS-DA模型校正集和预测集准确率分别为86.3%和94.44%;SNV-SG-2-SPA-LDA模型校正集和预测集准确率分别为86.3%和83.33%;SNV-SG-2-UVE-SVM模型校正集和预测集准确率分别为77.78%和71.11%。对于分类模型来说线性分类模型（PLS-DA、LDA）分类结果优于非线性分类模型（SVM）分类结果,在线性分类模型结果中PLS-DA优于LDA分类结果,PLS-DA可以更好的提供分类效果。研究表明,利用高光谱结合偏最小二乘判别分析分类模型,可以有效的实现灵武长枣损伤后随时间变化的快速检测,为灵武长枣在线检测提供理论依据。     

高光谱成像技术对鲜枣内外部品质检测的研究

外部缺陷以及内部可溶性固形物的含量对提升鲜枣的采后附加值和鲜枣后续生产加工具有重要的意义,因此,为了实现同时对鲜枣内外部品质进行快速、准确识别,利用高光谱成像技术(450-1,000nm)对壶瓶枣的"自然损伤"和可溶性固形物含量同时进行检测研究。首先,对光谱数据进行主成分分析(PCA)得到前7个主成分光谱值,对图像数据采用灰度共生矩阵(GLCM)提取到7项图像纹理指标(对比度、相关性、能量、同质性、方差、均值、熵)。然后,分别使用光谱主成分值、图像纹理特征值、以及主成分与纹理特征融合值建立偏最小二乘支持向量机(LS-SVM)模型对壶瓶枣的外部缺陷("自然损伤")和内部品质(可溶性固形物含量)进行检测研究。结果表明:使用主成分与纹理特征融合值建立的LS-SVM模型可作为通用模型同时对壶瓶枣内外部品质进行检测研究,其"自然损伤"判别正确率为92.5%,可溶性固形物预测集的预测相关系数(Rp)和预测均方根误差(RMSEP)分别达到了0.944和0.495。表明,采用高光谱成像技术可以建立通用模型同时对壶瓶枣的内外部品质进行检测,该研究为壶瓶枣的无损检测提供了理论参考。     

火焰原子吸收光谱法测定冬枣中的微量元素

用火焰原子吸收光谱法测定了冬枣中钾、钠、钙、镁、铁、锰、铜和锌的含量。结果表明,该法的加标回收率为96.7%～102.8%,RSD≤3.06%。该法操作简单,结果准确,为冬枣资源的进一步开发利用和栽培技术的改进提供了参考依据。     

酸枣仁中总黄酮的含量测定研究

该试验以亚硝酸钠、硝酸铝与氢氧化钠作为显色剂,用紫外分光光度法测定酸枣仁中黄酮类化合物的含量,该法测得芦丁对照品溶液浓度在0．8～6．4mg／L范围内线性关系良好,其线性方程为Y=0．0953X＋0．2081（r^2=0．9995）．平均回收率为98．59％,RSD=1．04％（n=6）．结果说明该方法准确、灵敏度高、重现性好,为酸枣仁的定量分析提供一个可选择的新方法．     

Nondestructive discrimination of internal defects in jujube (Huizao) of Xinjiang based on visible and near-infrared spectroscopy

Abstract

During harvest and transport, defects are most likely to affect the interior of jujubes and thus shorten their storage period. This study applied visible and near-infrared transmission spectroscopy to detect such internal defects. Spectra were acquired on the equator area at 0, 90, 180, and 270 degrees of each sample, and a model was constructed to obtain three-dimensional damage and defect detection model. The first derivative, multiplicative scatter correction, standard normal variate, and median filtering were used for preprocessing. Modeling by mean spectra achieved a better effect than using unidirectional spectra. Then, naive Bayes classifier and support vector machine were employed for the model establishment at 600–950?nm and 680–950?nm bands, respectively, using mean spectra. Median filtering effectively improved the signal to noise ratio and the discrimination accuracy of the support vector machine model at 600–950?nm reached 96.77%, which was the best value among all models. This result indicates that the support vector machine model was the optimum model and 600–950?nm was a suitable data range for the detection of internal defects. This research confirms the feasibility of implementing visible and near-infrared spectroscopy for the detection of internal defects in jujubes.     

基于近红外高光谱成像技术的干制红枣品种鉴别

为实现干制红枣的快速鉴别,提出了一种基于近红外高光谱成像技术的鉴别方法。采集四个品种共240个样本干制红枣的近红外高光谱图像(1 000~1 600 nm)。通过主成分分析法(principal component analysis,PCA)、载荷系数法(x-Loading Weights,x-LW)和连续投影算法(successive projections algorithm,SPA)分别提取7个、8个和10个特征波长;基于灰度共生矩阵(gray level co-occurrence matrix,GLCM)提取第一主成分图像的纹理特征。分别以光谱特征、纹理特征、光谱和纹理融合特征作为输入,建立偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)、反向传播神经网络(back-propagation neural network,BPNN)和最小二乘支持向量机(least squares support vector machines,LS-SVM)模型。结果显示,基于融合特征的模型鉴别率高于分别基于光谱特征或纹理特征的模型鉴别率;基于融合特征的BPNN模型的结果最优,对预测集样本鉴别正确率为100%。说明近红外高光谱成像技术可用于干制红枣品种的快速鉴别。