This work was partially supported by the project "A Scheme to Revitalize Agriculture and Fisheries in Disaster Area through Deploying Highly Advanced Technology" of Ministry of Agriculture, Forestry and Fisheries, Japan.

1.样品的收集<ol><li>决定哪个品种将被包括在校准模型的对象。 </li><li>收集足够数量和各​​类目标品种的样品蓝莓。 <ol><li>收集优选100蓝莓的校准模型的构造，并且在至少10的构造模型的验证。为了构建可靠的模型，收集各种样品， 即具有各种颜色，大小，并在不同的成熟条件。 </li></ol></li><li>称量每个蓝莓。注意:测量的权重后用于每蓝莓的成分的含量百分比的计算。 </li></ol> 2.光谱的测量<ol><li>热身测量前分光光度计足够（超过1小时），以获得可靠的光谱。 </li><li>设置分光光度计。确保条件不变全部通过测试。一个用于测量的典型条件的例子如下。 <ol><li>测量范围设置为12,500-3,600厘米-1。 </li><li>光谱分辨率设置为16 -1。 </li><li>积累设定为32次。 </li></ol></li><li>选择漫反射为计量模式。 </li><li>把标准反射分光光度计进行漫反射测量的窗口。通过使用"背景单通道"命令，测量后自动用于相对反射光谱从样品蓝莓后测得的频谱计算的背景光谱。 </li><li>把蓝莓样品在分光光度计为漫反射测量的窗口的中心。通过使用"样品单信道"命令，测量每个蓝莓的光谱最好在水果几个点。 注:库贝尔卡-芒克改造6,7也将执行自动matically样品蓝莓的观测频谱如果光谱采集的条件被设定为这样做。库贝尔卡-Munk的变换改变在漫反射模式到光谱等同于在发送模式测量并且需要对光谱的高精确度的分析测定的光谱。光谱中吸光度比例被用于分析。 </li><li>如果一个蓝莓样品的光谱在几个点测量计算用数据处理程序，例如MS Excel的各样品的光谱的平均频谱。使用用于分析平均频谱。 </li></ol> 3.预处理糖分和有机酸8的测定高效液相色谱法注意:提取糖和每个蓝莓的有机酸，可溶于水，用超纯水如下。每个蓝莓的整个被用于分析。 <ol><li>保持在冷冻蓝莓低于-30℃的准备FO如果它们不只是光谱测量后分析ř化学分析。 </li><li>切蓝莓成几片，因此它可以在下面的步骤很容易地匀化。切不蓝莓当它被冻结​​解冻。 </li><li>把碎片到50毫升的烧杯中。 </li><li>添加约10毫升超纯水（蒸​​馏水的导电率小于0.1μS/ cm）的烧杯中。 </li><li>加热在超纯水中切割蓝莓在微波炉中20秒钟停用的分析过程中可能分解糖的酶。 </li><li>加约 10毫升超纯水的烧杯中。 </li><li>用装有标准轴和发电机均化器匀化以12,000rpm离心5分钟将混合物。 </li><li>离心以3,000rpm（2,000×g下）10分钟的均化的混合物。 </li><li>通过使用图5B纸过滤器的离心样品真空过滤收集滤液。 </li><li>重复步骤3.6-3.9两次日Ë过滤残渣收集所有糖分和有机酸，并结合所有的滤液。 </li><li>测量滤液的pH，并将其用稀盐酸（0.1和0.01摩尔L-1）调节到7和稀氢氧化钠（0.1和0.01摩尔L-1）的水溶液。 </li><li>稀释滤液至50ml，用超纯水。 </li><li>将样本分为二;一个用于糖的分析和其他为有机酸的分析。 </li><li>通过第一样品通过柱串联连接以排除颜料，阳离子，和阴离子溶液（二C18，CM和QMA）。丢掉第一1ml得自列的样品溶液。然后使用从列样品溶液用HPLC糖的分析。 </li><li>通过串联连接的，以排除颜料和阳离子柱（二C18和CM）的第二样品溶液。丢掉第一1ml得自列的样品溶液。然后使用从C样品溶液olumns用HPLC的有机酸分析。 </li><li>离心从步骤3.14的各溶液和3.15 6,600转（5800×g）中，10分钟在装有0.45微米的过滤器以通过HPLC分析前一个微型离心机微管。 </li></ol> 4.糖的测定高效液相色谱法注意:在此研究中，蔗糖，葡萄糖和每个蓝莓的果糖的总含量被认为是总糖含量。因此，首先获得每个的三种糖的工作曲线，然后获得在每个蓝莓糖的总含量。标准内容被报告为0.3-0.4％（重量）（蔗糖），3.8-4.8％（重量）（葡萄糖），和4.2-5.3％（重量）（果糖）。9 <ol><li>精确测量约200mg蔗糖，和溶解​​在50毫升的超纯水，制备标准溶液。稀释将5ml溶液至50ml超纯水中，制备第二标准的解决方案。同样准备第三架从第二标准溶液ARD溶液。 </li><li>准备葡萄糖和果糖的标准溶液，同样地。 </li><li>安排HPLC系统如下: <ol><li>在40℃，使用在柱温箱的凝胶渗透柱。 </li><li>使用脱气的超纯水为0.1毫升/分钟的洗脱液的流速。 </li><li>使用折射率检测器。 </li></ol></li><li>通过注射每次测量20微升等分试样测量的标准溶液的HPLC谱图。注意:在此，PAC溶液被用作软件进行测量。 </li><li>通过点击"重新分析&#39;用鼠标的右键得到糖上的每个标准溶液的色谱图的频带的面积强度。 </li><li>绘制对相应浓度的区域的强度通过线性回归，其中FO获得表示区域的强度和浓度之间的关系的公式，获得每个糖工作曲线r分别糖。 </li><li>通过注射每次测量20微升等分试样测量样品溶液的HPLC谱图。 </li><li>如先前在步骤4.5中所述得到的各样品溶液的色谱图糖的带的区域的强度。 </li><li>获得使用对应于在步骤4.6中得到的工作曲线的方程解糖的浓度。 </li><li>获得在从在先前步骤中和样品溶液的总体积得到的样品溶液的浓度每蓝莓每种糖的量（50毫升，见步骤3.12）。 </li><li>由三种糖的内容总结获得每个水果的总糖量。 </li><li>通过使用在步骤1.3测定重量得到每蓝莓的总糖含量百分比。 </li></ol> 5.有机酸的HPLC测定注意:在此研究中，柠檬酸，奎尼酸总含量，苹果酸和琥珀酸被认为是总有机酸含量。因此，首先获得对于每个四有机酸工作曲线，然后在每一个蓝莓有机酸含量进行测定。标准内容被报告为0.42-0.62％（重量）（柠檬酸），0-0.15％（重量）（奎尼酸），0.08-0.23％（重量）（苹果酸），和0.06-0.25％（重量）（琥珀酸）。9 <ol><li>精确测量约5mg的柠檬酸，和其溶解到50ml超纯水中，制备标准溶液。稀释将5ml溶液至50ml超纯水中，制备第二标准的解决方案。制备类似地从第二标准溶液中的第三标准溶液。 </li><li>制备奎尼酸，苹果酸，和琥珀酸的标准溶液，同样地。 </li><li>安排HPLC系统如下: <ol><li>使用串联连接的柱加热炉2阴离子交换柱在40℃。 </li><li>使用脱气的磷酸0.1％的水溶液酸为0.02毫升/分钟的洗脱液的流速。 </li><li>在210nm使用紫外 - 可见检测器集。 </li></ol></li><li>通过注入每个测量标准溶液的20微升等分衡量标准溶液的HPLC谱图。 </li><li>通过点击"重新分析&#39;用鼠标的右键获得每个标准溶液的色谱图有机酸的频带的面积强度。 </li><li>绘制对相应浓度的区域的强度通过线性回归，其中对于每个有机酸获得表示区域的强度和浓度之间的关系的公式，获得每个有机酸的工作曲线。 </li><li>通过注入样品的每个测定的20微升等分试样测量样品溶液的HPLC谱图。 </li><li>得到的有机酸对各样品溶液的色谱图中带的面积强度如在步骤5.5之前描述。 </li><li>获得的在使用对应于在步骤5.6中得到的工作曲线的方程解的有机酸的浓度。 </li><li>获得在从在先前步骤中和样品溶液的总体积得到的样品溶液的浓度每蓝莓各有机酸的量（50毫升，见步骤3.12）。 </li><li>由四个有机酸含量总结获得在每个蓝莓总有机酸的量。 </li><li>通过使用在步骤1.3测定重量得到每蓝莓的总有机酸含量百分比。 </li></ol> 6.预处理花青素的测定高效液相色谱法<ol><li>保持蓝莓在冷冻低于-80℃准备用于化学分析，如果他们不只是光谱测量后进行分析。 </li><li>干各冷冻水果与真空冷冻干燥12小时。 </li><li>从1％的甲醇溶液0干燥蓝莓提取花青素˚F三氟乙酸[重量的蓝莓（G）的溶液的/体积（ml）= 1/10]通过在4℃下放置在冰箱中的混合物12小时。 </li><li>离心机利用超速离心机在-8℃，15000转（21900×G）的提取物在2 ml微管中15分钟。 </li><li>过滤提取物通过0.45μm的过滤器，以得到用于HPLC测量样品。 </li></ol> 7.花青素的测定高效液相色谱法注:关于13种花色苷都包含在蓝莓。由于很难获得工作曲线对于所有花青素，仅供矢车菊-3-O-葡萄糖苷酰氯工作曲线，在蓝莓最受欢迎花青素之一，被获得。工作曲线应用于其它花青素的近似quantifications。 <ol><li>精确测量约1.5mg的矢车菊-3-O-葡萄糖苷氯化物，它溶解到10ml的三氟乙酸的1％的甲醇溶液至Prepare标准溶液。稀释将5ml溶液至10ml用三氟乙酸的1％的甲醇溶液以制备第二标准的解决方案。同样，准备第三和第四个标准溶液顺序。 </li><li>安排HPLC系统如下: <ol><li>在列烘箱中在40℃，使用C18反相柱。 </li><li>申请使用的0.1％的三氟乙酸水溶液（洗脱液A）和0.5％三氟乙酸洗脱物在乙腈（洗脱B）中以0.1毫升/分钟的流速，梯度方法，其中的洗脱b增加从8％到15％的比例在注射后0-50分钟，从15％至75％的注射后50-60分钟内。 </li><li>在520nm用二极管阵列检测器的监控。 </li></ol></li><li>通过注入每个测量一个10微升等分试样测量的标准溶液的HPLC谱图。 "LC解决方案"被用作软件进行测量。 </li><li>得到的频带的面积强度每个标准溶液的色谱图矢车菊-3-O-葡萄糖苷氯通过点击"重新分析"，用鼠标右键。 </li><li>绘制对相应浓度的区域的强度通过线性回归，其中对于矢车菊-3-O-葡萄糖苷酰氯获得代表面积强度和浓度之间的关系的公式，获得矢车菊-3-O-葡萄糖苷氯化物工作曲线。 </li><li>通过注入每个测量一个10微升等分试样测量样品溶液的HPLC谱图。 </li><li>如先前在步骤7.4中所述得到的各样品溶液的色谱图每个花青素的频带的面积强度。 </li><li>获得使用对应于在步骤7.5中得到的工作曲线的方程的解的花色苷的浓度。 </li><li>从在先前获得的浓度得到各花青素的量在每蓝莓步骤，并在步骤6.3中使用的样品溶液的总体积。 </li><li>由十三花青素的内容总结获得花青素的每个蓝莓总量。 </li><li>通过使用在步骤1.3测定重量得到每蓝莓的总花色苷的含量百分比。 </li></ol> 8.施工校准模型为成分含量预测注意:PLS回归，4,5-其是使用潜变体的一种多回归技术的，被用于校准模型的构造用于从观察到的光谱和由化学分析测定的成分含量的各成分。 PLS回归要么与商业程序或自制程序进行。见参考文献5,10为模型建造的详细过程。 <ol><li>检查这对于观察到的光谱的预处理是最有效的准确和强大的预测。 <ol><li>通过应用下列预处理的一个或两个构建校正模型:MSC（多元散射校正），1,2,5 SNV（标准正态变量），1,2,5 MMN（最小-最大规范化），COE（恒定偏移消除），以及所述第一或SG上的二阶导数计算（Savitzky-格雷）方法。-1,2,5-预测从它们的光谱设定与构成模型验证的成分含量。 注意:在MMN，频谱是标准化使得最小和最大值分别成为0和1。在COE，频谱的坐标被移位，使得最小值变为零。 </li><li>计算测定，R 2和残余的预测偏差，RPD的系数之间所观察到的和预测的设置来检查哪个预处理对观测频谱是最有效的验证值。选择预处理提供了更大的组合R 2和RPD。 </li></ol></li><li>检查该波数区域都有效用于通过应用精确和鲁棒的预测，例如，移动窗口PLS技术11查询的有效区域。 注:该程序对应于去除光谱的地方包含的预测没有有效的信息或包含与预测信息的干扰波数的区域。 </li></ol> 9.构建的校准模型的验证注:参见参考文献5,10的构造模型的验证的详细过程。 <ol><li>预测由它们的光谱设置与构建的校准模型与预处理的和有效的预测波数区域的最佳组合验证成分的含量。5,10 </li><li>计算观察到的和预测的R 2和RPD验证集的值。5,10 </li><li>检查是否校准模型的实用性能，12,13 R上的一般标准2&gt; 0.85，RPD&gt; 2.5，是满意的。考虑模型的重建，如果条件不满足。 </li></ol>

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Separation Sci. 34, 150-159 (2011).</li><li>Li, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antioxidant+anthocyanins+screening+through+spectrum-effect+relationships+and+DPPH-HPLC-DAD+analysis+on+nine+cultivars+of+introduced+rabbiteye+blueberry+in+China.">Antioxidant anthocyanins screening through spectrum-effect relationships and DPPH-HPLC-DAD analysis on nine cultivars of introduced rabbiteye blueberry in China.</a> Food Chemistry. 132, 759-765 (2013).</li><li>Sinelli, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+quality+and+nutraceutical+content+of+blueberries+(Vaccinium+corymbosum+L.)+by+near+and+mid-infrared+spectroscopy.">Evaluation of quality and nutraceutical content of blueberries (Vaccinium corymbosum L.) by near and mid-infrared spectroscopy.</a> Postharvest Biol. Technol. 50, 31-36 (2008).</li><li>Giusti, M. M., Wrolsted, R. E., Wrolstad, R. E., Schwartz, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anthocyanins:+characterization+and+measurement+with+UV-visible+spectroscopy.">Anthocyanins: characterization and measurement with UV-visible spectroscopy.</a> Current Protocols in Food Analytical Chemistry. , 1-13 (2001).</li></ol>

We have nothing to disclose.

在协议的一些补充意见说明如下。首先，在步骤1.1中，提及来决定包含在目标的栽培品种。虽然是可能的构建模型覆盖蓝莓来自许多品种或不指定品种，用模型中的预测精度，有时比与单个品种的模型和对于有限品种低得多。还应当指出的是，校准模型应该被构造为从每个生产现场蓝莓得到高预测性能，因为在不同的生产地点收获蓝莓具有影响的预测性能不同的特性。1 其次，在步骤2.3中，提及到选择用于光谱测量的漫反射率模式。还准备用于在分光光度计测量的传输模式。虽然在发送模式中测得的光谱是LSO可用于校准模型的结构，更准确，更可靠的模型可以在漫反射模式在大多数情况下，所测量的光谱来构建。的总有机酸含量不能与在透射模式下测量的光谱来预测。3 第三，蓝莓光谱测量，不推荐在花萼测量光谱由于表面状态和周围的花萼的内容与在其他位置不同。尽管如此，有可能利用在花萼和其它位置都测量光谱的足够数目来构建校准模型。但是，模型的精度是在大多数情况下比那些模型只在比花萼其它位置测量的光谱构造低。 第四，蓝莓的近红外光谱取决于温度。因此，对于精确的预测要么是即时portant总是在相同的环境温度测量光谱或构建模型校准对温度变化的补偿。1 第五，虽然只有R 2和RPD用于这里选择预处理和评估构造模型的性能，一些其它的值，如SEC（校准的标准误差），SEP（预测标准误差），SECV（交叉的标准误差-Validation），RMSEP（均方根预测误差）和RMSECV（交叉验证的均方根误差）通常用于更详细的检查。在我们以前的纸，3，例如，RMSEP和RMSECV被用于选择预处理和评估构造模型的性能。 在蓝莓总糖，总有机酸，总花色苷含量的无损预测是缶如果预测模型被正确构造十二次是可能的。这种技术适用于仅美味蓝莓从所有收获的那些选择，它不能与其他传统的分析技术来实现。-8,9-虽然化学分析的程序可能看起来复杂，它们被包括在流行的分析技术，并且不伴随着很大的困难。重要的是要得到精确的结果为化学分析，因为结果是构造的模型的基础。在这项研究中，高效液相色谱测量的相对标准偏差（相对标准偏差）为1％左右。还必须遵循的基本过程， 例如 如图1中 ，实际应用的模型的构建。 简单快捷的方法，而不是HPLC可应用，用于化学分析。总糖含量和酸度可测，分别用折射（糖度仪）和pH计。的pH差方法16,17适用于总花色苷含量的测定。的简单方法应用使模型的结构更容易虽然由模型预测值的准确度可能比那些由此处所示的HPLC测量的基础上构建的模型预测低。然而，简单的化学分析的基础上构建的模型的精度可以是可实际应用在生产场地和流通过程，因为高的精度不总是需要在那里。用于化学分析的方法中，因此，应根据需要来构造模型的精度选定。 虽然有些水果，如苹果，橙与保证糖度和酸度一般出售，蓝莓尚未售出有保证的品质。其结果，商业蓝莓的质量不似乎并不稳定，至少在日本;有时低质量的蓝莓在市场上出售。由此处所示近红外光谱的无损分析方法有望能，在原则上，运输和销售的蓝莓具有保证品质。 最后，还有这种方法的局限性。首先，如上所述，预测模型的结构是相当麻烦的。此外，该预测模型应被构造为每个站点和栽培每年由于共存成分（其取决于站点和培养的一年）的量的差异影响了预测的精度。因此，需要用于维持预测模型的一些努力。其次，虽然我们已经表明，近红外光谱是，原则上适用于蓝莓的质量检查，这里示出的设备和技术，仅在实验室中使用，而不适用于生产场所是造成的大量浆果在生产现场进行快速检查是不可能的。合适的设备和适合于生产基地和流通过程中使用强大的校正模型发展的实际发展是未来的方向。

近红外（NIR）光谱仪被广泛地作为一种非破坏性的方法用于分析各种水果的内容和蔬菜。1,2无损近红外光谱的分析仅启用美味的水果和蔬菜有保证质量的运费。近红外光谱已被应用到橘子，苹果，甜瓜，樱桃，猕猴桃，芒果，木瓜，桃等知道他们的糖度对应于该总糖含量，酸度，TSC（总固体含量），等等。最近，我们已经报道了近红外光谱的蓝莓的质量评价中的应用。3，我们测得的不仅是总糖含量和相应的酸度总有机酸含量，还总花青素含量。花青素是这被认为是提高人类健康的生物活性成分。这是方便了消费者，如果他们能买好吃的蓝莓其含糖量，交流的保证idity和花青素含量。 在水果和蔬菜的近红外吸收光谱，只有广泛吸收带观察。它们主要是由于纤维和水分的频带。虽然许多弱带由于非自毁靶的各种成分同时观察到的，所观察到的条带不能被分配​​给在大多数情况下，目标的特定组件的特定振动模式。因此，传统的技术来确定使用朗伯 - 比尔定律的特定成分的含量为不有效NIR光谱。相反，校准模型来预测从观测频谱目标成分通过检查所观察到的谱和对应于谱的成分含量之间的相关性使用化学计量的构造的内容。4,5-这里，一个协议来构建和验证模式总糖含量的预测，总有机酸含量相当于酸化TY，并从NIR光谱蓝莓总花青素含量呈现。 图1显示了一般的流程图来构造可靠和稳定的校准模型。的足够数量的样本被收集。他们中的一些被用于模型的结构，而其他被用于构建模型的验证。对于每个采集的样本中，近红外光谱测定，然后将目标分量与传统的破坏性的化学分析方法进行定量分析。这里，高效液相色谱（HPLC）用于糖类，有机酸，和花色苷的化学分析。偏最小二乘（PLS）回归被用于校准模型的结构，其中观察到的谱和由化学确定的成分含量之间的相关性分析进行检测。为了构建可靠的模型用最好的预测能力，安装前后的预处理VED光谱和用于预测的波长区域也被检查。最后，构建模型进行验证，以确认他们足够的预测能力。在验证中，内容从由构造模型（预测值）所观察到的光谱预测与由化学分析（观测值）确定的内容进行比较。如果不能预测的和观察到的值之间找到了足够的相关性，该校准模型应该被重新构建，直到获得足够的相关性。虽然这是优选使用的模型的构建和验证样品的不同的基团如该图所示（外部验证），在同一组的样品既用于建造和验证（交叉验证）时的数样品不够大。 <img alt="图1" src="/files/ftp_upload/53981/53981fig1.jpg" /> Figure 1.流程图用于校准模型的构建和验证。由蓝色和绿色线包围的过程相对应，分别校准模型及其验证的建设。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a>

<table border="0" cellpadding="0" cellspacing="0" width="1315">
	<tbody>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">FT-NIR spectrophotometer</td>
			<td style="width:320px;">Bruker Optics GmbH</td>
			<td style="width:355px;">MPA&#160;</td>
			<td style="width:320px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">High-Performance Liquid Chromatography</td>
			<td style="width:320px;">Shimadzu Corporation</td>
			<td>228-45041-91, 228-45000-31, 228-45018-31,</td>
			<td>For sugar analysis</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;"></td>
			<td style="width:320px;"></td>
			<td>223-04500-31, 228-45010-31, 228-45095-31</td>
			<td>Refractive Index Detector</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">High-Performance Liquid Chromatography</td>
			<td style="width:320px;">Shimadzu Corporation</td>
			<td>228-45041-91, 228-45003-31, 228-45000-31,</td>
			<td>For organic acid analysis</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;"></td>
			<td style="width:320px;"></td>
			<td>228-45018-31, 228-45010-31, 223-04500-31</td>
			<td>Ultraviolet-Visible Detector</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">High-Performance Liquid Chromatography</td>
			<td style="width:320px;">Shimadzu Corporation</td>
			<td>228-45041-91, 228-45018-31, 228-45000-31,</td>
			<td style="width:320px;">For anthocyanin analysis</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;"></td>
			<td style="width:320px;"></td>
			<td>228-45012-31, 228-45119-31, 228-45005-31,</td>
			<td style="width:320px;">Photodiode Array Detector</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;"></td>
			<td style="width:320px;"></td>
			<td>228-45009-31</td>
			<td style="width:320px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">pH meter</td>
			<td style="width:320px;">Mettler-Toledo</td>
			<td>30019028</td>
			<td style="width:320px;">S220, Automatic temperature compensation</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Ultra-pure water treatment equipment</td>
			<td style="width:320px;">ORGANO Corporation</td>
			<td style="width:355px;">ORG-ULXXXM1; PRA-0015-0V0</td>
			<td style="width:320px;">PURELAB ultra; PURELITE</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Biomedical Freezers&#160;</td>
			<td style="width:320px;">SANYO</td>
			<td style="width:355px;">2-6780-01</td>
			<td style="width:320px;">MDF-U338</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Ultra-Low Temperature Freezer</td>
			<td style="width:320px;">Panasonic healthcare Co.,Ltd.</td>
			<td style="width:355px;">KM-DU73Y1</td>
			<td style="width:320px;">-80&#176;C</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Vacuum lyophilizer</td>
			<td style="width:320px;">IWAKI GLASS Co.,Ltd</td>
			<td>119770</td>
			<td style="width:320px;">DRC-3L;FRD-82M</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Homoginizer</td>
			<td style="width:320px;">Microtec Co., Ltd.&#160;</td>
			<td>Physcotron</td>
			<td style="width:320px;"></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Ultracentrifuge</td>
			<td style="width:320px;">Hitachi Koki Co.,Ltd</td>
			<td style="width:355px;">S204567</td>
			<td>CF15RXII</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Mini-centrifuge</td>
			<td style="width:320px;">LMS CO.,LTD.</td>
			<td>KN3136572</td>
			<td>MCF-2360</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Centrifuge</td>
			<td style="width:320px;">Kokusan Co.,Ltd</td>
			<td style="width:355px;">2-5534-01</td>
			<td>H-103N</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Filter Paper&#160;</td>
			<td style="width:320px;">Advantec</td>
			<td style="width:355px;">1521070</td>
			<td>5B, Eqivalent to Whatman 40</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Sep-Pak C18 column</td>
			<td style="width:320px;">Waters Corporation Milford</td>
			<td style="width:355px;">WAT020515</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Sep-Pak CM column</td>
			<td style="width:320px;">Waters Corporation Milford</td>
			<td style="width:355px;">WAT020550</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Sep-Pak QMA column</td>
			<td style="width:320px;">Waters Corporation Milford</td>
			<td style="width:355px;">WAT020545</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Centrifugal Filter Unit</td>
			<td style="width:320px;">Merck Millipore Corporation</td>
			<td style="width:355px;">R2SA18503</td>
			<td>PVDF, 0.45 &#956;m</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Microtube</td>
			<td style="width:320px;">As One Corporation</td>
			<td style="width:355px;">1-1600-02</td>
			<td>PP, 2 mL</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Syringe Filter</td>
			<td style="width:320px;">GE Healthcare CO.,LTD.</td>
			<td style="width:355px;">6788-1304</td>
			<td>PP, 0.45 &#956;m</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Sucrose</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">194-00011</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Glucose</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">049-31165</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Fructose</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">123-02762</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Citric acid</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">036-05522</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Malic acid</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">355-17971</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Succinic acid&#160;</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">190-04332</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Quinic acid</td>
			<td style="width:320px;">Alfa Aesar, A Johnson Matthey Company</td>
			<td style="width:355px;">10176328</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;">Phosphoric acid</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">162-20492</td>
			<td>HPLC-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Trifluoroacetic acid</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">208-02746</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Methanol</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">131-01826</td>
			<td>Reagent-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Acetonitrile</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">015-08633</td>
			<td>HPLC-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Grade cyanidin-3-O-glucoside chloride</td>
			<td style="width:320px;">Wako Pure Chemical Industries,Ltd</td>
			<td style="width:355px;">306-37661</td>
			<td>HPLC-grade</td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Software for analyses</td>
			<td style="width:320px;">Bruker Optics GmbH</td>
			<td style="width:355px;">OPUS ver. 6.5</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Softoware for preprocessing</td>
			<td style="width:320px;">Microsoft</td>
			<td style="width:355px;">Excel powered by Visual Basic for Applications</td>
			<td></td>
		</tr>
		<tr height="33">
			<td height="33" style="height:33px;width:320px;">Software for construction of models</td>
			<td style="width:320px;"></td>
			<td style="width:355px;">Freemat 4.0</td>
			<td>http://freemat.sourceforge.net/</td>
		</tr>
	</tbody>
</table>

construction of models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy based on hplc measurements

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared spectroscopy is used to predict nondestructively total sugar, total organic acid, and total anthocyanin content in each blueberry. The technique is expected to enable the selection of only delicious blueberries from all harvested ones. The near-infrared absorption spectra of blueberries are measured with the diffuse reflectance mode at the positions not on the calyx. The ingredient contents of a blueberry determined by high-performance liquid chromatography are used to construct models to predict the ingredient contents from observed spectra. Partial least squares regression is used for the construction of the models. It is necessary to properly select the pretreatments for the observed spectra and the wavelength regions of the spectra used for analyses. Validations are necessary for the constructed models to confirm that the ingredient contents are predicted with practical accuracies. Here we present a protocol to construct and validate the models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy.

图2示出了作为一个例子的一组，其中的70蓝莓光谱同时显示蓝莓的近红外吸收光谱的。由于带绝对分配到糖类，有机酸，或花青素未在近红外光谱观察到，传统的朗伯 - 比尔定律并不适用于量化的成分含量。因此，模型的成分的含量预测的建设是必要的。 图3示出了用于糖的蓝莓定量分析典型色谱图。从上面三个面板分别是蔗糖，葡萄糖和果糖的标准溶液的色谱图。底部面板显示的样品溶液的色谱图， 即蓝莓提取物。糖的样品溶液中的种类和浓度从保留时间已知一次观察到的峰面积的强度。总糖含量为蔗糖，葡萄糖和果糖含量的总和获得。 图4显示了色谱图的有机酸在蓝莓分析的一个例子。通过参照标准溶液（此处未示出）的色谱图中，样品溶液的种类和有机酸的浓度是已知的。在图例示出的观察到的峰的分配，两个峰观察到在标准和样品溶液的色谱图奎尼酸。他们可能是分配给奎尼酸异构体。总有机酸含量为柠檬酸，奎尼酸，苹果酸和琥珀酸含量的总和获得。 图5示出色谱用于花青素在蓝莓分析的一个例子。对应许多各色峰NT一种花青素观察。由于洗脱的对这些花青素的顺序报告14,15如表1所示，观察到的峰可被分配给各个花青素。总花色苷含量为13种花色苷的含量之和获得的。 校准模型从观测频谱构造和化学确定成分含量。 表2显示了预处理的检查的一个例子。六型预处理，包括"无（无预处理）"分别在12,500-3,600厘米-1的固定波数区域检查使用光谱总糖含量的校正模型的构建。不同预处理导致不同的预测表演。模型的性能，其中R 2和RPD进行评价。给出最佳predi类型预处理的ction性能选择。在表2中 ，"二衍生物+ MSC"，它的二阶导数计算之后装置的MSC，提供了最好的结果。然后用于模型构建的波数区域由与固定预处理变化的区域检查。 图6示出作为一个例子的校准模型对于总糖含量，其中示出由近红外光谱和那些通过HPLC测定的预测值之间的相关性的交叉验证的结果。该模型是"的二阶导数+ MSC"作为预处理和使用8,539-7,775厘米-1区域的光谱的构成。该模型的预测表现为R 2 = 0.85和RPD = 2.6，略高于用于实际使用的条件。在本实施例中，用于模型构建的样本数为30，这是太小了利弊truct高性能车型。 图7示出了作为一个例子的校准模型对于总有机酸含量，其中示出由近红外光谱和那些通过HPLC测定的预测值之间的相关性的交叉验证的结果。该模型是"第一导数+ MSC"作为预处理和使用7,505-5,446和4,605-4,242厘米-1光谱的区域构成。该模型的预测表现为R 2 = 0.92和RPD = 3.6，这是足以用于实际应用。 图8示出作为例子的校准模型对于总花色苷含量，与由近红外光谱和那些通过HPLC测定的预测值之间的相关性的外部验证的结果。该模型是"第一D构造erivative"作为前处理，并使用12,489-6,094和4,605-4,242厘米-1的光谱的区域。该模型的预测表现为R 2 = 0.95和RPD = 4.4，它显示了构建的模型的相当良好的性能。由于花色苷主要存在于蓝莓的剥离，故容易与漫反射测量观察到，虽然它在一个蓝莓含量不高。在图8所示的良好性能也将通过用于模型构建的大量样品（70）造成的。 <img alt="图2" src="/files/ftp_upload/53981/53981fig2.jpg" /> 蓝莓图2.近红外吸收光谱的70蓝莓光谱同时显示。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig2large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> 图3.色谱食糖蓝莓中的定量分析（ 一 ）蔗糖，（B）葡萄糖标准溶液的色谱图，（C）果糖和（D）样品溶液的色谱图。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig3large.jpg" target="_blank">请点击这里查看更大的版本这个数字。</a> <img alt="图4" src="/files/ftp_upload/53981/53981fig4.jpg" /> 图4.色谱对有机酸的蓝莓的定量分析。观察到的峰对应于酸柠檬酸（1），苹果酸（2），奎尼酸（0-3），和琥珀酸（4）。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig4large.jpg" target="_blank">PLE</a>ASE点击此处查看该图的放大版本。 <img alt="图5" src="/files/ftp_upload/53981/53981fig5.jpg" /> 图5. 色谱对蓝莓花青素的定量分析。观察到的峰被分配到表1，其中显示标准的保留时间为每个花青素所列个人花青素。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig5large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图6" src="/files/ftp_upload/53981/53981fig6.jpg" /> 图6.型号为总糖含量的交叉验证的结果。通过近红外光谱预测值被暗算那些用HPLC法测定。R 2 = 0.85，RPD =获得2.6。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig6large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图7" src="/files/ftp_upload/53981/53981fig7.jpg" /> 图7.型号为总有机酸含量的交叉验证的结果。通过近红外光谱预测值是对那些用HPLC法测定绘制。R 2 = 0.92和RPD = 3.6得到。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig7large.jpg" target="_blank">请点击此处查看大图这一数字。</a> <img alt="图8" src="/files/ftp_upload/53981/53981fig8.jpg" /> 图8.模型总花青素含量的外部验证的结果。通过近红外光谱预测值是对那些ð绘制通过etermined HPLC。R 2 = 0.95，RPD =得到4.4。 <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53981/53981fig8large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td> 式 </td><td> 花青素 </td><td> 代表保留时间（分钟） </td></tr><tr><td> C 21 H 21 O 12 </td><td>飞燕草-3-半乳糖苷Ø </td><td> 17.3 </td></tr><tr><td> C 21 H 21 O 12 </td><td>飞燕-3-O-葡萄糖苷</td><td> 19.7 </td></tr><tr><td> C 21 H 21 O 11 </td><td>矢车菊-3-O-半乳糖苷</td><td> 22.8 </td></tr><tr><td> C 20 H 19 O 11 </td><td>飞燕草-3- <eM&gt;Ø-arabinoside </td><td> 23.6 </td></tr><tr><td> C 21 H 21 O 11 </td><td>矢车菊-3-O-葡萄糖苷</td><td> 24.5 </td></tr><tr><td> C 22 H 23 O 12 </td><td> Petunidin -3-O-半乳糖苷</td><td> 28.7 </td></tr><tr><td> C 20 H 19 O 10 </td><td>矢车菊-3-O- -arabinoside </td><td> 31.3 </td></tr><tr><td> C 22 H 23 O 12 </td><td> Petunidin -3-O-葡萄糖苷</td><td> 36.0 </td></tr><tr><td> C 22 H 23 O 11 </td><td> Peonidin -3-O-半乳糖苷</td><td> 37.0 </td></tr><tr><td> C 21 H 21 O 11 </td><td> Petunidin -3-O- -arabinoside </td><td> 40.8 </td></tr><tr><td> C 22 H 23 O 11 </td><td> Peonidin -3-O-葡萄糖苷</td><td> 43.7 </td></tr><tr><td> C 23 H 25 O 12 </td><td>二甲花翠素-3-半乳糖苷Ø </td><td> 45.0 </td></tr><tr><td> C 23 H 25 O 12 </td><td>二甲花翠素-3-O-葡萄糖苷</td><td> 49.6 </td></tr></tbody></table> 载于蓝莓表1.主要花青素。也都列在HPLC分析本实验条件下，该代表保留时间。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td> 预处理 </td><td> 用于分析波数区域（-1） </td><td> RPD </td><td> R 2 </td></tr><tr><td>没有</td><td> 12,500-3,600 </td><td> 1.7 </td><td> 0.69 </td></tr><tr><td>二阶导数</td><td> 12,500-3,600 </td><td> 2.6 </td><td&gt; 0.85 </td></tr><tr><td>一阶导数</td><td> 12,500-3,600 </td><td> 2.5 </td><td> 0.84 </td></tr><tr><td> MSC </td><td> 12,500-3,600 </td><td> 2.3 </td><td> 0.81 </td></tr><tr><td>二阶导数+ MSC </td><td> 12,500-3,600 </td><td> 2.8 </td><td> 0.88 </td></tr><tr><td>一阶导数+ MSC </td><td> 12,500-3,600 </td><td> 2.7 </td><td> 0.87 </td></tr></tbody></table> 表2.对观测频谱的预处理预测性能的依赖性的检查。R 2和RPD为总糖含量的预测中列出。

Watch this Scientific Journal Video about Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements at JoVE.co

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

We present here a protocol to construct and validate models for nondestructive prediction of total sugar, total organic acid, and total anthocyanin content in individual blueberries by near-infrared spectroscopy.

基于HPLC测量通过近红外光谱对在蓝莓成分的含量无损预测模型的构建

Nondestructive prediction of ingredient contents of farm products is useful to ship and sell the products with guaranteed qualities. Here, near-infrared ...

construction-models-for-nondestructive-prediction-ingredient-contents

Nanyang Technological University

Research

JoVE Journal

Chemistry

10.5K 次观看.  Tokyo University of Agriculture and Technology. We present here a protocol to construct and validate models for nondestructive prediction of total sugar, total organic acid, and total anthocyanin content in individual blueberries by near-infrared spectroscopy. 

视频: Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Profiling the Triacylglyceride Contents in Bat Integumentary Lipids by Preparative Thin Layer Chromatography and MALDI-TOF Mass Spectrometry

The mammalian integument includes sebaceous glands that secrete an oily material onto the skin surface. Sebum production is part of the innate immune system that is protective against pathogenic microbes. Abnormal sebum production and chemical composition are also a clinical symptom of specific skin diseases. Sebum contains a complex mixture of lipids, including triacylglycerides, which is species-specific. The broad chemical properties exhibited by diverse lipid classes hinder the specific determination of sebum composition. Analytical techniques for lipids typically require chemical derivatizations that are labor-intensive and increase sample preparation costs. This paper describes how to extract lipids from mammalian integument, separate broad lipid classes by thin-layer chromatography, and profile the triacylglyceride contents using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. This robust method enables a direct determination of the triacylglyceride profiles among species and individuals, and it can be readily applied to any taxonomic group of mammals.

Mammalian integument contains solvent-extractable lipids that can provide chemical compositions characteristic of individual species. This paper presents a routine method for separating broad lipid classes isolated from integumentary tissues using thin layer chromatography and determining the triacylglyceride profile by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

用制备薄层色谱和MALDI-TOF质谱分析的三酸甘油酯含量的蝙蝠外皮血脂

The mammalian integument includes sebaceous glands that secrete an oily material onto the skin surface. Sebum production is part of the innate immune ...

科研

化学

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Antifreeze proteins (AFPs) are expressed in a variety of cold-hardy organisms to prevent or slow internal ice growth. AFPs bind to specific planes of ice through their ice-binding surfaces. Fluorescence-based ice plane affinity (FIPA) analysis is a modified technique used to determine the ice planes to which the AFPs bind. FIPA is based on the original ice-etching method for determining AFP-bound ice-planes. It produces clearer images in a shortened experimental time. In FIPA analysis, AFPs are fluorescently labeled with a chimeric tag or a covalent dye then slowly incorporated into a macroscopic single ice crystal, which has been preformed into a hemisphere and oriented to determine the a- and c-axes. The AFP-bound ice hemisphere is imaged under UV light to visualize AFP-bound planes using filters to block out nonspecific light. Fluorescent labeling of the AFPs allows real-time monitoring of AFP adsorption into ice. The labels have been found not to influence the planes to which AFPs bind. FIPA analysis also introduces the option to bind more than one differently tagged AFP on the same single ice crystal to help differentiate their binding planes. These applications of FIPA are helping to advance our understanding of how AFPs bind to ice to halt its growth and why many AFP-producing organisms express multiple AFP isoforms.

Antifreeze proteins (AFPs) bind to specific planes of ice to prevent or slow ice growth. Fluorescence-based ice plane affinity (FIPA) analysis is a modification of the original ice-etching method for determination of AFP-bound ice planes. AFPs are fluorescently labeled, incorporated into macroscopic single ice crystals, and visualized under UV light.

通过荧光基冰平面亲和力确定防冻蛋白的冰结合平面

Antifreeze proteins (AFPs) are expressed in a variety of cold-hardy organisms to prevent or slow internal ice growth. AFPs bind to specific planes of ice ...

Dissolution Dynamic Nuclear Polarization Instrumentation for Real-time Enzymatic Reaction Rate Measurements by NMR

The main limitation of NMR-based investigations is low sensitivity. This prompts for long acquisition times, thus preventing real-time NMR measurements of metabolic transformations. Hyperpolarization via dissolution DNP circumvents part of the sensitivity issues thanks to the large out-of-equilibrium nuclear magnetization stemming from the electron-to-nucleus spin polarization transfer. The high NMR signal obtained can be used to monitor chemical reactions in real time. The downside of hyperpolarized NMR resides in the limited time window available for signal acquisition, which is usually on the order of the nuclear spin longitudinal relaxation time constant, T1, or, in favorable cases, on the order of the relaxation time constant associated with the singlet-state of coupled nuclei, TLLS. Cellular uptake of endogenous molecules and metabolic rates can provide essential information on tumor development and drug response. Numerous previous hyperpolarized NMR studies have demonstrated the relevancy of pyruvate as a metabolic substrate for monitoring enzymatic activity in vivo. This work provides a detailed description of the experimental setup and methods required for the study of enzymatic reactions, in particular the pyruvate-to-lactate conversion rate in presence of lactate dehydrogenase (LDH), by hyperpolarized NMR.

The sensitivity enhancement provided by dissolution dynamic nuclear polarization (DNP) enables following metabolic processes in real time by NMR and MRI. The characteristics and performances of a dedicated dissolution DNP setup designed for study enzymatic reactions are discussed.

溶出动态核极化仪器用NMR实时酶反应速率测量

The main limitation of NMR-based investigations is low sensitivity. This prompts for long acquisition times, thus preventing real-time NMR measurements of ...

HPLC-based Assay to Monitor Extracellular Nucleotide/Nucleoside Metabolism in Human Chronic Lymphocytic Leukemia Cells

This method describes a sensitive, specific, reliable and reproducible reverse phase high-performance liquid chromatography (RP-HPLC) assay developed and validated for the quantification of extracellular purine nucleotides and nucleosides produced by purified chronic lymphocytic leukemia (CLL) cells under different culture conditions. The chromatographic separation of adenosine 5&#39;-monophosphate (AMP), adenosine (ADO) and inosine (INO) is performed at RT on a silica-based, reversed-phase column that is used for polar compound retention. The method includes a binary mobile phase, which consists of 7 mM ammonium acetate and acetonitrile with a flow rate of 1.00 ml/min. The eluates are monitored using a Photodiode Array UV detector set at 260 nm. A standard calibration curve is generated to calculate the equation for the analytical quantification of each purine compound. System control, data acquisition and analysis are then performed. Applying this protocol, AMP, INO and ADO elute at 7, 11 and 11.9 min, respectively, and the total run time for each sample is 20 min. This protocol may be applied to different cell types and cell lines (both suspension and adherent), using culture media as matrix. The advantages are easy and fast sample preparation and the requirement of a small amount of supernatant for analysis. Furthermore, the use of a serum-free medium allows skipping the protein precipitation step with acetonitrile that impacts the final concentration of purine compounds. One of the limitations of the method is the requirement of the equilibration column run before each single sample run, making the total run time of the experiment longer and preventing high throughput screening applications.

The protocol described here represents an easy and reproducible method that employs reverse phase high-performance liquid chromatography (RP-HPLC) to measure purine metabolism on chronic lymphocytic leukemia (CLL) cells cultured under different conditions.

HPLC-基于化验，以监测外核苷酸/核苷代谢的人类慢性淋巴细胞白血病细胞

This method describes a sensitive, specific, reliable and reproducible reverse phase high-performance liquid chromatography (RP-HPLC) assay developed and ...

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the results of femtosecond X-ray free electron laser (XFEL) experiments. An increasing number of experimental observations have shown that although nuclear motion can be negligible, given a short enough incident pulse duration, electronic motion cannot be ignored. The current and widely accepted models assume that although electrons undergo dynamics driven by interaction with the pulse, their motion could largely be considered &#39;random&#39;. This would then allow the supposedly incoherent contribution from the electronic motion to be treated as a continuous background signal and thus ignored. The original aim of our experiment was to precisely measure the change in intensity of individual Bragg peaks, due to X-ray induced electronic damage in a model system, crystalline C60. Contrary to this expectation, we observed that at the highest X-ray intensities, the electron dynamics in C60 were in fact highly correlated, and over sufficiently long distances that the positions of the Bragg reflections are significantly altered. This paper describes in detail the methods and protocols used for these experiments, which were conducted both at the Linac Coherent Light Source (LCLS) and the Australian Synchrotron (AS) as well as the crystallographic approaches used to analyse the data.

We describe an experiment designed to probe the electronic damage induced in nanocrystals of Buckminsterfullerene (C60) by intense, femtosecond pulses of X-rays. The experiment found that, surprisingly, rather than being stochastic, the X-ray induced electron dynamics in C60 are highly correlated, extending over hundreds of unit cells within the crystals1.

在纳米富勒的飞秒衍射实验的长程电子关联的测量

The precise details of the interaction of intense X-ray pulses with matter are a topic of intense interest to researchers attempting to interpret the ...

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

We demonstrate a method of quantification and detection of parasites in aqueous red blood cells (RBCs) by using a simple benchtop Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectrometer in conjunction with Multivariate Data Analysis (MVDA). 3D7 P. falciparum were cultured to 10% parasitemia ring stage parasites and used to spike fresh donor isolated RBCs to create a dilution series between 0&#8211;1%. 10 &#181;L of each sample were placed onto the center of the ATR diamond window to acquire the spectrum. The sample data was treated to improve the signal to noise ratio and to remove the contribution of water, and then the second derivative was applied to resolve spectral features. The data were then analyzed using two types of MVDA: first Principal Component Analysis (PCA) to determine any outliers and then Partial Least Squares Regression (PLS-R) to build the quantification model.

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Here, we present a protocol for the detection and quantification of Plasmodium falciparum in infected aqueous red blood cells using an attenuated total reflection infrared spectrometer and multivariate data analysis.

衰减全反射红外光谱和多变量数据分析检测和定量水红血球中恶性疟原虫

We demonstrate a method of quantification and detection of parasites in aqueous red blood cells (RBCs) by using a simple benchtop Attenuated Total ...

Photoelectron Imaging of Anions Illustrated by 310 Nm Detachment of F&#8722;

Anion photoelectron imaging is a very efficient method for the study of energy states of bound negative ions, neutral species and interactions of unbound electrons with neutral molecules/atoms. State-of-the-art in vacuo anion generation techniques allow application to a broad range of atomic, molecular, and cluster anion systems. These are separated and selected using time-of-flight mass spectrometry. Electrons are removed by linearly polarized photons (photo detachment) using table-top laser sources which provide ready access to excitation energies from the infra-red to the near ultraviolet. Detecting the photoelectrons with a velocity mapped imaging lens and position sensitive detector means that, in principle, every photoelectron reaches the detector and the detection efficiency is uniform for all kinetic energies. Photoelectron spectra extracted from the images via mathematical reconstruction using an inverse Abel transformation reveal details of the anion internal energy state distribution and the resultant neutral energy states. At low electron kinetic energy, typical resolution is sufficient to reveal energy level differences on the order of a few millielectron-volts, i.e., different vibrational levels for molecular species or spin-orbit splitting in atoms. Photoelectron angular distributions extracted from the inverse Abel transformation represent the signatures of the bound electron orbital, allowing more detailed probing of electronic structure. The spectra and angular distributions also encode details of the interactions between the outgoing electron and the residual neutral species subsequent to excitation. The technique is illustrated by the application to an atomic anion (F&#8722;), but it can also be applied to the measurement of molecular anion spectroscopy, the study of low lying anion resonances (as an alternative to scattering experiments) and femtosecond (fs) time resolved studies of the dynamic evolution of anions.

Here, we present a protocol for photoelectron imaging of anionic species. Anions generated in vacuo and separated by mass spectrometry are probed using velocity mapped photoelectron imaging, providing details of anion and neutral energy levels, anion and neutral structure and the nature of the anion electronic state.

由 F− 310 Nm 支队所示的阴离子光电子成像

Anion photoelectron imaging is a very efficient method for the study of energy states of bound negative ions, neutral species and interactions of unbound ...

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

While radioimmunotherapy (RIT) is a promising approach for the treatment of cancer, the long pharmacokinetic half-life of radiolabeled antibodies can result in high radiation doses to healthy tissues. Perhaps not surprisingly, several different strategies have been developed to circumvent this troubling limitation. One of the most promising of these approaches is pretargeted radioimmunotherapy (PRIT). PRIT is predicated on decoupling the radionuclide from the immunoglobulin, injecting them separately, and then allowing them to combine in vivo at the target tissue. This approach harnesses the exceptional tumor-targeting properties of antibodies while skirting their pharmacokinetic drawbacks, thereby lowering radiation doses to non-target tissues and facilitating the use of radionuclides with half-lives that are considered too short for use in traditional radioimmunoconjugates. Over the last five years, our laboratory and others have developed an approach to in vivo pretargeting based on the inverse electron-demand Diels-Alder (IEDDA) reaction between trans-cyclooctene (TCO) and tetrazine (Tz). This strategy has been successfully applied to pretargeted positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging with a variety of antibody-antigen systems. In a pair of recent publications, we have demonstrated the efficacy of IEDDA-based PRIT in murine models of pancreatic ductal adenocarcinoma and colorectal carcinoma. In this protocol, we describe protocols for PRIT using a 177Lu-DOTA-labeled tetrazine radioligand ([177Lu]Lu-DOTA-PEG7-Tz) and a TCO-modified variant of the colorectal cancer targeting huA33 antibody (huA33-TCO). More specifically, we will describe the construction of huA33-TCO, the synthesis and radiolabeling of [177Lu]Lu-DOTA-PEG7-Tz, and the performance of in vivo biodistribution and longitudinal therapy studies in murine models of colorectal carcinoma.

This protocol describes the synthesis and characterization of a trans-cyclooctene (TCO)-modified antibody and a 177Lu-labeled tetrazine (Tz) radioligand for pretargeted radioimmunotherapy (PRIT). In addition, it details the use of these two constructs for in vivo biodistribution and longitudinal therapy studies in a murine model of colorectal cancer.

基于逆电子需求函数-alder 反应的隐形放射免疫治疗

While radioimmunotherapy (RIT) is a promising approach for the treatment of cancer, the long pharmacokinetic half-life of radiolabeled antibodies can ...

Performing Spectroscopy on Plasmonic Nanoparticles with Transmission-Based Nomarski-Type Differential Interference Contrast Microscopy

Differential interference contrast (DIC) microscopy is a powerful imaging tool that is most commonly employed for imaging microscale objects using visible-range light. The purpose of this protocol is to detail a proven method for preparing plasmonic nanoparticle samples and performing single particle spectroscopy on them with DIC microscopy. Several important steps must be followed carefully in order to perform repeatable spectroscopy experiments. First, landmarks can be etched into the sample substrate, which aids in locating the sample surface and in tracking the region of interest during experiments. Next, the substrate must be properly cleaned of debris and contaminants that can otherwise hinder or obscure examination of the sample. Once a sample is properly prepared, the optical path of the microscope must be aligned, using Kohler Illumination. With a standard Nomarski style DIC microscope, rotation of the sample may be necessary, particularly when the plasmonic nanoparticles exhibit orientation-dependent optical properties. Because DIC microscopy has two inherent orthogonal polarization fields, the wavelength-dependent DIC contrast pattern reveals the orientation of rod-shaped plasmonic nanoparticles. Finally, data acquisition and data analyses must be carefully performed. It is common to represent DIC-based spectroscopy data as a contrast value, but it is also possible to present it as intensity data. In this demonstration of DIC for single particle spectroscopy, the focus is on spherical and rod-shaped gold nanoparticles.

The goal of this protocol is to detail a proven approach for the preparation of plasmonic nanoparticle samples and for performing single particle spectroscopy on them with differential interference contrast (DIC) microscopy.

基于传输的野武学型差分干涉对比显微镜对等离子体纳米粒子的光谱处理

Differential interference contrast (DIC) microscopy is a powerful imaging tool that is most commonly employed for imaging microscale objects using ...

Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy

Atomic force microscopy (AFM)-based single molecule force spectroscopy is an ideal tool for investigating the interactions between a single polymer and surfaces. For a true single molecule experiment, covalent attachment of the probe molecule is essential because only then can hundreds of force-extension traces with one and the same single molecule be obtained. Many traces are in turn necessary to prove that a single molecule alone is probed. Additionally, passivation is crucial for preventing unwanted interactions between the single probe molecule and the AFM cantilever tip as well as between the AFM cantilever tip and the underlying surface. The functionalization protocol presented here is reliable and can easily be applied to a variety of polymers. Characteristic single molecule events (i.e., stretches and plateaus) are detected in the force-extension traces. From these events, physical parameters such as stretching force, desorption force and desorption length can be obtained. This is particularly important for the precise investigation of stimuli-responsive systems at the single molecule level. As exemplary systems poly(ethylene glycol) (PEG), poly(N-isopropylacrylamide) (PNiPAM) and polystyrene (PS) are stretched and desorbed from SiOx (for PEG and PNiPAM) and from hydrophobic self-assembled monolayer surfaces (for PS) in aqueous environment.

Covalent attachment of probe molecules to atomic force microscopy (AFM) cantilever tips is an essential technique for the investigation of their physical properties. This allows us to determine the stretching force, desorption force and length of polymers via AFM-based single molecule force spectroscopy with high reproducibility.

基于AFM的力光谱单分子共价附价

Atomic force microscopy (AFM)-based single molecule force spectroscopy is an ideal tool for investigating the interactions between a single polymer and ...