Name: construction of models for nondestructive prediction of ingredient contents in blueberries by near-infrared spectroscopy based on hplc measurements
Uploaded: 2016-06-28T14:00:00.000+00:00
Duration: 10 min 25 s
Description: 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

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>

<ol>
	<li>Ozaki, Y., McClure, W. F., Christy, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Near-infrared Spectroscopy in Food Science and Technology. , (2007).</li><li>Sun, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Infrared Spectroscopy for Food Quality Analysis and Control. , (2009).</li><li>Bai, W., Yoshimura, N., Takayanagi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+analysis+of+ingredients+of+blueberry+fruits+by+near+infrared+spectroscopy.">Quantitative analysis of ingredients of blueberry fruits by near infrared spectroscopy.</a> J. Near Infrared Spectrosc. 22, 357-365 (2014).</li><li>Hasegawa, T., Tasumi, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Chemometrics in infrared spectroscopic analysis. In: Introduction to Experimental Infrared Spectroscopy. , 97-113 (2015).</li><li>Varmuza, K., Filzmoser, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Introduction to Multivariate Statistical Analysis in Chemometrics. , (2009).</li><li>Kubelka, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+contributions+to+the+optics+of+intensely+light-scattering+materials.+Part+I.">New contributions to the optics of intensely light-scattering materials. Part I.</a> J. Opt. Soc. Am. 38, 448-457 (1948).</li><li>Juang, R. H., Storey, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+determination+of+the+extent+of+neutralization+of+carboxylic+acid+functionality+in+carbopol+974P+NF+by+diffuse+reflectance+fourier+transform+infrared+spectrometry+using+Kubelka-Munk+function.">Quantitative determination of the extent of neutralization of carboxylic acid functionality in carbopol 974P NF by diffuse reflectance fourier transform infrared spectrometry using Kubelka-Munk function.</a> Pharm Res. 15, 1714-1720 (1998).</li><li>Ogiwara, I., Ohtsuka, Y., Yoneda, Y., Sakurai, K., Hakoda, N., Shimura, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extraction+method+by+water+followed+by+microwave+heating+for+analyzing+sugars+in+strawberry+fruits.">Extraction method by water followed by microwave heating for analyzing sugars in strawberry fruits.</a> J. Jpn. Soc. Hort. Sci. 68, 949-953 (1999).</li><li>Che, J., Suzuki, S., Ishikawa, S., Koike, H., Ogiwara, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fruit+ripening+and+quality+profile+of+64+cultivars+in+three+species+of+blueberries+grown+in+Tokyo.">Fruit ripening and quality profile of 64 cultivars in three species of blueberries grown in Tokyo.</a> Hort. Res. (Japan). 8, 257-265 (2009).</li><li>Pomerantsev, A. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Chemometrics in Excel. , (2014).</li><li>Jiang, H. J., Berry, R. J., Siesler, H. W., Ozaki, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wavelength+Interval+Selection+in+Multicomponent+spectral+analysis+by+moving+window+partial+least-squares+regression+with+applications+to+mid-infrared+and+near-infrared+spectroscopic+data.">Wavelength Interval Selection in Multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data.</a> Anal. Chem. 74, 3555-3565 (2002).</li><li>Edney, M. J., Morgan, J. E., Williams, P. C., Campbell, L. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+feed+barley+by+near+infrared+reflectance+spectroscopy.">Analysis of feed barley by near infrared reflectance spectroscopy.</a> J. Near-Infrared Spectrosc. 2, 33-41 (1994).</li><li>Mathison, G. W., 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=Prediction+of+composition+and+ruminal+degradability+characteristics+of+barley+straw+by+near+infrared+reflectance+spectroscopy.">Prediction of composition and ruminal degradability characteristics of barley straw by near infrared reflectance spectroscopy.</a> Can. J. Anim. Sci. 79, 519-523 (1999).</li><li>Chiara, F., 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=Analysis+of+anthocyanins+in+commercial+fruit+juices+by+using+nano-liquid+chromatography+electrospray-mass+spectrometry+and+high+performance+liquid+chromatography+with+UV-vis+detector.">Analysis of anthocyanins in commercial fruit juices by using nano-liquid chromatography electrospray-mass spectrometry and high performance liquid chromatography with UV-vis detector.</a> J. 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 <p class="jove_conte...

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.bdigitaluss.remotexs.co/files/ftp_upload/53981/53981fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a>

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

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

Universidad San Sebastian

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

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

风土通过概念葡萄果实代谢组学和转录组解释的

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to ...

科研

遗传学

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

RGB 和光谱根成像植物表型和生理研究: 实验装置和成像协议

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

环境科学

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that belongs to the Direct-Injection Mass Spectrometric (DIMS) technologies. These techniques allow the rapid determination of volatile organic compounds (VOCs), assuring high sensitivity and accuracy. In general, PTR-MS requires neither sample preparation nor sample destruction, allowing real time and non-invasive analysis of samples. PTR-MS are exploited in many fields, from environmental and atmospheric chemistry to medical and biological sciences. More recently, we developed a methodology based on coupling PTR-ToF-MS with an automated sampler and tailored data analysis tools, to increase the degree of automation and, consequently, to enhance the potential of the technique. This approach allowed us to monitor bioprocesses (e.g. enzymatic oxidation, alcoholic fermentation), to screen large sample sets (e.g. different origins, entire germoplasms) and to analyze several experimental modes (e.g. different concentrations of a given ingredient, different intensities of a specific technological parameter) in terms of VOC content. Here, we report the experimental protocols exemplifying different possible applications of our methodology: i.e. the detection of VOCs released during lactic acid fermentation of yogurt (on-line bioprocess monitoring), the monitoring of VOCs associated with different apple cultivars (large-scale screening), and the in vivo study of retronasal VOC release during coffee drinking (nosespace analysis).

Proton Transfer Reaction Time of Flight Mass Spectrometry allows high-sensitivity, rapid and non-invasive analysis of volatile organic compounds. To demonstrate its potential, we give three examples: lactic acid fermentation of yogurt (on-line bioprocess monitoring), different apple genotypes (large-scale screening), and retronasal space after drinking coffee (nosespace analysis).

PTR-ToF-MS结合自动采样系统和食品研究定制数据分析:生物过程监测，筛选和鼻空间分析

Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that ...

化学

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data. Thus, we propose to use partial least squares regression method, which has traditionally been used to reduce the data dimensionality and eliminate the collinearity between the original features. We implement the method for predicting the o-cresol concentration during the production of polyphenylene ether. The proposed approach offers the following advantages over component regression prediction methods: 1) partial least squares regression solves the multicollinearity problem of the independent variables and effectively avoids overfitting, which occurs in a regression analysis due to the high correlation between the independent variables; 2) the use of the near-infrared spectra results in high accuracy because it is a non-destructive and non-polluting method to obtain information at microscopic and molecular scales.

The protocol describes a method of predicting o-cresol concentration during the production of polyphenylene ether using near-infrared spectroscopy and partial least squares regression. To describe the process more clearly and completely, an example of predicting the o-cresol concentration during the production of polyphenylene is used to clarify the steps.

O-cresol 浓度在线测量基于近红外光谱通过部分最小平方回归

Unlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the ...

工程学

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits.
Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.

A headspace solid-phase microextraction-gas-chromatography platform is described here for fast, reliable, and semi-automated volatile identification and quantification in ripe blackcurrant fruits. This technique can be used to increase knowledge about fruit aroma and to select cultivars with enhanced flavor for the purpose of breeding.

使用顶空固相微萃取与气相色谱-质谱联用分析黑醋栗果实中的挥发性化合物

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars ...

生物化学

Pure Shift Nuclear Magnetic Resonance: a New Tool for Plant Metabolomics

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not only provides quantitative data but also permits structural identification of the metabolites present in complex mixtures.
Metabolic profiling by 1H NMR has proven useful in the study of various types of plant scenarios, which include the evaluation of crop&#160;conditions, harvest and post- harvest treatments, metabolic phenotyping, metabolic pathways, gene regulation, identification of biomarkers, chemotaxonomy, quality control, denomination of origin, among others. However, signal overlapping of the large number of resonances with expanded J-coupling multiplicities complicates the spectra analysis and its interpretation, and represents a limitation for classical 1H NMR profiling.
In the last decade, novel NMR broadband homonuclear decoupling techniques through which multiplet signals collapse into single resonance lines - commonly called Pure Shift methods - have been developed to overcome the spectra resolution problem inherent to 1H NMR classical spectra.
Here a step-by-step protocol of the plant extract preparation and the procedure to record optimal Pure Shift PSYCHE and SAPPHIRE-PSYCHE spectra in three different plant matrices - Vanilla plant&#160;leaves, potato tubers (S. tuberosum), and Cape gooseberries (P. peruviana) - is presented. The effect of the gain in resolution in metabolic identification, correlation analysis and multivariate analyses, as compared against classical spectra, is discussed.

<meta charset="utf8"></head><body>Pure Shift 核磁共振:植物代谢组学的新工具

This paper presents the use of PSYCHE and SAPPHIRE-PSYCHE in the metabolic profiling of plants and includes detailed procedures for sample preparation and optimal Pure Shift NMR spectra recording. Examples through which the gain in resolution achieved by homonuclear decoupling allows a more comprehensive understanding of the system are discussed.

Pure Shift 核磁共振:植物代谢组学的新工具

Nuclear Magnetic Resonance (NMR) is one of the most powerful tools used in metabolomics. It stands as a highly accurate and reproducible method that not ...

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

使用快速检测策略早期检测蓝藻花和相关氰毒素

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Phenolic acids are a class of organic compounds that bear both a phenolic group, and a carboxylic group. They are found in grains and concentrate in the bran of cereals or seed coat of legumes. They possess antioxidant properties that have generated much research interest in recent years, about their potential antioxidant protective health functions. This work presents a generalized method for the extraction of free soluble phenolic acids from whole grains and analysis of their antioxidant capacity. Five whole grain samples comprising two cereals (wheat and yellow corn) and three legumes (cowpea bean, kidney bean, and soybean), were used. The grains were milled into flour and their free soluble phenolic acids extracted using aqueous methanol. The compounds were then identified using a high-pressure liquid chromatograph (HPLC). The Folin-Ciocalteu method was used to determine their total phenolic content while their antioxidant capacities were determined using the DPPH radical scavenging capacity, Trolox equivalent antioxidant capacity (TEAC) and oxygen radical absorbance capacity (ORAC) assays. The phenolic acids identified included vanillic, caffeic, p-coumaric and ferulic acids. Vanillic acid was identified only in cowpea while caffeic acid was identified only in kidney bean. p-Coumaric acid was identified in yellow corn, cowpea, and soybean, while ferulic acid was identified in all the samples. Ferulic acid was the predominant phenolic acid identified. The total concentration of phenolic acids in the samples decreased in the following order: soybean &gt; cowpea bean &gt; yellow corn = kidney bean &gt; wheat. The total antioxidant capacity (sum of values of DPPH, TEAC and ORAC assays) decreased as follows: soybean &gt; kidney bean &gt; yellow corn = cowpea bean &gt; wheat. This study concluded that HPLC analysis as well as DPPH, TEAC, and ORAC assays provide useful information about the phenolic acid composition and antioxidant properties of whole grains.

Phenolic acids are important phytochemicals that are present in whole grains. They possess bioactive properties such as antioxidant protective functions. This work aimed at reporting on a generalized method for the HPLC identification, total phenolic content estimation, and determination of the antioxidant capacity of phenolic acids in cereals and legumes.

一种测定谷物和豆类游离可溶性酚酸组成和抗氧化能力的广义方法

Phenolic acids are a class of organic compounds that bear both a phenolic group, and a carboxylic group. They are found in grains and concentrate in the ...

Herbivore-induced Blueberry Volatiles and Intra-plant Signaling

Herbivore-induced plant volatiles (HIPVs) are commonly emitted from plants after herbivore attack1,2. These HIPVs are mainly regulated by the defensive plant hormone jasmonic acid (JA) and its volatile derivative methyl jasmonate (MeJA)3,4,5. Over the past 3 decades researchers have documented that HIPVs can repel or attract herbivores, attract the natural enemies of herbivores, and in some cases they can induce or prime plant defenses prior to herbivore attack. In a recent paper6, I reported that feeding by gypsy moth caterpillars, exogenous MeJA application, and mechanical damage induce the emissions of volatiles from blueberry plants, albeit differently. In addition, blueberry branches respond to HIPVs emitted from neighboring branches of the same plant by increasing the levels of JA and resistance to herbivores (i.e., direct plant defenses), and by priming volatile emissions (i.e., indirect plant defenses). Similar findings have been reported recently for sagebrush7, poplar8, and lima beans9..
Here, I describe a push-pull method for collecting blueberry volatiles induced by herbivore (gypsy moth) feeding, exogenous MeJA application, and mechanical damage. The volatile collection unit consists of a 4 L volatile collection chamber, a 2-piece guillotine, an air delivery system that purifies incoming air, and a vacuum system connected to a trap filled with Super-Q adsorbent to collect volatiles5,6,10. Volatiles collected in Super-Q traps are eluted with dichloromethane and then separated and quantified using Gas Chromatography (GC). This volatile collection method was used n my study6 to investigate the volatile response of undamaged branches to exposure to volatiles from herbivore-damaged branches within blueberry plants. These methods are described here. Briefly, undamaged blueberry branches are exposed to HIPVs from neighboring branches within the same plant. Using the same techniques described above, volatiles emitted from branches after exposure to HIPVs are collected and analyzed.

A push-pull method for collecting plant volatiles is described. The method allows for a comparison of volatiles induced by herbivore feeding, exogenous methyl jasmonate, and mechanical damage. This technique is also used to investigate the volatile response of undamaged branches to exposure to volatiles from herbivore-damaged branches within blueberry plants.

蓝莓虫害诱导挥发物和植物内信令

Herbivore-induced plant volatiles (HIPVs) are commonly emitted from plants after herbivore attack1,2.  These HIPVs are mainly regulated by the defensive ...

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

摘要

探索更多视频

此视频中的章节

风土通过概念葡萄果实代谢组学和转录组解释的

RGB 和光谱根成像植物表型和生理研究: 实验装置和成像协议

PTR-ToF-MS结合自动采样系统和食品研究定制数据分析:生物过程监测，筛选和鼻空间分析

O-cresol 浓度在线测量基于近红外光谱通过部分最小平方回归

使用顶空固相微萃取与气相色谱-质谱联用分析黑醋栗果实中的挥发性化合物

Pure Shift 核磁共振:植物代谢组学的新工具

Pure Shift 核磁共振:植物代谢组学的新工具

使用快速检测策略早期检测蓝藻花和相关氰毒素

一种测定谷物和豆类游离可溶性酚酸组成和抗氧化能力的广义方法

蓝莓虫害诱导挥发物和植物内信令