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
Summary
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.
Abstract
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.
Introduction
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。
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。 请点击此处查看该图的放大版本。
Protocol
Representative Results
Discussion
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…Divulgations
The authors have nothing to disclose.
Acknowledgements
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.
Materials
| FT-NIR spectrophotometer | Bruker Optics GmbH | MPA | |
| High-Performance Liquid Chromatography | Shimadzu Corporation | 228-45041-91, 228-45000-31, 228-45018-31, | For sugar analysis |
| 223-04500-31, 228-45010-31, 228-45095-31 | Refractive Index Detector | ||
| High-Performance Liquid Chromatography | Shimadzu Corporation | 228-45041-91, 228-45003-31, 228-45000-31, | For organic acid analysis |
| 228-45018-31, 228-45010-31, 223-04500-31 | Ultraviolet-Visible Detector | ||
| High-Performance Liquid Chromatography | Shimadzu Corporation | 228-45041-91, 228-45018-31, 228-45000-31, | For anthocyanin analysis |
| 228-45012-31, 228-45119-31, 228-45005-31, | Photodiode Array Detector | ||
| 228-45009-31 | |||
| pH meter | Mettler-Toledo | 30019028 | S220, Automatic temperature compensation |
| Ultra-pure water treatment equipment | ORGANO Corporation | ORG-ULXXXM1; PRA-0015-0V0 | PURELAB ultra; PURELITE |
| Biomedical Freezers | SANYO | 2-6780-01 | MDF-U338 |
| Ultra-Low Temperature Freezer | Panasonic healthcare Co.,Ltd. | KM-DU73Y1 | -80°C |
| Vacuum lyophilizer | IWAKI GLASS Co.,Ltd | 119770 | DRC-3L;FRD-82M |
| Homoginizer | Microtec Co., Ltd. | Physcotron | |
| Ultracentrifuge | Hitachi Koki Co.,Ltd | S204567 | CF15RXII |
| Mini-centrifuge | LMS CO.,LTD. | KN3136572 | MCF-2360 |
| Centrifuge | Kokusan Co.,Ltd | 2-5534-01 | H-103N |
| Filter Paper | Advantec | 1521070 | 5B, Eqivalent to Whatman 40 |
| Sep-Pak C18 column | Waters Corporation Milford | WAT020515 | |
| Sep-Pak CM column | Waters Corporation Milford | WAT020550 | |
| Sep-Pak QMA column | Waters Corporation Milford | WAT020545 | |
| Centrifugal Filter Unit | Merck Millipore Corporation | R2SA18503 | PVDF, 0.45 μm |
| Microtube | As One Corporation | 1-1600-02 | PP, 2 mL |
| Syringe Filter | GE Healthcare CO.,LTD. | 6788-1304 | PP, 0.45 μm |
| Sucrose | Wako Pure Chemical Industries,Ltd | 194-00011 | Reagent-grade |
| Glucose | Wako Pure Chemical Industries,Ltd | 049-31165 | Reagent-grade |
| Fructose | Wako Pure Chemical Industries,Ltd | 123-02762 | Reagent-grade |
| Citric acid | Wako Pure Chemical Industries,Ltd | 036-05522 | Reagent-grade |
| Malic acid | Wako Pure Chemical Industries,Ltd | 355-17971 | Reagent-grade |
| Succinic acid | Wako Pure Chemical Industries,Ltd | 190-04332 | Reagent-grade |
| Quinic acid | Alfa Aesar, A Johnson Matthey Company | 10176328 | Reagent-grade |
| Phosphoric acid | Wako Pure Chemical Industries,Ltd | 162-20492 | HPLC-grade |
| Trifluoroacetic acid | Wako Pure Chemical Industries,Ltd | 208-02746 | Reagent-grade |
| Methanol | Wako Pure Chemical Industries,Ltd | 131-01826 | Reagent-grade |
| Acetonitrile | Wako Pure Chemical Industries,Ltd | 015-08633 | HPLC-grade |
| Grade cyanidin-3-O-glucoside chloride | Wako Pure Chemical Industries,Ltd | 306-37661 | HPLC-grade |
| Software for analyses | Bruker Optics GmbH | OPUS ver. 6.5 | |
| Softoware for preprocessing | Microsoft | Excel powered by Visual Basic for Applications | |
| Software for construction of models | Freemat 4.0 | http://freemat.sourceforge.net/ |
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