Bai Wenming, Yoshimura Norio, Takayanagi Masao, Che Jingai, Horiuchi Naomi, Ogiwara Isao
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology.
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology;
J Vis Exp. 2016 Jun 28(112):53981. doi: 10.3791/53981.
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.
对农产品成分含量进行无损预测，有助于运输和销售质量有保证的产品。在此，利用近红外光谱法对每个蓝莓中的总糖、总有机酸和总花青素含量进行无损预测。该技术有望从所有收获的蓝莓中挑选出美味的蓝莓。采用漫反射模式在蓝莓萼片以外的位置测量其近红外吸收光谱。用高效液相色谱法测定蓝莓的成分含量，以此构建模型，根据观测光谱预测成分含量。采用偏最小二乘回归法构建模型。必须正确选择观测光谱的预处理方法以及用于分析的光谱波长区域。所构建的模型需要进行验证，以确认成分含量的预测具有实际准确性。本文介绍了一种通过近红外光谱法构建和验证蓝莓成分含量无损预测模型的方案。
