Zhao Yan-Ru, Yu Ke-Qiang, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
J Anal Methods Chem. 2015;2015:343782. doi: 10.1155/2015/343782. Epub 2015 Sep 14.
Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380-1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.
将化学计量学方法与可见近红外（Vis/NIR）区域（380 - 1030 nm）的高光谱成像技术相结合，用于评估桑椹中的总可溶性固形物（TSS）。利用高光谱反射成像系统（512波段）采集了310个桑椹的高光谱图像，并使用糖度计测量了它们相应的TSS含量。采用随机蛙跳（RF）法从全波长中选择重要波长。基于全波长和所选重要波长，通过偏最小二乘回归（PLSR）和最小二乘支持向量机（LS - SVM）模型预测桑椹果实中的TSS值。采用具有23个重要波长的最优PLSR模型来可视化测试样品中TSS的空间分布，并通过TSS空间分布揭示桑椹中的TSS浓度。结果表明，高光谱成像在确定桑椹果实中TSS含量的空间分布方面具有潜力，为检测果实内部品质提供了参考。
