The Nuclear Techniques and the Selection of Model Parameters in Big Data

Wang, Chunhong

doi:10.14257/ijdta.2014.7.6.14

Cited by 1 publication

(1 citation statement)

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“…Kernel function can map sample in original space to high-dimensional feature space to solve the linear inseparable problem [ 29 ]. There are several typical examples of kernel function such as linear kernel, polynomial kernel, RBF, and sigmoid kernel.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries

Zhao

2015

Journal of Analytical Methods in Chemistry

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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.

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Section: Methodsmentioning

confidence: 99%