Related Experiment Video
Updated: Jun 9, 2025

In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions
Published on: February 21, 2017
Identifying the Vertical Stratification of Sediment Samples by Visible and Near-Infrared Spectroscopy
Pingping Fan1,2, Zongchao Jia3, Huimin Qiu1
1Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, China.
Abstract:
Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0-50 cm (the upper layer), 50-100 cm (the middle layer), and 100-160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.
More Related Videos
09:41Isolation of Quartz Grains for Optically Stimulated Luminescence OSL Dating of Quaternary Sediments for Paleoenvironmental Research
Published on: August 2, 2021
08:57Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions
Published on: January 10, 2019
Related Concept Videos
Ultraviolet and Visible (UV–Vis) Spectroscopy: Overview
UV–Vis Spectrometers
Infrared (IR) Spectroscopy: Overview
Different compounds display unique properties due to their...
Spectrophotometry: Introduction
The essential components of a spectrophotometer include a source of electromagnetic radiation, a slot for placing a material to be analyzed, and a...
IR Spectrometers
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview
The ATR process begins by directing a beam...