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Related Concept Videos

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sample Preparation for Analysis: Overview01:21

Sample Preparation for Analysis: Overview

Sample preparation is an essential step in the analytical process. It involves preparing a sample so that it can be analyzed accurately. The goal is to extract the analyte, the substance you want to measure, from the sample while removing any components that may interfere with the analysis. Sample preparation techniques vary depending on the physical state of the sample.
Bulk or large solid samples are typically reduced in size using grinding, crushing, or milling techniques to increase the...

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Related Experiment Video

Updated: Jun 24, 2026

A Microfluidic Platform for Precision Small-volume Sample Processing and Its Use to Size Separate Biological Particles with an Acoustic Microdevice
11:32

A Microfluidic Platform for Precision Small-volume Sample Processing and Its Use to Size Separate Biological Particles with an Acoustic Microdevice

Published on: November 23, 2015

Automated sample pretreatment technologies: from surface-based liquid microjunction sampling to flow-based platforms.

Xinyu Jiang1, Qiang Ma2, Xuesong Feng3

  • 1Chinese Academy of Quality and Inspection & Testing, Beijing, 100123, China; School of Pharmacy, China Medical University, Shenyang, 110122, China.

Talanta
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Automated sample pretreatment enhances analytical workflows, moving from manual methods to integrated strategies. This review covers surface-based liquid microjunction sampling and flow-based platforms for efficient analysis.

Keywords:
Lab-in-syringeLiquid extraction surface analysisLiquid microjunction surface sampling probeMasSpec PenOnline solid-phase extraction

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Area of Science:

  • Analytical Chemistry
  • Biomedical Engineering
  • Process Automation

Background:

  • Sample pretreatment is critical for analytical workflows, especially in complex matrices.
  • Automation is increasingly adopted to improve efficiency, reproducibility, and sustainability in sample preparation.
  • Traditional manual methods are being replaced by integrated and standardized automated strategies.

Purpose of the Study:

  • To review two key automation paradigms in sample pretreatment: surface-based liquid microjunction sampling and flow-based automated platforms.
  • To analyze the methodological characteristics, design considerations, and limitations of these automation strategies.
  • To discuss current trends and future directions in automated analytical workflows.

Main Methods:

  • Examination of surface-based liquid microjunction techniques (e.g., LE SA, LM SP, MasSpec Pen) for localized, minimally invasive extraction.
  • Analysis of flow-based platforms (e.g., lab-in-syringe, online SPE) emphasizing controlled fluid handling and process integration.
  • Review of platform architecture's influence on analytical performance and application scope.

Main Results:

  • Liquid microjunction techniques enable in situ analysis of biological and clinical samples but face challenges in spatial resolution and quantitative consistency.
  • Flow-based platforms are crucial for high-throughput and trace-level analysis in various fields like biomedicine and environmental monitoring.
  • Automation significantly impacts efficiency, reproducibility, and the scope of analytical applications.

Conclusions:

  • Automated sample pretreatment, through both liquid microjunction and flow-based systems, offers significant advantages over manual methods.
  • Platform design critically influences analytical outcomes, necessitating careful consideration for specific applications.
  • Future trends point towards greater integration, intelligent control, and standardization in automated analytical workflows.