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Fresh-Cut Salad Vegetable Texture and Its Characterization Using Instrumental and Sensory Evaluation-A Review.

Cassidy Duan¹, Yiqun Weng², Xiaofen Du¹

¹Department of Nutrition and Food Sciences, Texas Woman's University, Denton, Texas, USA.

Journal of Texture Studies

|February 19, 2026

Summary

This summary is machine-generated.

Texture is key for fresh-cut produce acceptance. This review explores instrumental and sensory methods for salad texture analysis, highlighting AI

Keywords:

firmness fresh‐cut produce green salads hardness texture characterization texture profile analysis texture–sensory correlation

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Area of Science:

Food Science
Sensory Science
Agricultural Engineering

Background:

Texture significantly impacts consumer acceptance and perceived quality of fresh-cut produce, especially in salads.
Salads comprise diverse components like leafy greens, cucumbers, and tomatoes, each contributing unique textural properties.
Current literature lacks a comprehensive correlation between instrumental and sensory texture evaluation methods for salad ingredients.

Purpose of the Study:

To bridge the gap in understanding texture analysis for fresh-cut salad products.
To provide an in-depth review of techniques for quantifying texture parameters in salad ingredients.
To establish relationships between instrumental and sensory evaluation of texture in fresh-cut salads.

Main Methods:

Review of instrumental analysis techniques (destructive and non-destructive) for texture measurement.
Review of sensory evaluation methods (descriptive and consumer studies) for texture assessment.
Exploration of advanced technologies like hyperspectral imaging and AI-driven texture analysis.

Main Results:

Significant challenges exist in correlating instrumental and sensory texture data due to diverse produce characteristics and analysis modes.
Advanced technologies offer promising avenues for enhanced texture characterization and quality control in fresh-cut produce.
Predictive models utilizing artificial intelligence (AI) and machine learning (ML) are crucial for correlating instrumental and sensory data.

Conclusions:

A unified approach is needed to effectively correlate instrumental and sensory texture data for fresh-cut salad products.
Integration of AI and ML is vital for developing robust predictability models for texture assessment.
Enhanced quality assessment methods for fresh-cut produce can be achieved through advanced analytical techniques and predictive modeling.