Fresh‐Cut Salad Vegetable Texture and Its Characterization Using Instrumental and Sensory Evaluation—A Review

Along with flavor, texture is one of the most important attributes of fresh-cut produce, which plays a critical role in consumer acceptance, influencing perceived freshness, quality, and overall eating experience. Salads, specifically green salads, usually contain leafy greens and other fresh-cut fruit vegetables such as cucumbers and tomatoes. Each of these components has its own unique characteristics, together giving the salad a wide range of textures and consumer experiences. Texture can be measured by both instrumental analysis and sensory evaluation techniques. Instrumental analysis consists of destructive and non-destructive methods, whereas sensory evaluation consists of descriptive and consumer studies. However, a comprehensive understanding and correlation of instrumental analysis and sensory evaluation of texture in salad-related fresh-cut fruits and vegetables is lacking in the literature. This review aims to address this gap by providing an in-depth understanding of the techniques used to quantify texture-related parameters and establishing relationships between the two techniques in fresh-cut salad products. The two different modes of analysis, along with the vastly different texture-related characteristics of fresh-cut salad produce, make it challenging to correlate instrumental analysis with sensory evaluation results. Furthermore, the integration of advanced technologies, including hyperspectral imaging and artificial intelligence-driven texture analysis, is emerging as a promising approach to enhance texture characterization and quality control. In addition to providing a comprehensive approach to enhance quality assessment methods, this review highlights the importance of predictability models powered by artificial intelligence and machine learning, enabling correlating instrumental analysis and sensory evaluation.

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Publication Details

Journal
Journal of Texture Studies
Published
2026-02-01
DOI
https://doi.org/10.1111/jtxs.70072
Primary Topic
Postharvest Quality and Shelf Life Management
Type
article
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article

Fresh‐Cut Salad Vegetable Texture and Its Characterization Using Instrumental and Sensory Evaluation—A Review

Yiqun Weng, Xiaofen Du, Cassidy Duan
Journal of Texture Studies
Postharvest Quality and Shelf Life Management
article

Fresh‐Cut Salad Vegetable Texture and Its Characterization Using Instrumental and Sensory Evaluation—A Review

Yiqun Weng, Xiaofen Du, Cassidy Duan
article en

Abstract

Along with flavor, texture is one of the most important attributes of fresh-cut produce, which plays a critical role in consumer acceptance, influencing perceived freshness, quality, and overall eating experience. Salads, specifically green salads, usually contain leafy greens and other fresh-cut fruit vegetables such as cucumbers and tomatoes. Each of these components has its own unique characteristics, together giving the salad a wide range of textures and consumer experiences. Texture can be measured by both instrumental analysis and sensory evaluation techniques. Instrumental analysis consists of destructive and non-destructive methods, whereas sensory evaluation consists of descriptive and consumer studies. However, a comprehensive understanding and correlation of instrumental analysis and sensory evaluation of texture in salad-related fresh-cut fruits and vegetables is lacking in the literature. This review aims to address this gap by providing an in-depth understanding of the techniques used to quantify texture-related parameters and establishing relationships between the two techniques in fresh-cut salad products. The two different modes of analysis, along with the vastly different texture-related characteristics of fresh-cut salad produce, make it challenging to correlate instrumental analysis with sensory evaluation results. Furthermore, the integration of advanced technologies, including hyperspectral imaging and artificial intelligence-driven texture analysis, is emerging as a promising approach to enhance texture characterization and quality control. In addition to providing a comprehensive approach to enhance quality assessment methods, this review highlights the importance of predictability models powered by artificial intelligence and machine learning, enabling correlating instrumental analysis and sensory evaluation.

Journal of Texture StudiesVol. 57(1)
Texas Woman's University (US), U.S. Vegetable Laboratory (US)
Openalex Percentile: Top 50%
Postharvest Quality and Shelf Life Management
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