Investigation of physicochemical properties, digestibility, and 3D printability of three unripe mango pulp starches

This study integrated multiscale structural characterization to elucidate the structure and functionality of starches from three mango cultivars. All three mango starches were of C-type crystalline features. Jinhuang mango starch (JHMS) showed the highest amylose content (25.25%) with a distinct short-range ordered structure and amorphous regions, resulting in superior water-binding capacity and moderate relative crystallinity (RC), onset gelatinization temperature (T o ) and resistant starch (RS) content. Tainong mango starch (TNMS), despite having the lowest RC, featured a more branched amylopectin structure, contributing to the highest T o (65.72 °C) and an elevated RS content (28.65%). Ivory mango starch (IVMS) had the highest RC (30.9%), but lower amylose content and longer chain length (Rh AM = 19.13 nm), demonstrating the lowest T o (63.11 °C) along with excellent rheological stability and gel strength. Notably, JHMS exhibited optimal 3D printability due to its balanced viscosity and structural integrity. These findings highlight the potential of underutilized mango starches for tailored food and 3D printing applications. • Three unripe mango pulp starches displayed C-type crystallinity and varying structures. • Jinhuang Mango starch had the highest amylose content with distinct short-range order. • Tainong mango starch showed the highest gelatinization temperature and resistant starch. • Ivory mango starch displayed the highest relative crystallinity and gel strength. • Jinhuang mango starch demonstrated optimal 3D printing performance.

Authors

Institutions

Publication Details

Journal
LWT
Published
2026-03-11
DOI
https://doi.org/10.1016/j.lwt.2026.119257
Primary Topic
Food composition and properties
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigation of physicochemical properties, digestibility, and 3D printability of three unripe mango pulp starches

Yue Wang, Sai Li, Yilin Lin, Tao Yang et al.
LWT
Food composition and properties
article

Investigation of physicochemical properties, digestibility, and 3D printability of three unripe mango pulp starches

Yue Wang, Sai Li, Yilin Lin, Tao Yang, Rui He, Kun Pan, Shanshan Li
article en

Abstract

This study integrated multiscale structural characterization to elucidate the structure and functionality of starches from three mango cultivars. All three mango starches were of C-type crystalline features. Jinhuang mango starch (JHMS) showed the highest amylose content (25.25%) with a distinct short-range ordered structure and amorphous regions, resulting in superior water-binding capacity and moderate relative crystallinity (RC), onset gelatinization temperature (T o ) and resistant starch (RS) content. Tainong mango starch (TNMS), despite having the lowest RC, featured a more branched amylopectin structure, contributing to the highest T o (65.72 °C) and an elevated RS content (28.65%). Ivory mango starch (IVMS) had the highest RC (30.9%), but lower amylose content and longer chain length (Rh AM = 19.13 nm), demonstrating the lowest T o (63.11 °C) along with excellent rheological stability and gel strength. Notably, JHMS exhibited optimal 3D printability due to its balanced viscosity and structural integrity. These findings highlight the potential of underutilized mango starches for tailored food and 3D printing applications. • Three unripe mango pulp starches displayed C-type crystallinity and varying structures. • Jinhuang Mango starch had the highest amylose content with distinct short-range order. • Tainong mango starch showed the highest gelatinization temperature and resistant starch. • Ivory mango starch displayed the highest relative crystallinity and gel strength. • Jinhuang mango starch demonstrated optimal 3D printing performance.

LWTVol. 245
McGill University (CA), Hainan Medical University (CN), South China University of Technology (CN)
Openalex Percentile: Top 24%
Food composition and properties
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.