Smartphone-Assisted Phenol–Sulfuric Acid Assay with ImageJ Analysis for Total Carbohydrate Quantification
- 1Department of Physical and Life Sciences, School of Science, Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Abstract
The phenol–sulfuric acid (PSA) assay is widely recognized for its versatility and simplicity in the detection and quantification of carbohydrates. However, as well as the appropriate reagents, this method requires a UV–visible spectrophotometer, the cost of which may be prohibitive for some research groups. This study aims to revisit the PSA assay and investigate a novel smartphone-assisted method as a potential alternative to using a spectrophotometer. To the best of our knowledge this is the first time the PSA assay has been used in this way. A range of monosaccharides—glucose, fructose, galactose, xylose, arabinose, mannose, fucose, rhamnose, and galacturonic acid—were tested to establish calibration curves and evaluate the PSA assay’s quantitative parameters, utilizing both the traditional spectrophotometer-based method and the adapted smartphone-assisted assay, where ImageJ was used to estimate the change in “colour” of the samples. Calibration curves generated from the traditional method demonstrated excellent linearity, with adjusted coefficients of determination (Adj. R2) between 0.992 and 0.998; the slopes (m), intercepts (c) and the standard deviation of the residuals of these curves (sx/y) were used to estimate the limits of detection (LOD) and limits of quantification (LOQ) at 95% confidence. For the traditional PSA assay, the LOD values ranged from 5.5 to 12.3 ppm and the LOQ values from 16.8 to 37.5 ppm. Differences in the LOD/LOQ between different monosaccharides were attributed to variations in chemical reactivity, molecular structure, and chromophore absorbance at 490 nm. The smartphone-assisted PSA assay with ImageJ also demonstrated excellent linearity, with adjusted coefficients of determination (Adj. R2) between 0.991 and 0.997. The LOD values ranged from 7.9 to 13.1 ppm and the LOQ values from 24.1 to 39.9 ppm, providing an alternative for use in resource-limited environments. To demonstrate the potential usefulness of this assay, two case studies were undertaken: (i) to estimate the total carbohydrate content of pectin-like polymers from okra and potato, using both the traditional and smartphone-assisted PSA assay with ImageJ; and (ii) to generate a chromatogram in the preparative size exclusion chromatography (SEC) of a commercial citrus pectin sample. In the first case study, the total carbohydrate contents across all samples were similar using both methods and in the second case study this allowed the analysis of 32 samples (in triplicate) in one image. Future work should focus on fully optimising both image capture and the analysis steps of the new protocol and extending the method’s applicability to complex biological and environmental matrices. Overall, this study offers an alternative to the traditional PSA assay, reinforcing its role as a fundamental tool in modern carbohydrate quantification.
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BibTeX
@article{iqbal2026smartphoneassisted,
title = {{Smartphone-Assisted Phenol–Sulfuric Acid Assay with ImageJ Analysis for Total Carbohydrate Quantification}},
author = {Zaiba Iqbal and Gordon A. Morris},
journal = {Molecules},
year = {2026},
month = oct,
doi = {10.3390/molecules31213794},
url = {https://www.mdpi.com/1420-3049/31/21/3794},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Smartphone-Assisted Phenol–Sulfuric Acid Assay with ImageJ Analysis for Total Carbohydrate Quantification
AU - Zaiba Iqbal
AU - Gordon A. Morris
PY - 2026
DA - 2026/10/22
JO - Molecules
DO - 10.3390/molecules31213794
UR - https://www.mdpi.com/1420-3049/31/21/3794
AB - The phenol–sulfuric acid (PSA) assay is widely recognized for its versatility and simplicity in the detection and quantification of carbohydrates. However, as well as the appropriate reagents, this method requires a UV–visible spectrophotometer, the cost of which may be prohibitive for some research groups. This study aims to revisit the PSA assay and investigate a novel smartphone-assisted method as a potential alternative to using a spectrophotometer. To the best of our knowledge this is the first time the PSA assay has been used in this way. A range of monosaccharides—glucose, fructose, galactose, xylose, arabinose, mannose, fucose, rhamnose, and galacturonic acid—were tested to establish calibration curves and evaluate the PSA assay’s quantitative parameters, utilizing both the traditional spectrophotometer-based method and the adapted smartphone-assisted assay, where ImageJ was used to estimate the change in “colour” of the samples. Calibration curves generated from the traditional method demonstrated excellent linearity, with adjusted coefficients of determination (Adj. R2) between 0.992 and 0.998; the slopes (m), intercepts (c) and the standard deviation of the residuals of these curves (sx/y) were used to estimate the limits of detection (LOD) and limits of quantification (LOQ) at 95% confidence. For the traditional PSA assay, the LOD values ranged from 5.5 to 12.3 ppm and the LOQ values from 16.8 to 37.5 ppm. Differences in the LOD/LOQ between different monosaccharides were attributed to variations in chemical reactivity, molecular structure, and chromophore absorbance at 490 nm. The smartphone-assisted PSA assay with ImageJ also demonstrated excellent linearity, with adjusted coefficients of determination (Adj. R2) between 0.991 and 0.997. The LOD values ranged from 7.9 to 13.1 ppm and the LOQ values from 24.1 to 39.9 ppm, providing an alternative for use in resource-limited environments. To demonstrate the potential usefulness of this assay, two case studies were undertaken: (i) to estimate the total carbohydrate content of pectin-like polymers from okra and potato, using both the traditional and smartphone-assisted PSA assay with ImageJ; and (ii) to generate a chromatogram in the preparative size exclusion chromatography (SEC) of a commercial citrus pectin sample. In the first case study, the total carbohydrate contents across all samples were similar using both methods and in the second case study this allowed the analysis of 32 samples (in triplicate) in one image. Future work should focus on fully optimising both image capture and the analysis steps of the new protocol and extending the method’s applicability to complex biological and environmental matrices. Overall, this study offers an alternative to the traditional PSA assay, reinforcing its role as a fundamental tool in modern carbohydrate quantification.
SN - 1420-3049
ER -