Evidence map›Paper›PMID 39994272›Full record

ArticleScientific reports2025

LIGHt-based rapid detection of starch in tobacco leaves by smartphone sensing.

Wenchen Li, Yunfei Sha, Junwei Xiong, Yumei Chen, Yaqiong Zhang, Jianhao Zhang, Lan Xu, Weiying Lu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wenchen LiInstitute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yunfei ShaTechnology Center of Shanghai Tobacco Group Co. Ltd, Shanghai, 200082, China.
Junwei XiongTechnology Center of Shanghai Tobacco Group Co. Ltd, Shanghai, 200082, China.
Yumei ChenInstitute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yaqiong ZhangInstitute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jianhao ZhangInstitute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Lan XuTechnology Center of Shanghai Tobacco Group Co. Ltd, Shanghai, 200082, China. xulan@sh.tobacco.com.cn.
Weiying LuInstitute of Food and Nutraceutical Science, Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China. weiying.lu@sjtu.edu.cn.

Funding

National Key Research and Development Program of China 2023YFF1104304
6 · The paper itself

Abstract

A rapid and efficient detection method to detect the total starch in tobacco leaves using smartphone-based anthrone-sulfuric acid colorimetry is established under the framework of the low-cost, immediate, general-purpose, and high-throughput (LIGHt) smartphone-based colorimetry. The LIGHt smartphone colorimetry resulted average relative error of 5.74%, with a relative standard deviation (RSD) ranging from 2.58 to 4.31%, a detection limit of 1.53 µg/mL, and an average recovery rate of 95.72%. The results demonstrated that the LIGHt smartphone colorimetry perform comparably to the regular visible spectrophotometry in determining starch concentration, validating the feasibility of this novel colorimetric method, which requires no specific instruments. The LIGHt-based method indicated the feasibility and potential for application in the field of rapid quality detection of plant leaves and other food and medicinal-related quality control practices.

Indexed as

ColorimetryNicotianaPlant LeavesSmartphoneStarchLightLimit of DetectionStarchColorimetryLIGHt smartphone detectionStarch concentration

Identifiers

PMID39994272
PMCPMC11850627

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.