ArticleRSC advances2021
A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging.
Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Hyperspectral Imaging for Early Detection and Severity Grading of Potato Bacterial Wilt.Plants (Basel, Switzerland) · 2026Article
- Assessing food by-products macrocomposition by FTIR microspectroscopy.Analytical and bioanalytical chemistry · 2025Article
- Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude.Frontiers in nutrition · 2022Article
- Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality.Foods (Basel, Switzerland) · 2021Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fresh-cut potatoes are popular with consumers because of their healthiness, hygiene, and convenience. Currently, starch content is mainly detected using chemical methods, which are time-consuming and laborious. Moreover, these methods may cause some side effects in the human body. Therefore, suitable methods are required for the rapid and accurate detection of starch content. In this study, Zihuabai and Atlantic potatoes were used as experimental samples. The potatoes were sliced with stainless-steel blades, and images of these potatoes were obtained through hyperspectral imaging. The images were preprocessed using different methods. Competitive adaptive reweighed sampling (CARS) and the successive projection algorithm (SPA) were used to extract characteristic wavelengths. A partial least squares regression (PLSR) model was constructed to predict the starch content from the preprocessed full spectrum and the spectrum under the characteristic wavelength. The results indicate that the full spectrum model constructed through standard normal variable transformation (SNV) preprocessing had the best performance, with a correlation coefficient in the calibration set (
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Registered trials
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