Open AccessStudy on starch content detection and visualization of potato based on hyperspectral imaging
Author(s) -
Wang Fuxiang,
Wang Chunguang,
Song Shiyong,
Xie Shengshi,
Kang Feilong
Publication year - 2021
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.2415
Subject(s) - hyperspectral imaging , mathematics , partial least squares regression , calibration , sampling (signal processing) , starch , potato starch , artificial intelligence , computer science , detector , statistics , chemistry , food science , telecommunications
Abstract Starch is an important quality index in potato, which contributes greatly to the taste and nutritional quality of potato. At present, the determination of starch depends on chemical analysis, which is time consuming and laborious. Thus, rapid and accurate detection of the starch content of potatoes is important. This study combined hyperspectral imaging with chemometrics to predict potato starch content. Two varieties of Kexin No.1 and Holland No.15 potatoes were used as experimental samples. Hyperspectral data were collected from three sampling sites (the top, umbilicus, and middle regions). Standard normal variate (SNV) was used for spectral preprocessing, and three different methods of competitive adaptive reweighted sampling (CARS), iterative variable subset optimization (IVSO), and the variable iterative space shrinkage approach (VISSA) were used for characteristic wavelength selection. Linear partial least‐squares regression (PLSR) and nonlinear support vector regression (SVR) models were then established. The results indicated that the sampling site has a considerable impact on the accuracy of the prediction model, and the umbilicus region with CARS‐SVR model gave best performance with correlation coefficients in calibration (Rc) of 0.9415, in prediction (Rp) of 0.9346, root mean square errors in calibration (RMSEC) of 15.9 g/kg, in prediction (RMSEP) of 17.4 g/kg, and residual predictive deviation (RPD) of 2.69. The starch content in potatoes was visualized using the best model in combination with pseudo‐color technology. Our research provides a method for the rapid and nondestructive determination of starch content in potatoes, providing a good foundation for potato quality monitoring and grading.