A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

• Published in: RSC advances 2021-04, Vol.11 (22), p.13636-13643
• Main Authors: Wang, Fuxiang, Wang, Chunguang, Song, Shiyong
• Format: Article
• Language: English
• Subjects: Adaptive sampling; Algorithms; Automobiles; Chemistry; Correlation coefficients; Error correction; Forecasting; Hygiene; Hyperspectral imaging; Least squares method; Mean square errors; Model testing; Potatoes; Regression models; Root-mean-square errors; Side effects; Spatial distribution; Stainless steels
• ISSN: 2046-2069; 2046-2069
• DOI: 10.1039/d1ra01013a

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 ( R c ) value of 0.9020, a root mean square error of correction (RMSEC) of 2.06, and a residual prediction deviation (RPD) of 2.33. The characteristic wavelength-based multivariate scattering correction (MSC)-CARS-PLSR model exhibited better performance than the PLSR model constructed using the full spectrum, with an R c value of 0.9276, RMSEC of 1.76, correlation coefficient in the prediction set ( R p ) value of 0.9467, root mean square error of prediction of 1.63, and RPD of 2.95. The starch content in fresh-cut potatoes was visualized using the best model in combination with pseudocolor technology. The results indicate that hyperspectral imaging is effective for mapping the spatial distribution of starch content; thus, a solid theoretical basis is obtained for the grading and online monitoring of fresh-cut potato slices. The starch content of fresh-cut potato chips was detected via hyperspectral imaging, and a representation of the visual distribution of the starch content was realized.