Distributional uniformity quantification in heterogeneous prepared dishes combined the hyperspectral imaging technology with Moran's I: A case study of pizza.

Autor: Gao P; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Li W; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Hashim SBH; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Liang J; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Xu J; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Huang X; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Zou X; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China., Shi J; Agricultural Product Processing and Storage Lab, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; International Joint Research Laboratory of Intelligent Agriculture and Agri-products Processing (Jiangsu University), Jiangsu Education Department, Zhenjiang 212013, China; China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China. Electronic address: shi_jiyong@ujs.edu.cn.
Jazyk: angličtina
Zdroj: Food chemistry [Food Chem] 2025 Feb 28; Vol. 466, pp. 141511. Date of Electronic Publication: 2024 Oct 05.
DOI: 10.1016/j.foodchem.2024.141511
Abstrakt: Quality detection is critical in the development of prepared dishes, with distributional uniformity playing a significant role. This study used hyperspectral imaging (HSI) and Moran's I to quantify distributional uniformity, employing pizza as case. Pizza ingredients' spectra were collected, pre-processed with Detrended Fluctuation Analysis (DFA), Savitzky-Golay (SG) and Standard Normal Variate (SNV), and down-scaled with Principal Component Analysis (PCA). Subsequently, the classifiers Fine Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were utilized, where KNN based on the DFA-processed data had the greatest accuracy of 99.2 %. This best-fit model was used to create visualization maps. At last, image analysis methods containing regional statistics, Grey Level Co-occurrence Matrix (GLCM) and Moran's I were used to measure distributional uniformity. Moran's I demonstrated great distinctiveness and accuracy, making it the best tool. Therefore, HSI and Moran's I combination proved feasible to indicate distributional uniformity, ensuring the high quality of prepared dishes.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Ltd.)
Databáze: MEDLINE
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