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

Journal: Food chemistry
PMID: 39608109

Abstract

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.

Authors

  • Peipei Gao
    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.
  • Wenlong Li
    Institute of Clinical Pharmacology, Qilu Hospital, Shandong University, Jinan, China.
  • Sulafa B H Hashim
    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.
  • Jing Liang
    College of Management Science, Chengdu University of Technology, Chengdu, China.
  • Jialong Xu
    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.
  • Xiaowei Huang
    School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, PR China.
  • Xiaobo Zou
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
  • Jiyong Shi
    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.

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