Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network. (October 2022)

• Record Type: Journal Article
• Authors: Zhang, Yaoxin; Zheng, Minchong; Zhu, Rongguang; Ma, Rong
• Is Part Of: Meat science. Volume 192(2022)
• Publication Date: 2022-10
• Subjects: Hyperspectral -- Adulterated mutton -- Recurrence plot -- Convolutional neural network -- Qualitative discrimination -- Quantitative prediction; Meat -- Periodicals; Meat industry and trade -- Periodicals; Viande -- Périodiques; Viande -- Industrie -- Périodiques; Meat; Meat industry and trade; Periodicals; 641.36
• DOI: 10.1016/j.meatsci.2022.108900
• Languages: English
• ISSNs: 0309-1740

Abstract

Abstract: This paper presented a method to detect adulterated mutton using recurrence plot transformed by spectrum combined with convolutional neural network (RP-CNN). For this, 100 adulterated samples of mutton mixed with different proportions (0.5–1–2-5-10% ( w /w)) of pork and 20 pure mutton samples were prepared. The results of the classification model of adulterated mutton and the quantitative prediction model of pork content established by this method were comparable for fresh, frozen-thawed and mixed datasets. It shows that the classification accuracies of adulteration mutton on three datasets were 100.00%, 100.00% and 99.95% respectively. Moreover, for the pork content prediction of adulterated mutton, the R 2 on three datasets of fresh, frozen-thawed and mixed samples were 0.9762, 0.9807 and 0.9479, respectively. Therefore, the hyperspectral combined with RP-CNN proposed in this paper shows great potential in the classification of adulterated mutton and the pork content prediction of adulterated mutton. Highlights: A method of two-dimensional spectrogram combined with CNN is proposed to detect adulterated mutton. The recurrence plot transformed by spectrum are used as the inputs of CNN models. The qualitative quantitative CNN model of adulterated mutton at low levels are developed. The CNN qualitative and quantitative models are compared with machine learning algorithm.