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Lettuce storage time is an important factor affecting the lettuce freshness. To realize the non-destructive, rapid and effective discrimination of lettuce storage time, generalized fuzzy K-harmonic means (GFKHM) clustering was proposed by introducing the pth power of Euclidean distance into fuzzy K-harmonic means (FKHM) clustering to replace the square of Euclidean distance in FKHM, and furthermore GFKHM was applied in the discrimination of lettuce storage time. Sixty fresh lettuce samples were prepared as the research object, and the near infrared reflectance (NIR) spectra of lettuces were collected by Antaris Ⅱ near infrared spectrometer with a spectral range of 10 000~4 000 cm-1 for three 12-hour detections. Firstly, the 1 557-dimensional NIR spectra were reduced by principal component analysis (PCA) to decrease redundant information. After the first 20 principal components were selected, PCA translated the 1 557-dimensional NIR spectra into the 20-dimensional data. Secondly, linear discriminant analysis (LDA) was used to extract the discriminant information from the 20-dimensional data to improve the clustering accuracy. With the first two discriminant vectors, LDA translated the 20-dimensional data into the two-dimensional data. Finally, the cluster centers from fuzzy C-means clustering (FCM) were set as the initial cluster centers for FKHM and GFKHM and fuzzy membership values of FKHM and GFKHM were calculated to identify lettuce storage time. The experimental results showed that the discrimination accuracy of GFKHM has achieved 92.5% which was higher than that of FKHM. The cluster centers of GFKHM were much closer to the true cluster centers in comparison with FKHM. Furthermore, the convergence of the GFKHM was significantly faster than FKHM. Near infrared spectroscopy coupled with GFKHM, PCA and LDA could cluster NIR spectra of lettuce quickly and correctly, and this provided a fast and nondestructive method for identifying lettuce storage time.
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Food Microbiol
January 2026
Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, 48824, USA. Electronic address:
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Department of Biotechnology and Food Engineering, Guangdong Technion Israel Institute of Technology bda; Daxue Road, Jinping District, Shantou 515063, Guangdong Province, China; Guangdong Provincial Key Laboratory of Materials and Technologies for Energy Conversion, Guangdong Technion - Israel Insti
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Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, China.
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Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
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Technical University of Denmark, National Food Institute, Henrik Dams Allé, Building 202, DK-2800 Kgs. Lyngby, Denmark. Electronic address:
Nitrite, nitrate, and N-nitrosamines (NAs) have been extensively studied for their potential health risks; however, significant gaps remain in dietary exposure assessments, particularly due to the lack of occurrence data for many food categories including vegetables. Most studies have focused on processed meats, creating a need to explore other food sources to better estimate total dietary exposure. Leafy green vegetables, particularly when processed and stored, may accumulate significant nitrite and NA concentrations due to their naturally high nitrate content, contributing to dietary exposure underestimated in current risk assessments.
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