Expeditious detection of Fusarium graminearum infection in rice by FTNIR using hierarchical cluster analysis. (October 2022)

• Record Type: Journal Article
• Authors: Srivastava, Shubhangi; Mishra, Hari Niwas
• Is Part Of: Food Chemistry Advances. Volume 1(2022)
• Publication Date: 2022-10
• Subjects: Fungi -- Classification -- Histograms -- Heterogeneity -- Spectral; 664
• DOI: 10.1016/j.focha.2022.100140
• Languages: English
• ISSNs: 2772-753X

Abstract

Highlights: FTNIR with ward algorithm was used for rice spoiled by F. graminearum . A hierarchical cluster analysis (HCA) and histograms analysis was applied. The % accuracy of classification for 0–14 days was 100% and 87.58% for 14–35 days. The width of each class for 0-35 days range between 0.048–0.130. FTNIR combined with HCA can help in sorting fungi infected rice in food industry. Abstract: The lurking appositeness of Fourier transform near infrared spectroscopy (FTNIR) as a screening tool with ward's algorithm was implemented for rice samples spoiled by Fusarium graminearum followed by hierarchical cluster analysis (HCA) and histograms with storage days (0–35). The spectral inspection of F. graminearum spoiled rice was done at wave numbers ranging from 12, 000 to 4000 cm −1 and the HCA was executed using vector normalization as a pre-processing method with a Euclidean distance of 0.017 with a wavenumber of 3595–12489 cm −1 . The dissimilarities' between rice spoiled by F. graminearum were computed using Pearson's correlation coefficients, which were further converted to D values. The major absorbance bands were detected at 7220.15, 6629.042, 5968.17, and 4981.861 cm −1 . Further, it was observed that the absorbance units (AU) increased with storage days, the concentration of F. graminearum, and the moisture content (MC) of the rice grains. A total of 10 classes were constituted with a 'D' value of 36-153 for 0-35 storage days. The width of each class varied between 0.04 and 0.13, with an X mean D value of 0.37–1.31. The results further uncovered that the % accuracy of classification for 0-14 days was achieved at 100%, and 87.58% for 14–35 days up to 1 standard deviation (SD) respectively. Graphical abstract: Image, graphical abstract