Identification and differentiation of insect infested rice grains varieties with FTNIR spectroscopy and hierarchical cluster analysis. (1st December 2018)

• Record Type: Journal Article
• Authors: Srivastava, Shubhangi; Mishra, Gayatri; Mishra, Hari Niwas
• Is Part Of: Food chemistry. Volume 268(2018)
• Page Start: 402
• Page End: 410
• Publication Date: 2018-12-01
• Subjects: Algorithm -- Clusters -- Dendrograms -- Histograms -- Hierarchical -- Infested rice; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2018.06.095
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: FTNIR & HCA used for identification of infested rice stored varieties. Heterogeneity with infestation level was identified in rice by cluster & dendrogram analysis. FTNIR with HCA has potential for sorting rice varieties where quantification is not required. Abstract: The potential and practicality of FTNIR as a screening tool, with ward's algorithms, was performed for two different varieties of rice namely, ' badshah bhog ' and ' swarna ', followed by cluster, dendrogram, histogram, and conformity analysis with different storage periods (0–225) and insect infestation. Dendrogram analysis resulted in a clear differentiation between infested rice varieties with non-infested ones while hierarchical cluster analysis, lead to detection of different levels of infestations. Histograms analysis of averaged FTINR spectra of rice grains samples provided 100% classification between infested and non-infested samples. Dissimilarities between rice grains were calculated using Pearson's correlation coefficients which were further converted to D values, and heterogeneity among the different varieties of rice along with a different level of infestation was identified. The results further revealed that the percentage accuracy (%) of classification for badshah bhog varied from 93.10 to 98.84%, while that for the swarna rice was between 95.75 and 99.74%.