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%.

利用沃德算法，FTNIR作为筛选工具的潜力和实用性针对两种不同的水稻品种“ badshah bhog ”和“ swarna ”进行了研究。'，然后进行聚类，树状图，直方图和具有不同存储时间段（0-225）和昆虫侵扰的合格性分析。通过树状图分析，可以清楚地区分出受侵染的水稻品种与未侵染的水稻品种，同时进行层次聚类分析，从而可以检测出不同程度的侵染。稻谷样品的平均FTINR光谱的直方图分析提供了受侵染和未受侵染的样品之间100％的分类。利用皮尔森相关系数计算出水稻籽粒之间的差异，并将其进一步转换为D值，并鉴定出不同水稻品种之间的异质性以及不同程度的侵染。结果进一步表明，badshah bhog分类的百分比准确性（％）斯瓦纳（Swarna）稻的波动率从93.10到98.84％，介于95.75和99.74％之间。