In the processing of raw cottons, the presence of entomological sugars from insect contamination on surfaces of lint can affect quality. Sticky sugars and other carbohydrates collect on processing machinery, inhibiting speed and efficiency. Current methods used before processing to identify and screen potentially sticky cottons containing entomological sugars are generally effective, but also time-consuming, costly, and difficult to bring into routine classing systems. A rapid, reliable, and

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... destructive test capable of detecting such sugars on cottons would be a welcome tool. Near infrared (NIR) spectral analysis may offer the potential to identify and quantify such entomological sugars, and this study explores that possibility. Two sugars, unique to whitefly and aphid honeydew, were applied to a non-insect contaminated cotton at different concentrations. Subsequent correlations of actual levels on fiber surfaces and NIR spectral analysis within a fiber moisture range of 50 to 100 g kg $1 (5-10%) were studied. It was concluded that a good relationship between NIR spectral analysis and entomological sugar content can be developed if the fiber moisture content is known or can be measured. ABSTRACT Entomological sugars from insect contamination of raw cotton (Gossypium hirsutum L.) lint not only can affect quality, but these sticky sugars and other carbohydrates also collect on processing machinery, inhibiting its speed and efficiency. In the search for a nondestructive, reliable, and quick test to identify potentially sticky cottons that could be used as a screening tool in a fiber classing system, a single non-insect contaminated cotton was treated with different concentrations of the two honeydew-specific sugars trehalulose and melezitose. High performance liquid chromatography (HPLC) analyses identified and quantified individual carbohydrate concentrations, then near infrared (NIR) spectra scans characterized untreated and treated cottons, that subsequently were conditioned to four fiber moisture levels, ranging from 46 to 93 g kg $ $ $ $1 (4.6-9.3%). Statistical analysis of data from chemical analysis and NIR spectra resulted in the selection of the fiber moisture content and 12 wavelengths as independent variables in multiple regression equations to predict concentrations of entomological sugars on these cottons. Calculations for linear correlation coefficients of predictability were able to classify cotton samples with different entomological sugar contents with 89.2% success ratio.