all rights reserved there is an increased consumer interest in the potential health benefits from regular consumption of blueberries (Vaccinium sp.) due to their antioxidant properties shown to be correlated to inhibition of cancer cell proliferation. 1 a major quality concern for the blueberry industry is the internal infestation of fruit by the blueberry fruit fly larvae.for the last few decades, near infrared (nIr) spectroscopy has shown considerable promise for the non-destructive analysis of food products and is ideally suited for on-line measurements in the agrofood industry due to its advantages: minimal or no sample preparation, versatility, speed and low-cost of analysis. 2 Most of the well-known applications of nIr spectroscopy in fruits 3 have focused on the quantitative prediction of chemical composition, internal damage and ripening stage in various fruits such as: kiwifruit, 4,5 apple and mango, 6,7 cherry, 8 grape, 9 plum and nectarine 10 and dates. 11 Grain Marketing and production research center, uSda-arS, Manhattan, KS, uSa c Biological Sciences, university of Maine, orono, Me, uSa A near infrared (NIR) spectroscopy system for rapid, automated and non-destructive detection of insect infestation in blueberries is desirable to ensure high quality fruit for the fresh and processed markets. The selection of suitable instruments is the first step in system development. Three diode array spectrophotometers were evaluated based on technical specifications and capacity for larva detection in wild blueberries (Vaccinium angustifolium) using discriminant partial least squares (PLS) regression models. These instruments, differing mainly in wavelength range and detector type, comprised two spectrophotometers with scanning wavelength ranges of 650-1100 nm and 600-1700 nm and an imaging spectrograph with the scanning range of 950-1400 nm. The assessed factors affecting predictions included signal-to-noise ratio, wavelength range, resolution, measurement configuration, spectral pre-processing and absorbance bands related to infestation. The scanning spectrophotometers demonstrated higher signal-to-noise ratios with infestation prediction accuracies of 82% and 76.9% compared to the imaging spectrograph with 58.9% accuracy. Resolution, spectral pre-processing and measurement configuration had a lesser effect on model accuracy than wavelength range. The 950-1690 nm bands were identified as important for infestation prediction. In general, NIR spectroscopy should be a feasible technique for rapid classification of insect infestation in fruit.
