Cameras at nest sites are becoming a common means for quantifying nestling diet, but there are two problems associated with this method: food items delivered to nestlings often cannot be identified, and quantification of error around diet estimates for individual nests is problematic. We present a novel method of incorporating unidentified food items into diet estimates and quantifying error around these estimates for individual nests. In our method, unidentified food items are accounted for by considering all of the possible ways in which they could be allocated among previously defined food categories (possible outcomes). We then calculate the probability of each possible outcome by assuming the probability that an unidentified food item belongs to any given category is equal to the proportion of identified items from that category. All possible outcomes, along with the probability of each, represent a probability space. We allocate the unidentified food items to each category according to the most probable outcome in the probability space when estimating the contribution of each food category to nestling diets. Confidence intervals around diet estimates for each food category are estimated by simulating many samples from this probability space and using kernel density estimation. We demonstrate the implementation of our method with data from motion-sensitive cameras monitoring Arctic peregrine falcon Falco peregrinus tundrius nests in Nunavut, Canada.