Weeds contribute to biodiversity and a wide range of ecosystem functions. It is crucial to map different weed species and analyze their physiological activities. Remote sensing techniques for plant identification, especially hyperspectral imaging, are being developed using spectral response patterns to vegetation for detection and species identification. A library of hyperspectral images of 40 urban weed species in northeast China was established in this study. A terrestrial hyperspectral camera was used to acquire 435 hyperspectral images. The hyperspectral information for each weed species was extracted and analyzed. The spectral characteristics and vegetation indices of different weeds revealed the differences between weed species in the cities of northeast China and indirectly characterized the growth and physiological activity levels of different species, but could not effectively distinguish different species. Five methods—first derivative spectrum (FDS), second derivative spectrum (SDS), standard normal variate (SNV), moving averages (MA), and Savitzky-Golay (SG) smoothing—were used to pretreat the spectral curves to maximize the retention of spectral characteristics while removing the influence of noise. We investigated the application of a convolutional neural network (CNN) with terrestrial hyperspectral remote sensing to identify urban weeds in northeast China. A CNN classification model was established to distinguish weeds from the hyperspectral images and demonstrated a test accuracy of 95.32% to 98.15%. The accuracy of the original spectrum was 97.45%; SNV had the best accuracy (98.15%) and SG was the least accurate (95.32%). This provides a baseline for understanding the hyperspectral characteristics of urban weed species and monitoring their growth. It also contributes to the development of a hyperspectral imaging database with global applicability.

In addition to supplying essential nutrients, some food proteins can confer additional health benefits beyond nutrition. The presence of bioactive proteins and peptides in different foods is a factor not currently taken into consideration when assessing the dietary quality of food proteins. The range of described physiological benefits attributed to bioactive proteins and peptides is diverse. Multiple factors can potentially impact on the ability of a bioactive peptide or protein to elicit an effect. Although some food proteins act directly in their intact form to elicit their effects, generally it is peptides derived from digestion, hydrolysis or fermentation that are of most interest. The levels of bioactive peptides generated must be sufficient to elicit a response, but should not be so high as to be unsafe, thus causing negative effects. In addition, some peptides cause systemic effects and therefore must be absorbed, again in sufficient amounts to elicit their action. Many studies to date have been carried out in vitro; therefore it is important that further trials are conducted in vivo to assess efficacy, dose response and safety of the peptides, particularly if health related claims are to be made. Therefore, methods must be developed and standardised that enable the measurement of health benefits and also the level of bioactive peptides which are absorbed into the bloodstream. Once standardised, such methods may provide a new perspective and an additional mechanism for analysing protein quality which is currently not encompassed by the use of the protein digestibility-corrected amino acid score (PDCAAS).