Project description:Insects are gaining more and more space in food and feed sectors, creating an intense scientific interest towards insects as food ingredients. Several papers deal with cereal-based products complemented by insect powder in the past few years. However, adulteration and quality control of such products present some hot topics for researchers, e.g., how can we justify the amounts and/or species of the insects used in the given products? Our paper aims to answer such questions by analysing seven edible insect powders of different species independently. The mixtures with wheat flour were analysed using near infrared spectroscopy and chemometric methods. Not only powders of different species were clearly differentiated, but also mixtures created by different amounts of wheat flour. Prediction of insect content showed 0.65% cross-validated error. The proposed methodology gives an excellent tool for quality control of insect-based cereal food products.
Project description:Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.
Project description:We develop a model for ribosome-protected fragment (RPF) spectra that accounts for the local codon context-dependent variation of peptide elongation times and fragment processing biases. We use a Marquardt algorithm for non-linear regression with maximum likelihood (ML) like statistics to fit the RPF sequencing data. Taking advantage of the factorial character of the present model, we reconstruct the parameters adhering to an ideal, unbiased RPF spectrum.