The data generated from NTA is typically vast and complex, necessitating advanced bioinformatics approaches for interpretation. Software tools and databases such as mass spectral libraries and chemical databases are essential for identifying unknown compounds. Machine learning and artificial intelligence are increasingly being employed to enhance the accuracy and speed of compound identification.
