Autism spectrum disorder (ASD) is a neurodevelopmental disorder that begins early in life and continues lifelong with strong personal and societal implications. It affects about 1%–2% of the children population in the world. The absence of auxiliary methods that can complement the clinical evaluation of ASD increases the probability of false identification of the disorder, especially in the case of very young children. In this study, analytical models for auxiliary diagnosis of ASD in children and adolescents, based on the analysis of patients’ blood serum ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) spectra, were developed. The models use chemometrics (either Principal Component Analysis (PCA) or Partial Least Squares Discriminant Analysis (PLS-DA)) methods, with the infrared spectra being the X-predictor variables. The two developed models exhibit excellent classification performance for samples of ASD individuals vs. healthy controls. Interestingly, the simplest, unsupervised PCA-based model results to have a global performance identical to the more demanding, supervised (PLS-DA)-based model. The developed PCA-based model thus appears as the more economical alternative one for use in the clinical environment. Hierarchical clustering analysis performed on the full set of samples was also successful in discriminating the two groups.

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world’s population of children, and about 30–50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2–5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700–900 cm⁻¹ range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.

Seeds belonging to fourth generation mutants (M ₄ ) of Ataem-7 cultivar (A7) variety and S04-05 (S) breeding line salt-tolerant soybeans were investigated by Raman spectroscopy, complemented by chemometrics methods, in order to evaluate changes induced by mutations in the relative lipid–protein contents, and to find fast, efficient strategies for discrimination of the mutants and the control groups based on their Raman spectra. It was concluded that gamma irradiation caused an increase in the lipid to protein ratio of the studied Ataem-7 variety mutants, while it led to a decrease of this ratio in the investigated S04-05 breeding line mutants. These results were found to be in agreement with data obtained by reflectance spectrum analysis of the seeds in the full ultraviolet to near-infrared spectral region and suggest the possibility of developing strategies where gamma irradiation can be used as a tool to improve mutant soybean plants targeted to different applications, either enriched in proteins or in lipids. Ward's clustering and principal component analysis showed a clear discrimination between mutants and controls and, in the case of the studied S-type species, discrimination between the different mutants. The grouping scheme is also found to be in agreement with the compositional information extracted from the analysis of the lipid–protein contents of the different samples.