## 6 Conclusions and Future Work  
To sum up, despite the imbalanced data (which is known to pose difficulties in classification methods), this study supports the use of physiological responses to odours to classify ASD in a sample of adult individuals. This is consistent with previous studies relying on sniff responses and EDA [26; 28] and provides preliminary data on the potential use of facial EMG data as well. Since the most significant features correspond to the activity of facial muscles associated with negative emotions, the difference between ASD and TD groups may be greater when negative emotions are induced via the sense of smell. Given that these physiological changes occur at an unconscious level and are strongly  
Figure 4: Contingency table and respective precision, recall, negative predictive value, specificity, accuracy, and F1-score values. independent of language, communication, and attention, the use of ASD classifiers could aid in the development of diagnostic tools used in both clinical and research settings. Nevertheless, several limitations need to be considered. Firstly, caution is needed when interpreting the data due to the small and unequal sample size, which lowers the performance in predicting ASD. Additionally, the clinical manifestation of ASD is highly heterogeneous, which might be particularly critical in studies with small sample sizes due to an increased likelihood of artefacts. To overcome this limitation, future studies could focus on specific subgroups of ASD (e.g., ASD with or without Speech Onset Delay) or adopt a dimensional \"autistic traits\" approach (as proposed by [56]). Moreover, a profound assessment of possible comorbidities was not conducted in the ASD group. This is of foremost relevance, since approximately 70% of individuals diagnosed with ASD have co-occurring medical, developmental, or psychiatric conditions, such as intellectual disability, attention-deficit hyperactivity disorder, anxiety, and depression (see [57]). A detailed clinical (and cognitive) assessment would help to clarify if the obtained outcomes are indeed explained by the ASD diagnosis or rather by an interplay between multiple conditions. Another limitation concerns the non-inclusion of a clinical control group (e.g., individuals diagnosed with schizophrenia), and, therefore, it is not possible to assure our results are limited to ASD rather than generalised to other neurodevelopmental or psychiatric conditions. It should also be considered that groups were not matched for sex, age, and cognitive abilities. There is evidence that in general, women have better olfactory abilities than men (see [58]). Moreover, [PERSON] et al. [59] found a facial muscle response indicative of a fearful state in women exposed to fearful BO, whereas men failed to show any emotionally differentiated response to fearful and happy BO, suggesting that women are capable of establishing emotional synchrony via the sense of smell. Concerning a dimensional approach to ASD, [PERSON] et al. [60] found that being a woman and reporting higher attention to detail--a subscale of the Autistic Spectrum Quotient--was associated with better odour discrimination in the general population. Despite this evidence, since ASD diagnosis is more prevalent in men than in women (see [61])--a pattern also observed in our sample--most studies addressing olfaction in ASD either recruit male individuals exclusively or fail to acknowledge sex differences. Having this in mind, we cannot fully exclude a potential role of sex differences in the obtained outcomes and urge researchers to recruit larger samples, allowing them to further explore this effect. Furthermore, it is proposed to extend the dataset to better support the classifier and enable the application to the sub-block data, as it could not be used at this time due to the short response time associated with each sub-block (30 s). Concerning the prediction goal, other ML models should be considered so that performance comparisons can be made.