## 2 Contributions in the Special Issue  
The paper by [PERSON] et al. [9] contributes to knowledge of the applicability of Fourier transform near-infrared (FT-NIR) spectroscopy. Because established techniques for cultivar analysis are destructive, expensive, and slow, the authors of this paper propose a combination of FT-NIR spectroscopy and discriminant analysis for the cultivar classification of sweet corn seeds. Their method was tested on 760 seed samples and allowed them to obtain a high classification accuracy (>99%), thus demonstrating the feasibility of the approach. The work by [PERSON] et al. [10] deals with the characterization of pure dielectric micro/nano powders with diffuse reflectance spectroscopy (DRS). Their methodology for preparing suitable samples of powder pellets in a well-reproducible way and granting high-quality DRS data/analysis is meticulously detailed, like a user manual, enabling laymen to approach this field for the first time. The study by [PERSON] and [PERSON] [11] proposes an approach to improve the accuracy of continuous-wave near-infrared spectroscopy (CW-NIRS) measurements of layered structures such as the human head that was devised to overcome the non-uniqueness of the solution of the diffusion equation by exploiting information derived from multiple measurement points and multiple wavelengths. In this way, in silico, both the tissue oxygenation index and the top layer thickness were recovered with an error of \(\leq\)4.4% in bilayer structures. An in vivo feasibility test was also reported and complemented the work. In the same field, [PERSON] et al. [12] reported the design, characterization, and validation of the tissue-mimicking phantom of a CW-NIRS device for brain functional imaging. Their prototype consisted of 12 dual-wavelength laser sources and 13 silicon photomultiplier (SiPM) detectors arranged inside an 8 \(\times\) 8 cm\({}^{2}\) area. The main advantage of their architecture is the absence of optical fibers (which are typical sources of losses and artefacts) and the use of SiPMs (offering high signal-to-noise ratios). In the field of diffuse correlation spectroscopy (DCS), [PERSON] and [PERSON] [13] applied an established theoretical model to analyze unconventional short source-detector separation measurements, which are unavoidable when analyzing small samples (e.g., small animals) or in constrained scenarios (e.g., endoscopy). With both in silico and on tissue mimicking phantoms, the authors demonstrated that the model could work well not only at traditional source-detector distances but also down to 1.5 mm. The paper by [PERSON] et al. [14] presents a multimodal approach that combines few-wavelength frequency-domain (FD) NIRS and broadband CW-NIRS acquisitions. It is capable of drastically increasing the quantification accuracy of tumor-like perturbations in diffuse optical imaging scenarios down to a depth of 1 cm, mainly targeting breast cancer diagnosis. In their work, [PERSON] and [PERSON] [15] propose the use of an analysis algorithm based on Bayesian filtering for the CW diffuse optical tomography (DOT) of the human brain. Their approach was shown to be more robust and reliable in the reduction of background physiological noise (due to, e.g., heart rate or ventilation) during in vivo measurements. [PERSON] et al. [16] present a functional near-infrared spectroscopy (fNIRS) study performed on 20 children with a commercially available CW device. Their goal was to quantify the effect on the updating function of the working memory of coordinative exercise interventions. The authors detected improved performance in the experimental group with respect to the control one in combination with evidence of a larger blood oxygenation increase during the task in the prefrontal area. The paper by [PERSON] et al. [17] presents the development of a time-domain (TD) DOT simulation platform used to estimate the in silico performance of a hand-held probe also under development in their research group.