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Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Objective: To evaluate the feasibility of Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy to detect cartilage degradation due to osteoarthritis and to validate the methodology with osteochondral human cartilage samples for future development towards clinical use. Design: Cylindrical (d ​= ​4 ​mm) osteochondral samples (n ​= ​349) were prepared from nine human cadavers and measured with FTIR-ATR spectroscopy. Afterwards, the samples were assessed with Osteoarthritis Research Society International (OARSI) osteoarthritis cartilage histopathology assessment system and divided into two groups: 1) healthy (OARSI 0–2) and 2) osteoarthritic (OARSI 2.5–6). The classification was done with partial least squares discriminant analysis model utilizing cross-model validation. Receiver operating characteristics curve analysis was performed and the area under curve (AUC) was calculated. Results: For all samples combined, classification accuracy was 73% with AUC of 0.79. Femoral samples had accuracy of 74% and AUC of 0.77, while tibial samples had accuracy of 66%, and AUC of 0.74. Patellar samples had accuracy of 84% and AUC of 0.91. Conclusions: The results indicate that FTIR-ATR spectroscopy can differentiate between healthy and osteoarthritic femoral, tibial and patellar human tissue. If combined with a fiber optic probe, FTIR-ATR spectroscopy could provide additional objective intraoperative information during arthroscopic surgeries, which could improve clinical outcomes.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

This thesis reports on studies of plasmonic nanoparticles and particularly gold nanostars as signal enhancers and contrast agents for biophotonic applications including visualisation, treatment of living cells and chemical sensing. In this thesis, the optical properties of nanoparticles of different size and morphology and their silica composites were compared. Because they are the most suitable plasmonic nanostructures, gold nanostars were utilised for optical imaging modalities such as confocal microscopy and Doppler optical coherence tomography. The ability of gold nanoparticles to enhance the signal in surface-enhanced vibrational spectroscopy, including Raman and Fourier transform infrared spectroscopy was additionally studied. Finally, various gold nanoparticles were applied for cell optoporation to increase the penetration ability of exogeneous substances. In summary, significant advantages of nanostars such as their low-toxicity, high scattering and contrast abilities, in addition to a broad, tunable, plasmon resonance wavelength range, as well as the capability to enhance the signal of analyte molecules in vibrational spectroscopy were demonstrated in this thesis. The results of this study on the effectiveness of nanostars have a broad scope of utility and open a wide perspective for their utilisation in nanobiophotonics and biomedicine., Tiivistelmä Tämä opinnäytetyö kertoo tutkimuksista, joissa plasmoninanopartikkeleita ja erityisesti kultananotähtiä on käytetty signaalinvahvistimina biofotoniikan sovelluksissa, kuten visualisointi, elävien solujen käsittely ja kemiallinen tunnistus. Tässä työssä verrattiin eri kokoisten ja muotoisten nanopartikkeleiden ja niiden piioksidikomposiittien optisia ominaisuuksia. Sopivimpina plasmoninanorakenteina kultananotähtiä käytettiin optisiin kuvantamismenetelmiin, kuten konfokaalimikroskopiaan ja Doppler-optiseen koherenssitomografiaan. Lisäksi kuvattiin myös kultananopartikkelien kykyä parantaa pinta-aktivoidun värähtelevän spektroskopian signaalia, mukaan lukien Raman- ja Fourier-muunnos-infrapuna-spektroskopia. Lopuksi, eri kultananopartikkeleita käytettiin soluoptoporaatioon eksogeenisten aineiden läpäisevyyden lisäämiseksi. Yhteenvetona, työssä osoitettiin nanotähtien merkittävät edut, kuten matala-myrkyllisyys, suuret sironta- ja kontrastiominaisuudet, laaja plasmoniresonanssin aallonpituusalue ja sen viritettävyys, sekä kyky parantaa analyyttimolekyylien signaalia värähtelyspektroskopiassa. Niinpä tutkimustulokset nanotähtien tehokkuudesta ovat laajasti käyttökelpoisia ja ne avaavat laajan näkökulman niiden hyödyntämiseen nanobiofotoniikassa ja biolääketieteessä.