Basic NIRS research consists of collecting a set NIR spectra with associated reference variables and then processing that data into a calibration model for a specific ap-plication. The same dataset can be further utilized in developing new methods for the various steps (e.g., preprocessing, variable selection, modeling, etc.) needed to construct calibration models. While data collection can be time consuming, require specialized laboratory expertise and equipment, the method development part of NIRS research (also known as chemometrics) is more akin to regular data science and machine learning. The field of chemometrics can, therefore, greatly benefit from publicly available datasets, as new methods can be developed tested on existing data without an expensive data collection phase.
In the past, several NIRS datasets have been released covering a wide range of application areas [142–144] but only a few of these were related to the analysis of biological tissues. Despite the rising interest in arthroscopic NIRS, no datasets on ar-ticular cartilage or other connective tissues of the knee have been published to date.
Data journals, i.e., journals focusing on publishing datasets with an accompanying article, are a relatively new phenomenon but are quickly gaining popularity. Data journals provide a suitable outlet not only for releasing the data but also providing in-depth documentation regarding the details. StudyIVaimed, for the first time, to provide an arthroscopic NIRS dataset to be used in the testing and development of new calibration models. Different parts of the published data had previously been analysed in four separate studies [11, 22, 124, 145] and this dataset was also utilized in the second example of studyIII. The dataset contains multiple NIRS measure-ments from articular cartilage regions of varying condition and health. The included reference properties cover the mechanical, chemical and histological characteristics of the tissue. Although the publication was primarily intended for NIRS research,
the wealth of reference measurements could very well be of interest to the wider scientific community.
9 Summary and conclusions
This thesis investigated the capability of the NIRS technique to quantitatively eval-uate various properties of ligaments and the patellar tendon of the knee. The inves-tigation was performed on sets of primary knee ligaments (including the patellar tendon) from ten bovine stifle joints which were fully characterized by a wide as-sortment of reference measurements covering the mechanical, morphological, struc-tural, and compositional properties of the tissue. Simultaneously, the thesis aimed to improve the general chemometric techniques related to the construction of NIRS calibration models. To this end, the thesis included the development, documen-tation, and release of an open source Python module for automatic generation of spectral preprocessing pipelines. Finally, an open dataset of NIRS measurements and associated reference variables obtained from the articular surfaces of equine fet-lock joints was published to further facilitate the development of improved methods for arthroscopic NIRS.
The main findings of the thesis can be summarized by the following points:
1. The highest accuracy of NIRS models predicting the mechanical and morpho-logical properties of knee ligaments was reached with properties related to tissue failure mechanics.
2. The highest prediction of accuracy of NIRS models predicting compositional and structural properties of knee ligaments was reached with water and colla-gen contents of the tissue.
3. NIRS shows potential for quantitative evaluation of ligaments and could plau-sibly be utilized for diagnostics during arthroscopies. Earlier studies have demonstrated the sensitivity of NIRS for assessing the properties of cartilage, meniscus and subchondral bone, indicating that the technique could be uti-lized for all connective tissues of the knee joint.
4. The automated preprocessing tool developed in this thesis can demonstrably improve the accuracy of NIRS calibration models. The open source spectral preprocessing Python module could be useful to researchers in the field of optical spectroscopy.
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