Tissue engineering is a promising approach to address the unmet clinical need for treating cartilage damage. Monitoring the characteristics of tissue-engineered cartilage constructs (TECs) during culture is critical for optimizing culture conditions and assessing TEC development. An important characteristic of TEC development is the ratio of glycosaminoglycans (GAGs) to DNA. However, current methods for quantifying GAGs and DNA are destructive, making them impractical for longitudinal monitoring. This study investigates the potential of optical spectroscopy in the visible and near-infrared (NIR) ranges coupled with machine learning (ML) for non-destructive prediction of GAGs and DNA in TECs. Six types of TECs were incubated for 7 or 28 days (n = 6 × 2 × 3). Spectral data were acquired from the constructs in the NIR and visible-short-NIR (VIS-SNIR) ranges using a fiber-optic probe and a multichannel spectrometer, followed by biochemical quantification of GAGs and DNA. ML models were developed for predicting the GAGs and DNA from the spectra and evaluated by leave-one-sample-out cross-validation after determining the optimal spectral range, preprocessing method, and ML algorithm. The performance of the models for predicting the construct's relative maturity (based on culture duration) using the predicted GAGs/DNA ratio was also evaluated. A NIR-based model was optimal for DNA content prediction (R² = 0.81), while a VIS-SNIR-based model was optimal for GAGs prediction (R² = 0.79). The predicted GAGs/DNA ratios were 100 % accurate in distinguishing immature TECs from more mature ones. Thus, optical spectroscopy combined with ML can enable non-destructive prediction of the maturity of TECs via predicting their DNA and GAG contents.

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.