As the global community seeks effective ways to manage carbon and combat climate change, biochar has emerged as a key technology for sequestering carbon in soil and promoting resource circularity. In a study published in Scientific Reports, researchers Yejin Kim, Chaewon Hwang, Haewon Shin, Sung-Wook Hwang, and Bonwook Koo explored how underutilized forest biomassBiomass is a complex biological organic or non-organic solid product derived from living or recently living organism and available naturally. Various types of wastes such as animal manure, waste paper, sludge and many industrial wastes are also treated as biomass because like natural biomass these More, which often goes to waste through open burning, can be converted into high-value carbon materials. In the Republic of Korea alone, approximately 1.43 million tons of this forest biomass is generated annually, yet its heterogeneous nature makes it difficult to assess quality using traditional methods. Conventional elemental analysis is often costly, time-consuming, and destructive, creating a significant barrier for small-scale producers and field applications that require rapid quality control.

To bridge this gap, the research team developed a non-destructive prediction framework using a specialized type of infrared spectroscopy. By scanning biochar samples produced at temperatures of 200°C, 300°C, and 400°C, the researchers were able to capture complex chemical fingerprints without damaging the material. When these light-based scans were combined with advanced mathematical modeling, the system achieved a high coefficient of determination, predicting critical indicators like total carbon content and atomic ratios with remarkable precision. Specifically, the optimized models reached an accuracy level above 0.94 for all primary carbon indices. This high predictive power demonstrates that quality assessment can be performed rapidly and reliably, potentially replacing more cumbersome laboratory techniques.

The success of this model relies on identifying key spectral regions that change as wood transforms into biochar. As carbonization temperatures rise, the wood undergoes chemical shifts, such as the loss of oxygen and hydrogen and the formation of stable aromatic structures similar to those found in coal. The study utilized a statistical technique known as variable importance in projection to highlight exactly which parts of the light spectrum were most informative. They found that regions related to the breakdown of carbohydrate backbones and the conversion of aliphatic structures into aromatic rings were the most critical for accurate predictions. These insights not only improve the model’s performance but also help scientists understand the molecular evolution of biochar during the heating process.

By incorporating rigorous data cleaning steps, such as the detection and removal of outliers, the researchers ensured the model remained robust even when dealing with the natural variations found in forest waste. This focus on model interpretability and practical deployment makes the technology particularly suited for real-world field applications where rapid decision-making is essential. Ultimately, this light-based scanning approach offers a sustainable and efficient platform for biochar characterization. By making it easier to monitor production and verify quality, this research supports the broader adoption of biochar as a geoengineering tool, helping to transform forest residues into a stable carbon sink that benefits both the environment and the economy.

Source: Kim, Y., Hwang, C., Shin, H., Hwang, S. W., & Koo, B. (2026). Non-destructive prediction of carbonization indices in biochar derived from underutilized forest biomass using ATR-IR chemometric modeling. Scientific Reports.