| Popis: |
Abstract The significance of waterlogged archaeological wood (WAW) lies in its profound informational value, encompassing historical, cultural, artistic, and scientific aspects of human civilization, and therefore need to be properly studied and preserved. In this study, the utilization of near-infrared (NIR) spectroscopy is employed as a predictive tool for assessing the hardness value of WAW. Given the submerged burial conditions, waterlogged wooden heritage frequently undergo substantial degradation in their physical and mechanical properties. The mechanical properties of waterlogged wooden heritage are essential for evaluating their state of preservation and devising appropriate conservation and restoration strategies. However, conventional methods for testing mechanical properties are limited by factors such as the availability of adequate sample size and quantity, adherence to the “principle of minimum intervention,” and cost considerations. NIR spectroscopy is a non-destructive, rapid, sensitive, and low-cost analytical technique with great potential for application in this area. In this study, two large and significant ancient Chinese shipwrecks were investigated. One hundred ninety-seven samples were collected and analyzed using NIR spectroscopy and a portable C-type shore hardness testing method. A partial least squares (PLS) regression model was developed to predict the hardness of the WAW. The model was optimized and validated using different preprocessing methods and spectral ranges. The results indicate that the best models were obtained with first derivatives + multiple scattering corrections (MSC) and first derivatives + standard normal variate (SNV) preprocessing in the 1000–2100 nm spectral range, both with an R 2 c of 0.97, a root mean squared error of correction (RMSEC) of 2.39 and 2.40, and a standard error of correction (SEC) of 2.40 and 2.41. Furthermore, they exhibited an R 2 v of 0.89 and 0.87, a root mean squared error of cross-validation (RMSECV) of 4.43 and 4.67, a standard error of cross-validation (SECV) of 4.45 and 4.68, and RPD values of 3.02 and 2.88, respectively. A coefficient of determination of the established prediction model (R 2 p) of 0.89 with a relative standard deviation for prediction (RSD) of 6.9% |
