Conventional fluorescence spectroscopy has been suggested as a valuable tool for classifying wood species rapidly and non-destructively. However, because it is challenging to conduct absolute emission intensity measurements, fluorescence analysis statistics are difficult to obtain. In this study, another dimension of fluorescence, that is, fluorescence lifetime, was further evaluated to address this issue. A time-resolved fluorescence spectroscopic measurement system was first designed, mainly using a streak camera, picosecond pulsed laser at 403 nm, and a spectroscope, to collect the fluorescence time-delay (FTD) profiles and steady-state fluorescence intensity (FI) spectra simultaneously from 15 wood species. For data analysis, principal component analysis was used to “compress” the mean-centered FTD and FI spectra. Then, support vector machine classification analysis was utilized to train the wood species classification model based on their principal component scores. To avoid overfitting, ten-fold cross-validation was used to train the calibration model using 70 % of the total samples, and the remaining 30 % hold-out validation was used to test its reproducibility. The cross-validation accuracies were 100 % (5 softwoods) and 96 % (10 hardwoods), with test-validation accuracies of 96 % and 89 %.

中文翻译：

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使用化学计量学方法对荧光寿命成像数据进行无拟合分析，以进行快速、无损的木材树种分类

传统的荧光光谱法被认为是快速、非破坏性地对木材种类进行分类的有价值的工具。然而，由于进行绝对发射强度测量具有挑战性，因此很难获得荧光分析统计数据。在本研究中，进一步评估了荧光的另一个维度，即荧光寿命，以解决这个问题。首先设计了时间分辨荧光光谱测量系统，主要使用条纹相机、403 nm皮秒脉冲激光和分光镜，收集荧光时间延迟（FTD）曲线和稳态荧光强度（FI）光谱同时来自 15 种木材。对于数据分析，主成分分析用于“压缩”以平均值为中心的 FTD 和 FI 谱。然后，利用支持向量机分类分析来训练基于主成分得分的木材树种分类模型。为了避免过度拟合，使用总样本的 70% 使用十倍交叉验证来训练校准模型，并使用剩余的 30% 保留验证来测试其再现性。交叉验证准确度分别为 100%（5 颗软木）和 96%（10 颗硬木），测试验证准确度分别为 96% 和 89%。