Abstract Laser Induced Breakdown Spectroscopy (LIBS) was combined with some machine learning techniques, such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) in order to obtain useful information concerning the classification of olive oil samples (authentication of their geographic origin) and the detection of adulteration. Since the plasma characteristics can depend substantially on the state of a sample (solid, gas or liquid) three different configurations of handling the sample were examined, spray of olive oil, a thin laminar flow and the free surface of few gr of olive oil sample. Then, the effects of experimental parameters, such as the laser energy, the temporal gating conditions (i.e., delay time and integration time) of the CCD detector of the spectrometer, on the plasma characteristics and subsequently on the classification results, were thoroughly investigated and analyzed. The combination of LIBS with the machine learning techniques used, resulted in excellent classification results of the olive oils studied, achieving classification accuracies of 100%.

中文翻译：

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机器学习辅助的激光诱导击穿光谱用于橄榄油分类：实验参数的影响

摘要 激光诱导击穿光谱 (LIBS) 与一些机器学习技术相结合，如主成分分析 (PCA) 和线性判别分析 (LDA)，以获得有关橄榄油样品分类的有用信息（验证其地理来源) 和掺假检测。由于等离子体特性在很大程度上取决于样品的状态（固体、气体或液体），因此检查了处理样品的三种不同配置，橄榄油喷雾、薄层流和几克橄榄油样品的自由表面. 然后，实验参数的影响，如激光能量、光谱仪 CCD 探测器的时间选通条件（即延迟时间和积分时间），对血浆特性以及随后的分类结果进行了彻底的调查和分析。LIBS 与所使用的机器学习技术相结合，对所研究的橄榄油产生了出色的分类结果，达到了 100% 的分类准确率。