Abstract The presence in raw sugarcane of low levels of solid impurities from soil particles and green and dry/brown sugarcane leaves is relevant to improving sugar mill production performance. Two ranges of impurities for raw sugar manufacturing processes need to be characterized from 0 to 5 wt% (desired material) and 8 to 10 wt% (undesired material); these ranges are denoted as 1 and 2, respectively. Laser-induced breakdown spectroscopy (LIBS) combined with chemometrics is used to detect chemical elements and different impurity ranges in leached raw sugarcane solutions. The potential use of LIBS based on leached solutions immobilized in a polyvinyl alcohol (PVA) polymer requires approximately 2 h sample preparation time. LIBS data are assigned to the above two impurity ranges using fusion of multiple classifiers. Most classifiers require a training set and optimization of a tuning parameter to select the best model; however, the sum fusion across a tuning parameter window used for classifying the samples in this study is a process that does not require either. The classification results are 97% accuracy for both ranges; 94% and 100% specificity for ranges 1 and 2, respectively; and 100% and 94% sensitivity for ranges 1 and 2, respectively. The classification results indicate potential for future applications in sugarcane refineries.

中文翻译：

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使用激光诱导击穿光谱 (LIBS) 和跨调谐参数窗口的总和融合对原甘蔗中的杂质范围进行分类

摘要 来自土壤颗粒和绿色和干燥/棕色甘蔗叶的低水平固体杂质在原甘蔗中的存在与提高糖厂生产性能有关。原糖制造过程中需要表征两个范围的杂质：0 至 5 重量％（所需材料）和 8 至 10 重量％（非所需材料）；这些范围分别表示为 1 和 2。激光诱导击穿光谱 (LIBS) 与化学计量学相结合，用于检测浸出的粗甘蔗溶液中的化学元素和不同的杂质范围。基于固定在聚乙烯醇 (PVA) 聚合物中的浸出溶液的 LIBS 的潜在用途需要大约 2 小时的样品制备时间。使用多个分类器的融合将 LIBS 数据分配到上述两个杂质范围。大多数分类器需要一个训练集和一个调整参数的优化来选择最佳模型；然而，本研究中用于对样本进行分类的调整参数窗口的总和融合是一个不需要的过程。两个范围的分类结果都是 97% 的准确率；范围 1 和 2 的特异性分别为 94% 和 100%；范围 1 和范围 2 的灵敏度分别为 100% 和 94%。分类结果表明未来在甘蔗精炼厂中的应用潜力。分别; 范围 1 和范围 2 的灵敏度分别为 100% 和 94%。分类结果表明未来在甘蔗精炼厂中的应用潜力。分别; 范围 1 和范围 2 的灵敏度分别为 100% 和 94%。分类结果表明未来在甘蔗精炼厂中的应用潜力。