As the food and pharmaceutical industries move toward more sustainable forms of packaging, paper is experiencing somewhat of a comeback as a packaging material. However, by its very nature paper is rich in volatiles—both with and without significant olfactory impact. Human sensory analysis has long been the benchmark for determining that a product is fit for consumer application. However, sensory analysis is limited in its application due to the high per‐sample cost. Selected ion flow tube mass spectrometry (SIFT‐MS) is an instrumental approach that has potential to rapidly screen paper samples for both sensory and chemical composition attributes in a single broad‐spectrum and sensitive analysis of volatiles. In this study, a combined approach utilizing SIFT‐MS sample analysis with multivariate statistical data processing is evaluated for its ability to distinguish between samples of different paper composition (in particular, virgin, recycled, or mixed fibres), mill of origin (manufacturing mill), sensory odour intensity rating, and sensory odour note. Paper samples from four mills were analysed using instrumental and sensory methods, together with one pulp sample. The key discriminating volatiles varied depending on the classification model but typically included hydrogen sulfide, ethanol, acetone, hexanal, dimethyl sulfide, and formaldehyde. The pulp sample generally exhibited significantly higher concentrations of volatiles—especially methanol and hydrogen sulfide. Because it was so different, it was eliminated from certain models—in particular, for determination of the mill of origin. Chemical analysis with multivariate statistical processing exhibited near‐perfect classification of the fibre type and the mill of origin. Evaluation of instrumental prediction of odour intensity rating and odour note used multivariate statistical analysis to correlate sensory and instrumental results. It was found that concentration data provided superior classification of odour intensity rating and odour note than did odour activity values (compound concentrations divided by their human odour detection thresholds [ODTs]). This result is tentatively attributed to significant uncertainties introduced by literature ODTs and the method's limit of quantitation being higher than the ODT for several compounds. When using SIFT‐MS concentration data—when coupled with multivariate statistical data processing—this preliminary study demonstrates potential for rapid, instrumental determination of the odour intensity rating and odour note traditionally determined using human sensory analysis.

中文翻译：

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评估选定的离子流管质谱仪，以快速仪器确定纸张类型，来源和感官属性

随着食品和制药行业朝着更具可持续性的包装形式发展，纸张作为包装材料正逐渐卷土重来。但是，就其本质而言，纸张富含挥发物，无论有没有重大的嗅觉影响。长期以来，人体感官分析一直是确定产品适合消费者应用的基准。但是，由于每个样本的成本较高，因此感官分析的应用受到限制。选定的离子流管质谱仪（SIFT-MS）是一种仪器方法，可以在单个广谱和灵敏的挥发物分析中快速筛选纸质样品的感官和化学成分属性。在这项研究中，使用SIFT-MS样品分析和多元统计数据处理的组合方法评估了其区分不同纸张成分（特别是原始纤维，再生纤维或混合纤维），原纸厂（制造厂），感官气味的能力强度等级和感官气味说明。使用仪器和感官方法分析了来自四家工厂的纸样，以及一种纸浆样品。关键的区分挥发物根据分类模型而有所不同，但通常包括硫化氢，乙醇，丙酮，己醛，二甲基硫和甲醛。纸浆样品通常表现出明显更高的挥发物浓度，尤其是甲醇和硫化氢。因为它是如此不同，所以从某些模型中删除了它，特别是，确定原产地。经过多变量统计处理的化学分析显示，纤维类型和原纤维的分类近乎完美。对气味强度等级和气味记录的仪器预测的评估使用了多元统计分析来关联感官和仪器结果。已发现，浓度数据比气味活性值（化合物浓度除以其人类气味检测阈值[ODT]）提供的气味强度等级和气味记录具有更好的分类。暂时将该结果归因于文献ODT引入的显着不确定性，并且该方法的定量限高于几种化合物的ODT。