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通过红外光声光谱法和化学计量学分析的具有特定缺陷的烘焙咖啡数据。

Data on roasted coffee with specific defects analyzed by infrared-photoacoustic spectroscopy and chemometrics.

作者信息

Dias Rafael Carlos Eloy, Valderrama Patrícia, Março Paulo Henrique, Dos Santos Scholz Maria Brigida, Edelmann Michael, Yeretzian Chahan

机构信息

Zurich University of Applied Sciences (ZHAW), Institute of Chemistry and Biotechnology, Coffee Excellence Center, Einsiedlerstrasse 31, CH - 8820 Wädenswil, Switzerland.

Federal Technological University of Paraná State - UTFPR, Post-Graduation Program in Food Technology - PPGTA, Via Rosalina Maria dos Santos, 1233, Postal Code 271, 87301-899, Campo Mourão, Paraná, Brazil.

出版信息

Data Brief. 2018 Aug 9;20:242-249. doi: 10.1016/j.dib.2018.08.013. eCollection 2018 Oct.

Abstract

This article contains data related to the research article entitled "Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy" (Dias et al., 2018) [1]. A method potentially able for assessing the quality of roasted ground coffees is described in the origin paper. Infrared spectroscopy and photoacoustic detection (FTIR-PAS) associated with multivariate calibration were used. The samples were obtained blending whole and healthy coffee beans ( and ) with specific blends of defects, named , which contain broken, sour, and black beans, skin, woods and healthy beans still not collected. In addition to a reduction in commercial value, the presence of defects compromises the sensory attributes of coffee. On the other hand, selections are commonly found in coffee crops and can be added intentionally to the product. Twenty-five selections were used to obtain a panel of 154 blends. The FTIR-PAS spectra of each sample generated the prediction model of Partial Least Squares Regression parameters, which are also presented here.

摘要

本文包含与题为《通过红外光声光谱法对烘焙咖啡粉中的特定缺陷进行定量评估》(迪亚斯等人,2018年)[1]的研究论文相关的数据。原始论文中描述了一种潜在的能够评估烘焙咖啡粉质量的方法。使用了与多元校准相关的红外光谱和光声检测(傅里叶变换红外光声光谱法)。通过将完整且健康的咖啡豆(和)与特定的缺陷混合物(称为)混合来获取样品,该缺陷混合物包含破碎的、发酸的和黑豆、豆皮、豆梗以及尚未采摘的健康豆子。除了商业价值降低外,缺陷的存在还会损害咖啡的感官特性。另一方面,在咖啡作物中通常会发现有缺陷的咖啡豆,并且可以有意添加到产品中。使用了25种有缺陷的咖啡豆来获得一组154种混合物。每个样品的傅里叶变换红外光声光谱法光谱生成了偏最小二乘回归参数的预测模型,本文也展示了这些模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0adb/6104563/9988000232f6/gr1.jpg

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