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利用近红外光谱和数字图像对美味的地面烘焙咖啡进行无损认证。

Non-destructive authentication of Gourmet ground roasted coffees using NIR spectroscopy and digital images.

机构信息

Universidade Federal da Paraíba, Centro de Ciências Exatas e da Natureza, Departamento de Química, CEP 58051-970, João Pessoa, PB, Brazil.

Programa de Pós-Graduação em Química Pura e Aplicada, Universidade Federal do Oeste da Bahia, Campus Reitor Edgard Santos, Rua Bertioga, 892, Bairro Morada Nobre I, CEP 47.810-059, Barreiras, BA, Brazil.

出版信息

Food Chem. 2021 Dec 1;364:130452. doi: 10.1016/j.foodchem.2021.130452. Epub 2021 Jun 24.

Abstract

The growing demand for excellent-quality coffees allied with their symbolic aestheticization that add value to the products favor the adulteration practices and consequently economic losses. So, this work proposes the suitability of NIR spectroscopy and Digital Images (from CACHAS) coupled with one-class classification methods for the non-destructive authentication of Gourmet ground roasted coffees. For this, Gourmet coffees (n = 44) were discriminated from Traditional (n = 36) and Superior (n = 10) by directly analyzing their powder without any sample preparation. Then, OC-PLS and dd-SIMCA were used to construct the models. dd-SIMCA using offset correction for NIR and RGB histogram for CACHAS achieved the best results, correctly recognizing all the 90 samples in both the training and test sets. Therefore, the proposed methodologies can be useful for both the consumers and regulatory agencies because it confirms the elevated standards of excellence of Brazilian specialty coffees, preventing fraudulent labeling, besides following the Principles of Green Analytical Chemistry.

摘要

随着对优质咖啡需求的不断增长,以及它们为产品增添价值的象征美学,这使得掺假行为盛行,进而导致经济损失。因此,这项工作提出了近红外光谱和数字图像(来自 CACHAS)与单类分类方法相结合,用于非破坏性鉴别美食磨碎烘焙咖啡的适用性。为此,通过直接分析粉末而无需任何样品制备,将美食咖啡(n=44)与传统咖啡(n=36)和特级咖啡(n=10)区分开来。然后,使用 OC-PLS 和 dd-SIMCA 构建模型。dd-SIMCA 使用近红外和 CACHAS RGB 直方图的偏移校正,在训练和测试集中都正确识别了所有 90 个样本,取得了最佳结果。因此,所提出的方法可以为消费者和监管机构提供有用的信息,因为它可以确认巴西特种咖啡的卓越标准,防止欺诈性标签,同时遵循绿色分析化学原则。

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