首页> 外文期刊>Journal of Cereal Science >Authentication of indigenous flours (Quinoa, Amaranth and kaniwa) from the Andean region using a portable ATR-Infrared device in combination with pattern recognition analysis
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Authentication of indigenous flours (Quinoa, Amaranth and kaniwa) from the Andean region using a portable ATR-Infrared device in combination with pattern recognition analysis

机译:使用便携式ATR红外装置与模式识别分析结合使用便携式ATR红外设备的Antean地区认证土着面粉(Quinoa,Amanth和Kaniwa)

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摘要

Quinoa (Chenopodium quinoa Willd.), kaniwa (Chenopodium pallidicaule Aellen) and kiwicha (amaranth, Chenopodium pallidicaule) are high nutritional value grains that are at risk for adulteration with less expensive grains. Our objective was to develop a rapid untargeted approach to authenticate high-value Andean grain flours by combining infrared spectroscopy and pattern recognition analysis. Samples (n = 106) were collected from Peruvian (n = 89) and US (n = 17) markets that included quinoa, amaranth, kahiwa and other common flours from the Andean region. Flour composition was characterized by reference methods including fat (Soxhlet, AOAC#922.06), protein (Dumas, ICC Standard #167), and fatty acid profile (AOAC #996.0). Spectra was collected by using a portable infrared system with a single-reflection diamond ATR crystal, and analyzed by Soft Independent Model of Class Analogies and Partial Least Square Regression. Authentic grain flours formed distinct clusters and commercial samples from Peruvian markets showed a prevalence of adulteration. Spectral differences were associated with the fingerprint region (1100-900 cm(-1)) due to differences in starch structure. Regression models were generated for rapid determination of fat (Rcv = 0.98, SECV = 0.3) and protein (Rcv = 0.98, SECV = 0.96) levels. New generation of portable infrared devices provided a viable tool for chemical profiling allowing for rapid, "in-field", and reliable authentication of food ingredients. (C) 2018 Elsevier Ltd. All rights reserved.
机译:奎奴亚藜(藜普藜王莹,藜哌妥),Kaniwa(Chenopopodium pallidicaule Aellen)和Kiwicha(苋菜,辛博二钠)是高营养价值颗粒,其面临较少昂贵的谷物的掺假风险。我们的目标是通过结合红外光谱和模式识别分析,制定一种快速未明确的方法来验证高价值的Andean谷物面粉。从秘鲁(n = 89)和美国(n = 17)市场收集样品(n = 106),其中包括奎奴亚藜,苋菜,Kahiwa和Andean地区的常见面粉。通过参考方法表征面粉组合物,包括脂肪(Soxhlet,AOAc#922.06),蛋白质(Dumas,ICC标准#167)和脂肪酸分布(AOAC#996.0)。通过使用具有单反反射金刚石ATR晶体的便携式红外系统收集光谱,并通过类别类别类别的软独立模型和局部最小二乘回归分析。正宗的谷物面粉形成了不同的群集和来自秘鲁市场的商业样本表现出掺假的患病率。由于淀粉结构的差异,光谱差异与指纹区域(1100-900cm(-1))相关联。产生回归模型以快速测定脂肪(RCV = 0.98,SECV = 0.3)和蛋白质(RCV = 0.98,SECV = 0.96)水平。新一代便携式红外器件为化学分析提供了一种可行的工具,可允许快速,“现场”,可靠的食品成分认证。 (c)2018年elestvier有限公司保留所有权利。

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