首页> 外文期刊>Journal of Food Composition and Analysis >Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy.
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Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy.

机译:使用拉曼光谱法通过高果糖玉米糖浆和麦芽糖糖浆检测蜂蜜掺假。

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Raman spectroscopy was used to detect adulterants such as high fructose corn syrup (HFCS) and maltose syrup (MS) in honey. HFCS and MS were each mixed with authentic honey samples in the following ratios: 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w). Adaptive iteratively reweighted penalized least squares (airPLS) was chosen to remove background of spectral data. Partial least squares-linear discriminant analysis (PLS-LDA) was used to develop a binary classification model. Classification of honey authenticity using PLS-LDA showed a total accuracy of 91.1% (authentic honey vs. adulterated honey with HFCS), 97.8% (authentic honey vs. adulterated honey with MS) and 75.6% (authentic honey vs. adulterated honey with HFCS and MS), respectively. Classification of honey adulterants (e.g. HFCS or MS) using PLS-LDA gave a total accuracy of 84.4%. The results showed that Raman spectroscopy combined with PLS-LDA was a potential technique for detecting adulterants in honey.
机译:拉曼光谱用于检测掺假品,例如蜂蜜中的高果糖玉米糖浆(HFCS)和麦芽糖糖浆(MS)。 HFCS和MS分别以以下比例与纯正的蜂蜜样品混合:1:10(10%),1:5(20%)和1:2.5(40%,w / w)。选择自适应迭代加权加权最小二乘(airPLS)来去除光谱数据的背景。偏最小二乘线性判别分析(PLS-LDA)用于建立二元分类模型。使用PLS-LDA进行的蜂蜜真伪分类显示,总准确度分别为91.1%(真蜂蜜与掺假蜂蜜与HFCS),97.8%(真蜂蜜与掺假蜂蜜与MS)和75.6%(真蜂蜜与掺假蜂蜜与HFCS)和MS)。使用PLS-LDA对蜂蜜掺杂物(例如HFCS或MS)进行分类的总准确度为84.4%。结果表明,拉曼光谱法结合PLS-LDA是检测蜂蜜中掺假物的一种潜在技术。

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