کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7585184 | 1492035 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Non-targeted detection of paprika adulteration using mid-infrared spectroscopy and one-class classification - Is it data preprocessing that makes the performance?
ترجمه فارسی عنوان
تشخیص غیر هدفمند تقلب پاپریکا با استفاده از طیف سنجی نیمه مادون قرمز و طبقه بندی یک طبقه - آیا پیش پردازش داده ای است که باعث عملکرد آن می شود؟
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کلمات کلیدی
اصالت غذایی، تشخیص تقلید، تست انحنای عمومی، روش غربالگری، پیش پردازش اطلاعات، مدل سازی یک کلاس،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
A method for the non-targeted detection of paprika adulteration was developed using Fourier transform mid-infrared (FT-MIR) spectroscopy and one-class soft independent modelling of class analogy (OCSIMCA). One-class models based on commercially available paprika powders were developed and optimised to provide >80% sensitivity by external validation. The performances of the established models for adulteration detection were tested by predicting spiked paprika samples with various types of fraudulent material and levels of adulterations including 1% (w/w) Sudan I, 1% (w/w) Sudan IV, 3% (w/w) lead chromate, 3% (w/w) lead oxide, 5% (w/w) silicon dioxide, 10% (w/w) polyvinyl chloride, and 10% (w/w) gum arabic. Further, the influence of data preprocessing on the model performance was investigated. Relationship between classification results and data preprocessing was identified and specificity >80% was achieved for all adulterants by applying different preprocessing methods including standard normal variate (SNV), first and second derivatives, smoothing, and combinations thereof.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Chemistry - Volume 257, 15 August 2018, Pages 112-119
Journal: Food Chemistry - Volume 257, 15 August 2018, Pages 112-119
نویسندگان
Bettina Horn, Susanne Esslinger, Michael Pfister, Carsten Fauhl-Hassek, Janet Riedl,