تجزیه و تحلیل تقلبات در ادویه دارچین با استفاده از طیف‌سنجی: تأثیر پیش‌پردازش داده‌ها بر مدل‌های پیش‌بینی چندمتغیره

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی بیوسیستم، دانشگاه فردوسی مشهد

2 عضو هیات علمی گروه مهندسی بیوسیستم دانشگاه فردوسی مشهد

3 دانشکده علوم کامپیوتر و فناوری اطلاعات، دانشگاه مرداک، پرت، استرالیا

چکیده

تشخیص تقلب در زنجیره تأمین دارچین برای تضمین ایمنی مصرف‌کننده و حفظ یکپارچگی محصول، امری حیاتی است. پیشرفت‌های اخیر در تکنیک‌های پیش‌پردازش داده‌های طیفی، دقت تشخیص ناخالصی‌ها در ادویه‌هایی مانند دارچین را بهبود بخشیده‌اند. این مطالعه به بررسی تأثیر تکنیک‌های مختلف پیش‌پردازش طیفی بر پیش‌بینی ناخالصی‌های مخلوط‌شده با پودر دارچین می‌پردازد—به‌طور مشخص پودر سویا، پودر پوست فندق، و پودر نان خشک—که از ترکیب طیف‌سنجی و تحلیل چندمتغیره بهره می‌برد.طیف‌های عبوری در محدوده فروسرخ میانی (400 تا 4000 سانتی‌متر⁻¹) جمع‌آوری شدند و رگرسیون حداقل مربعات جزئی (PLSR) برای مدل‌سازی سطوح تقلب بر اساس این طیف‌ها به کار گرفته شد. برای بهینه‌سازی داده‌های طیفی، روش‌های پیش‌پردازش مختلفی اعمال گردید. در این میان، تصحیح سیگنال متعامد (OSC) در ترکیب با روندزدایی (detrending) بالاترین دقت پیش‌بینی را نشان داد، با ضریب پیش‌بینی (R²p) در بازه 0.900 تا 0.981. در مقابل، روش‌های تصحیح پراکندگی ضربی توسعه‌یافته (EMSC) و مشتق دوم ساویتزکی-گولای (D2) عملکرد ضعیف‌تری داشتند و R²p آن‌ها بین 0.115 تا 0.931 متغیر بود. در میان ناخالصی‌ها، پودر سویا آسان‌ترین ماده برای شناسایی بود و دامنه خطای پیش‌بینی آن بین ۵ تا ۱۰ درصد قرار داشت. این یافته‌ها بر اهمیت انتخاب تکنیک پیش‌پردازش مناسب برای بهبود دقت تشخیص تقلب در پودر دارچین با استفاده از روش‌های طیف‌سنجی تأکید می‌کنند.

چکیده تصویری

تجزیه و تحلیل تقلبات در ادویه دارچین با استفاده از طیف‌سنجی: تأثیر پیش‌پردازش داده‌ها بر مدل‌های پیش‌بینی چندمتغیره

تازه های تحقیق

  • تأثیر روش‌های مختلف پیش‌پردازش بر طیف‌های FTIR بررسی شد.
  • دقت پیش‌بینی PLSR با استفاده از روش‌های مختلف پیش‌پردازش ارزیابی شد.
  • روش‌های پیش‌پردازش می‌توانند عملکرد مدل PLSR را بهبود بخشیده یا مانع آن شوند. 
  • یک رویکرد روش‌شناختی برای پیش‌پردازش طیف‌های FTIR ادویه دارچین پیشنهاد شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analyzing Cinnamon Spice Adulteration with Spectroscopy: The Influence of Data Preprocessing on Multivariate Prediction Models

نویسندگان [English]

  • Mohammad Masoudi 1
  • Rasool Khodabakhshian 2
  • Mahmood Reza Golzarian 3
1 Department of Biosystem Engineering, Ferdowsi University of Mashhad
2 Assistant Professor Department of Biosystems Engineering Ferdowsi University of Mashhad
3 School of Computer Science and Information Technology, Murdoch University, Perth, Australia
چکیده [English]

Detecting fraud in the cinnamon supply chain is critical for ensuring consumer safety and maintaining product integrity. Recent advances in spectral data preprocessing techniques offer enhanced accuracy in identifying adulterants in spices like cinnamon. This study investigates the impact of different spectral preprocessing techniques on predicting adulterants—specifically soybean powder, hazelnut shell powder, and dry bread powder—mixed with cinnamon powder using spectroscopy combined with multivariate analysis. The transmittance spectra were collected across the mid-infrared range of 400–4000 cm⁻¹, and Partial Least Squares Regression (PLSR) was employed to model the adulteration levels based on these spectra. Various preprocessing methods were applied to optimize the spectral data. Among them, orthogonal signal correction (OSC) combined with detrending yielded the highest predictive accuracy, with a coefficient of prediction (R²p) ranging from 0.900 to 0.981. Conversely, Extended Multiplicative Scatter Correction (EMSC) and Savitzky-Golay second derivative (D2) were less effective, with R²p values between 0.115 and 0.931. Soybean powder was the easiest adulterant to detect, with a prediction error range of 5–10%. These findings underscore the importance of selecting appropriate preprocessing techniques to improve the accuracy of fraud detection in cinnamon powder using spectroscopic methods.

کلیدواژه‌ها [English]

  • Adulterants
  • Chemometrics
  • Data Preprocessing
  • Food Safety
  • Spectral Analysis
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دوره 13، شماره 1
آبان 1404
صفحه 1-12
  • تاریخ دریافت: 12 تیر 1404
  • تاریخ بازنگری: 12 مرداد 1404
  • تاریخ پذیرش: 02 شهریور 1404
  • تاریخ اولین انتشار: 02 شهریور 1404
  • تاریخ انتشار: 01 آبان 1404