احراز هویت سریع نانوامولسیون روغن باندنگ (Chanos chanos) با استفاده از طیف‌سنجی مادون قرمز تبدیل فوریه (FTIR) همراه با شیمی‌سنجی

نوع مقاله : مقاله کوتاه انگلیسی

نویسندگان

ماس روکان ماس روکان ¹

فیترا تونیسا ²

عارف نور ایخسان ³

¹ گروه صنایع غذایی، دانشکده علوم و فناوری، دانشگاه ویدیا ماتارام، یوگیاکارتا، اندونزی

² گروه بیوتکنولوژی، دانشکده تحصیلات تکمیلی دانشگاه گادجاه مادا، یوگیاکارتا، اندونزی

³ گروه داروسازی، دانشکده داروسازی، دانشگاه گادجاه مادا، یوگیاکارتا، اندونزی

https://doi.org/10.22067/ifstrj.2026.96524.1514

چکیده

سطح بالای اسید چرب چند غیراشباع (PUFA) در نانوامولسیون روغن باندنگ (BON)، تقلب در آن را با استفاده از روغن‌های بی‌کیفیت مانند روغن پالم (PO) خطرناک می‌کند. احراز هویت BON برای تضمین کیفیت و ایمنی محصول ضروری است. شناسایی نانوامولسیون تقلبی به‌ویژه چالش برانگیز است زیرا ماتریس سورفکتانت و فاز آبی، بو، رنگ و نشانگرهای شیمیایی خاص قابل تشخیص در روغن‌های فله را می‌پوشانند. هدف از این تحقیق، احراز هویت BON با استفاده از طیف‌سنجی FTIR همراه با شیمی‌سنجی است. مدل‌های تقلب با استفاده از روغن پالم به‌عنوان یک ماده تقلبی، روغن باندنگ به‌عنوان یک جزء خالص و ماتریس نانوامولسیون ساخته شدند. همه نمونه‌ها با استفاده از طیف‌سنجی ATR-FTIR در طول موج‌های 4000 تا 650 (cm^⁻¹) تجزیه و تحلیل شدند. تکنیک‌های شیمی‌سنجی، مانند تجزیه و تحلیل مؤلفه‌های اصلی (PCA)، رگرسیون حداقل مربعات جزئی (PLSR) و رگرسیون مؤلفه‌های اصلی (PCR)، برای جداسازی و تعیین کمیت BON از ماده تقلبی انجام شد.PCA  با موفقیت BON را از روغن پالم (PO) به‌عنوان یک ماده تقلبی جدا کرد. بنابراین،PLSR  با استفاده از طیف‌های نرمال در طول موج‌های ۱۵۰۰ تا ۶۵۰ (cm^⁻¹)، بهترین مقدار را بر اساس بالاترین مقدار R₂cal برابر با ۰.۹۵۰۳؛ مقدار R₂pred برابر با ۰.۸۷۶۱؛ کمترین RMSEC برابر با ۰.۱۴۵؛ و RMSEP برابر با ۰.۲۴۵ داشت. می‌توان نتیجه گرفت که طیف‌سنجی FTIR همراه با روش‌های شیمی‌سنجی، روشی سریع، دقیق و مناسب برای تشخیص BON از PO به‌عنوان یک ماده‌ی تقلبی در شکل نانوامولسیون است.

کلیدواژه‌ها

اثر انگشت

تحلیل مؤلفه‌های اصلی

رگرسیون حداقل مربعات جزئی

رگرسیون مؤلفه‌های اصلی

نانوامولسیون روغن ماهی

موضوعات

• فناوری مواد غذایی

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عنوان مقاله English

Rapid Authentication of Bandeng (Chanos chanos) Oil Nanoemulsion Using Fourier Transform Infrared (FTIR) Spectroscopy Combined with Chemometrics

نویسندگان English

Masrukan Masrukan ¹

Fitra Tunnisa ²

Arif Nur Ikhsan ³

¹ Department of Food Technology, Faculty of Science and Technology, Universitas Widya Mataram, Yogyakarta, Indonesia

² Department of Biotechnology, The Graduate School of Universitas Gadjah Mada, Yogyakarta, Indonesia

³ Department of Pharmaceutics, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, Indonesia

چکیده English

A high level of polyunsaturated fatty acid (PUFA) in bandeng oil nanoemulsion (BON) makes it risky to be adulterated using low-quality oils such as palm oil (PO).  Authentication of BON is essential to guarantee product quality and safety. Adulterated nanoemulsion is particularly challenging to identify because the surfactant matrix and aqueous phase mask the characteristic odor, color, and specific chemical markers detectable in bulk oils. This research aims to perform the authentication of BON using FTIR spectroscopy combined with chemometrics. Adulteration models were built using palm oil as an adulterant, bandeng oil as a pure component, and nanoemulsion matrix. All samples were analyzed using ATR-FTIR spectroscopy at 4000 – 650 cm^-1 wavenumbers. Chemometrics technique, such as principal component analysis (PCA), partial least squares regression (PLSR), and principal component regression (PCR), was performed to separate and quantify BON from adulterant. PCA successfully separated BON from palm oil (PO) as an adulterant. Therefore, PLSR using normal spectra at wavenumbers 1500 - 650 cm^-1 had the best value based on highest R²_cal value 0.9503; R²_pred value 0.8761; lowest RMSEC 0.145; RMSEP 0.245. It can be concluded that FTIR spectroscopy combined with chemometrics was claimed as rapid, accurate, and suitable for authenticating BON from PO as an adulterant in nanoemulsion form.

کلیدواژه‌ها English

Fish oil nanoemulsion

Fingerprint

Partial least squares regression

Principal component analysis

Principal component regression

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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