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هشتمین سمینار دوسالانه کمومتریکس ایران
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
An inexpensive analytical method based on colorimetric sensor array and chemometrics data analysis techniques for fraud detection in cherry seed oil samples |
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| چکیده انگلیسی مقاله |
ABSTRACT: Cherry seed oil is a high expensive and essential ingredient in cosmetics industries. due to the importance of this oil in cosmetic industries, there is an increasing potential for fraud in this oil products. one type of these adulterations including the replacement of this expensive oil with cheaper substitutes, could potentially be very lucrative for a vendor or raw material supplier [1,2]. Colorimetric sensor arrays(CSA) are example of simple and cost-effective analytical devices that have been developed by suslick et al. in 2000[3]. These methods have been used in very different quality control assessments. Multivariate statistical techniques have been used for extraction of useful information from colorimetric sensor array patterns. Among this chemometrics techniques, pattern recognition and partial least square (PLS) regression methods have been found widespread application in sensor arrays [4]. In this work, we developed a cross-responsive colorimetric sensor array for determination of amount of adulteration in cherry seed oil, the sensor array was constructed from pH and redox indicators, these sensors were exposed to the vapor of oil samples and the difference in the color intensity of them was considered as a corresponding signal. the responses of the sensors were dependent on the amounts of canola oil, sunflower oil and sesame oil, as counterfeit that added to the cherry seed oil samples. Principal component analysis (PCA) as an unsupervised pattern recognition techniques was used for discrimination of different adulteration amount in the cherry seed oil samples, fitting accuracies of 95%, 86% and 82% were obtained for canola, sunflower and sesame oils and partial least square (PLS) regression as a multivariate calibration method was used to estimate the content of canola, sunflower and sesame oils in cherry seed oil samples through image analysis. A root mean square error for calibration of 0.31 8 for canola oil, 3.34 for sunflower oil and also 1.37 for sesame oil were obtained, respectively. This colorimetric sensor array demonstrates excellent potential for qualitative and quantitative control of cherry seed oil samples. |
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| کلیدواژه‌های انگلیسی مقاله |
Keywords، Colorimetric sensor array، Cherry seed oil، Partial least squares (PLS) regression، Chemometrics، Multivariate statistical techniques، Principal component analysis(PCA). |
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| نویسندگان مقاله |
| مهسا چهارلنگی
دانشگاه شیراز
| جواد تشخوریان
دانشگاه شیراز
| بهرام همتی نژاد
دانشگاه شیراز
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| نشانی اینترنتی |
http://chemo2021.modares.ac.ir/browse.php?a_code=A-10-155-1&slc_lang=fa&sid=1 |
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| زبان مقاله منتشر شده |
fa |
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