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بررسی اثر پیشپردازشهای مختلف طیفی بر ارزیابی غیر مخرب کیفیت پرتقال با اسپکتروفتومتری فروسرخ نزدیک (NIRS) | |||||||
| تحقیقات مهندسی صنایع غذایی | |||||||
| مقاله 3، دوره 15، شماره 2، شهریور 1393، صفحه 27-44 اصل مقاله (440.58 K) | |||||||
| نوع مقاله: مقاله پژوهشی | |||||||
| شناسه دیجیتال (DOI): 10.22092/jaer.2014.100188 | |||||||
| نویسندگان | |||||||
| بهاره جمشیدی1؛ سعید مینایی* 2؛ عزالدین مهاجرانی3؛ حسن قاسمیان4 | |||||||
| 1دانشآموخته دکتری تخصصی دانشگاه تربیت مدرس و استادیار موسسه تحقیقات فنی و مهندسی کشاورزی | |||||||
| 2استاد دانشکده کشاورزی دانشگاه تربیت مدرس | |||||||
| 3استاد پژوهشکده لیزر و پلاسما دانشگاه شهید بهشتی | |||||||
| 4استاد دانشکده مهندسی برق و کامپیوتر دانشگاه تربیت مدرس | |||||||
| چکیده | |||||||
| اسپکتروسکوپی فروسرخ نزدیک (NIR) در ترکیب با روشهای شیمیسنجی، شامل پیشپردازشهای طیفی و مدلسازیهای چندمتغیره، یکی از پرکاربردترین روشهای غیر مخرب اندازهگیری ویژگیهای کیفی میوهها و سبزیهاست که در سالهای اخیر بیشتر مد نظر پژوهشگران بوده است. در این پژوهش، توانایی روش اسپکتروسکوپی NIR بازتابی در محدوده طیفی 1650-930 نانومتر بهمنظور پیشبینی غیر مخرب مواد جامد حلشدنی (SSC) و اسیدیته قابل تیتر کردن (TA) پرتقال تامسون، همچنین اثر پیشپردازشهای مختلف طیفی بر دقت مدلهای چندمتغیره پیشبینیکننده بررسی شد. در این راستا، مدلهای واسنجی چندمتغیره حداقل مربعات جزئی (PLS) بر پایه اندازهگیریهای مرجع و اطلاعات طیفهای پیشپردازش شده با ترکیب روشهای مختلف هموارسازی (میانگینگیری متحرک (MA)، ساویتزکی- گولای (SG)، تبدیل موجک (WT))؛ نرمالسازی (تصحیح پراکنش افزاینده (MSC)، توزیع نرمال استاندارد (SNV))؛ و افزایش قدرت تفکیک طیفی (مشتقهای اول و دوم (D1، D2)) برای پیشبینی SSC و TA پرتقالها تدوین شدند. نتایج نشان داد که اسپکتروسکوپی NIR بازتابی، در ترکیب با روشهای شیمیسنجی، توانایی پیشبینی غیر مخرب SSC و TA پرتقال را دارد. همچنین، روشهای پیشپردازش اثر مستقیم بر نتایج مدلهای PLS تدوین شده برای پیشبینی این پارامترهای درونی داشتند و بهترین نتایج پیشبینی برای SSC(430/0RMSEC=، 923/0rc=، 451/0RMSEP=، 936/0rp=، 798/2SDR=) و TA(133/0RMSEC=، 883/0rc=، 177/0RMSEP=، 863/0rp=، 853/1SDR=) بر پایه ترکیب روشهای پیشپردازش MA + SNV بهدست آمد. | |||||||
| عنوان مقاله [English] | |||||||
| Effect of Spectral Pre-Processing Methods on Non-Destructive Quality Assessment of Oranges Using NIRS | |||||||
| چکیده [English] | |||||||
| Near-infrared spectroscopy (NIRS) combined with chemometric methods of spectral pre-processing and multivariate modeling is popular non-destructive method for measuring quality attributes of fruits and vegetables. The present study investigated the feasibility of reflectance NIRS in a spectral range of 930-1650 nm for non-destructive prediction of soluble solids content (SSC) and titratable acidity (TA) in Thomson oranges. The effect of spectral pre-processing methods on the accuracy of multivariate predictor models was also assessed. Partial least squares (PLS) multivariate calibration models were developed using the reference measurements and pre-processed spectra. The following methods were used: smoothing (moving average (MA), Savitzky-Golay (SG), wavelet transform (WT)); normalizing (multiplicative scatter correction (MSC), standard normal variate (SNV)); and increasing the spectral resolution (the first and second derivatives (D1, D2)) to predict the SSC and TA of oranges. The results indicate that reflectance NIRS plus chemometrics gives the potential for non-destructive prediction of SSC and TA in oranges. Pre-processing methods directly affected the results of the PLS models. The best prediction results for SSC (RMSEC = 0.430, rc = 0.923, RMSEP = 0.451, rp = 0.936, SDR = 2.798) and TA (RMSEC = 0.133, rc = 0.883, RMSEP = 0.177, rp = 0.863, SDR = 1.853) were achieved based on a combination of the MA + SNV pre-processing methods. | |||||||
| کلیدواژهها [English] | |||||||
| multivariate analysis, Near-infrared spectroscopy, Non-destructive, Pre-processing methods | |||||||
| مراجع | |||||||
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