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ارزیابی غیرمخرب رسیدگی میوه انجیر با استفاده از طیف‌سنجی فروسرخ نزدیک موج کوتاه/مرئی | ||
| تحقیقات مهندسی صنایع غذایی | ||
| مقاله 8، دوره 21، شماره 1 - شماره پیاپی 72، مرداد 1401، صفحه 109-122 اصل مقاله (973.3 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/fooder.2021.353402.1293 | ||
| نویسندگان | ||
| رضا صیاد حق شمار1؛ رضا محمدی گل* 2؛ بابک وکیل زاده کاجی3 | ||
| 1دانشجوی کارشناسی ارشد گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، شهر اراک، ایران | ||
| 2گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران. | ||
| 3گروه مهندسی باغبانی، دانشکده کشاورزی، دانشگاه اراک، اراک، ایران | ||
| چکیده | ||
| در تحقیق حاضر، طیف‌سنجی بازتابی مرئی/ فروسرخ نزدیک موج کوتاه (Vis/SWNIR) در محدوده 950-425 نانومتر به‌منظور پیش‌بینی شاخص مزه و سفتی بافت میوه انجیر بررسی شد. علاوه بر این، عملکرد طبقه‌بندهای LDA و QDA در تفکیک انجیرهای رسیده، نیمه‌رسیده و نرسیده بر پایه ترکیب روش‌های مختلف پیش‌پردازش طیفی بررسی گردید. تعداد 167 میوه انجیر پس از حذف داده‌های پرت برای تدوین و اعتبارسنجی مدل‌ها انتخاب شدند. از تکنیک تحلیل مؤلفه‌های اصلی برای استخراج مؤلفه‌های اصلی طیف‌ها استفاده شد. عملکرد مدل حداقل مربعات جزئی (PLS) و روش‌های رایج پیش‌پردازش داده‌های طیفی با شاخص‌های: انحراف پیش‌بینی باقیمانده (RPD)، ضریب همبستگی تخمین (rp) و ریشه میانگین مربعات خطای پیش‌بینی (RMSEP) ارزیابی شد. کارآمدی طبقه‌بندها به همراه روش‌های پیش‌پردازش نیز با درصد دقت تفکیک درست دسته نمونه‌های آزمون اعتبارسنجی شد. بیشترین شاخص RPD در پیش‌بینی سفتی بافت و شاخص مزه به ترتیب برابر 1.79 (0.845=rp و 1.64= RMSEP) و0.89 (0.215=rp و 10.90= RMSEP) در پیش‌پردازش ترکیبی میانگین‌گیری متحرک (MA) و De-trending به دست آمد. دقت تفکیک درست طبقه‌بندهای LDA و QDA نیز برابر 93.33 درصد (بدون پیش‌پردازش طیف‌ها) به دست آمد. | ||
| کلیدواژه‌ها | ||
| رسیدگی انجیر؛ حداقل مربعات جزئی؛ طیف‌سنجی مرئی/ فروسرخ نزدیک موج‌کوتاه؛ طبقه‌بندی؛ غیرمخرب | ||
| عنوان مقاله [English] | ||
| Nondestructive Evaluation of Fig Fruit Maturity Using Visible / Short-Wave Near-Infrared Spectroscopy | ||
| نویسندگان [English] | ||
| Reza Saiad Haghshomar1؛ Reza Mohammadigol2؛ Babak Valizadehkaji3 | ||
| 1Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran | ||
| 2Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran | ||
| 3Assistant professor, Department of Biosystem Engineering, Agriculture College, Arak University, Arak, Iran. | ||
| چکیده [English] | ||
| In the present study, visible/shortwave near-infrared reflectance spectroscopy (Vis/SWNIR, 425–950 nm) was used to predict the taste index (SSC/TA) and flesh firmness of fig fruits. Besides, the efficiency of LDA and QDA classifiers in detecting ripe, semi-ripe, and unripe figs was studied based on a combination of pretreatment methods. A total of 167 fig trees were selected for the development and validation of the models. Principal component analysis (PCA) was employed to extract the principal components of the spectra. PLS performance and common spectral data pretreatment methods were evaluated using the residual prediction deviation (RPD), predictive correlation coefficient (rp), and root mean square error of prediction (RMSEP). Moreover, the efficiency of the classifiers and pretreatment methods was evaluated using the mean overall accuracy (%) of the testing samples. The highest mean value of RPDs based on the combined pretreatment method of MA + de-trending was 1.79 for flesh firmness (RMSEP = 1.64, rp = 0.845) and 0.89 for the taste index (RMSEP = 10.09, rp = 0.215).LDA and QDA classifiers had an overall accuracy of 93.33 percent (in no-pretreatment spectral data). | ||
| کلیدواژه‌ها [English] | ||
| Fig ripeness, Partial least squares, Visible/shortwave near-infrared reflectance spectroscopy, Classification, Non-destructive evaluation | ||
| مراجع | ||
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