تخمین پارامترهای کیفی لیموشیرین (Citrus limetta) با استفاده از روش غیرمخرب طیف‌سنجی Vis/SWNIR

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، دانشگاه صنعتی اصفهان

2 عضو هیأت علمی

3 عضو هیأت علمی، دانشگاه صنعتی اصفهان.

4 عضو هیأت علمی، دانشگاه کاشان

چکیده

مؤلفه‌های مختلفی برای تعیین رسیدگی و کیفیت لیموشیرین معرفی شده‌اند که از مهمترین آن‌ها می‌توان به مقدار مواد جامد محلول (SSC)، اسیدیته قابل تیتر (TA) و رطوبت (MC) اشاره کرد. در این تحقیق، با استفاده از روش غیرمخرب طیف‌سنجی مرئی-فروسرخ نزدیک طول موج کوتاه (Vis/SWNIR)، تخمین پارامترهای SSC، MC و TA لیموشیرین مورد مطالعه قرار گرفت. طیف 120 نمونه لیموشیرین در محدوده‌ی طول موج 400 تا 1100 نانومتر در دو وضعیت تقابلی و عبوری جمع‌آوری شدند. اثر پیش پردازش‌های مختلف نظیر الگوریتم ساویتسکی-گولای (SG)، تصحیح پراکنش افزاینده (MSC)، تصحیح خط پایه (BC)، متغیر نرمال استاندارد (SNV)، مشتق اول (1st Derivatives) و ترکیبی از این روش‌ها مورد بررسی و پیش پردازش مناسب برای مدل‌سازی با روش حداقل مربعات نسبی (PLS) انتخاب شد. براساس نتایج بدست آمده، مدل‌های تخمین SSC و MC در مد تقابلی و TA در مد عبوری منجر به بهترین نتایج شدند. همچنین در بین سه پارامتر مورد بررسی، بهترین نتایج به ترتیب برای SSC، MC و TA حاصل شد. SSC با ضریب همبستگی (rp) 87/0 و ریشه میانگین مربعات خطای پیشگویی (RMSEP) 5/0 درجه بریکس، MC با rp برابر 88/0 و RMSEP برابر 57/0 درصد و TA نیز با rp برابر 74/0 و RMSEP برابر 0076/0 درصد پیشگویی شد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Non-destructive prediction of quality parameters of sweet lemon (Citrus limetta) by Vis/SWNIR spectroscopy

نویسندگان [English]

  • Shahram Moomkesh 1
  • Seyed Ahmad Mireei 2
  • Morteza Sadeghi 3
  • Majid Nazeri 4
1 M.Sc. student, Isfahan University of Technology
2 Faculty member
3 Faculty member, Isfahan University of Technology
4 Faculty member, University of Kashan
چکیده [English]

In this study, the potential of visible and short-wavelength near infrared spectroscopy for nondestructive predicting the SSC, MC and TA of sweet lemon was evaluated. The spectra of 120 sweet lemon samples were acquired in the interactance and transmission modes and the wavelength region of 400 to 1100 nm. Different preprocessing methods, including Savitzky-Golay, multiplicative scatter correction, baseline correction, standard normal variate, 1st derivative, and the combination of these methods were applied to the raw spectra. The most appropriate preprocessing methods were then selected for building the predictive models using partial least squares method. The results showed that the best SSC and MC predictive models were achieved in the interactance mode, while the best TA predictive model was obtained in the transmission mode. Among three quality parameters, the best models were resulted in the prediction of SSC, MC, and TA, respectively. The SSC with a correlation coefficient (rp) of 0.87 and a root mean squares error of prediction (RMSEP) of 0.5 °Brix, MC with a rp of 0.88 and an RMSEP of 0.57%, and TA with a rp of 0.74 and an RMSEP of 0.0076% could be predicted.

کلیدواژه‌ها [English]

  • "Correlation coefficient"
  • "interactance"
  • "partial least squares"
  • "preprocessing"
  • "transmission"
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