کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
5132402 1492048 2018 6 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy
ترجمه فارسی عنوان
طبقه بندی سریع فیله های سینه مرغ نابجا با پیش بینی مولفه های اصلی مولفه های کیفیت با طیف سنجی قابل مشاهده / نزدیک به مادون قرمز
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی


- Five quality traits were dimensionally reduced and were presented as new PCs by PCA.
- A feasible chicken quality grades classification method by PC1 score was proposed.
- The PC1 score value of each fillet could be rapidly predicted using Vis/NIRS.
- The predicted PC1 score values could be classified into 3 quality grades.

In this study visible/near-infrared spectroscopy (Vis/NIRS) was evaluated to rapidly classify intact chicken breast fillets. Five principal components (PC) were extracted from reference quality traits (L∗, pH, drip loss, expressible fluid, and salt-induced water gain). A quality grades classification method by PC1 score was proposed. With this method, 150 chicken fillets were properly classified into three quality grades, i.e., DFD (dark, firm and dry), normal, and PSE (pale, soft and exudative). Furthermore, PC1 score could be predicted using partial least squares regression (PLSR) model based on Vis/NIRS (R2p = 0.78, RPD = 1.9), without the measurement of any quality traits. Thresholds of PC1 classification method were applied to classify the predicted PC1 score values of each fillet into three quality grades. The classification accuracy of calibration and prediction set were 85% and 80%, respectively. Results revealed that PC1 score classification method is feasible, and with Vis/NIRS, this method could be rapidly implemented.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Chemistry - Volume 244, 1 April 2018, Pages 184-189
نویسندگان
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