کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8502871 1554055 2018 32 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Identification of ground meat species using near-infrared spectroscopy and class modeling techniques - Aspects of optimization and validation using a one-class classification model
ترجمه فارسی عنوان
شناسایی گونه های گوشتی زمین با استفاده از طیف سنجی نزدیک به مادون قرمز و تکنیک های مدل سازی کلاس - جنبه های بهینه سازی و اعتبارسنجی با استفاده از یک مدل طبقه بندی یک طبقه
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک دانش تغذیه
چکیده انگلیسی
Chemometric methods permit the construction of classifiers that effectively assist in monitoring safety, quality and authenticity of meat based on the near-infrared (NIR) spectral fingerprints. Discriminant techniques are often considered in multivariate quality control. However, when the authenticity of meat products is the primary concern, they often lead to an incorrect recognition of new samples. The performances of two class modeling techniques (CMT) in order to recognize meat sample species based on their NIR spectra was compared - a one-class classifier variant of the partial least squares method (OCPLS) and the soft independent modeling of class analogy (SIMCA). Based on obtained sensitivity and specificity values, OCPLS and SIMCA can be considered as an effective CMT for the classification of complex natural samples such as studied meat samples (with a relatively large variability). Moreover, particular attention was paid to the optimization and validation of a one-class classification model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Meat Science - Volume 139, May 2018, Pages 15-24
نویسندگان
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