کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7597345 1492130 2014 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods
ترجمه فارسی عنوان
تجزیه و تحلیل پیش بینی کننده کیفیت آبجو با همبستگی ارزیابی حسی با تولید الکل و استر بالاتر با استفاده از روش های آمار چند متغیره
کلمات کلیدی
ارزیابی حسی ماشین بردار پشتیبانی، شبکه های عصبی مصنوعی، حداقل مربعات جزئی، کیفیت آبجو،
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Chemistry - Volume 161, 15 October 2014, Pages 376-382
نویسندگان
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