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
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
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
Journal: Food Chemistry - Volume 161, 15 October 2014, Pages 376-382
نویسندگان
Jian-Jun Dong, Qing-Liang Li, Hua Yin, Cheng Zhong, Jun-Guang Hao, Pan-Fei Yang, Yu-Hong Tian, Shi-Ru Jia,