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Sensory characterization and acceptance of coffee brews of C. arabica and C. canephora blended with steamed defective coffee.

Food research international (Ottawa, Ont.)
Steam treatment has been reported as an alternative to improve the cup quality of coffee; in this research, it was applied to C. canephora defective coffee. The aim of the study was to evaluate the sensory perception of steamed defective C. canephora...

Study on Recognition of Cooking Oil Fume by Fourier Transform Infrared Spectroscopy Based on Artificial Neural Network.

Guang pu xue yu guang pu fen xi = Guang pu
With the developing of catering trade, cooking oil fume has became one of the three major air pollution sources in some cities. In recent years, a lot of research on the cooking oil fume have been done for its high threaten to human health. The cooki...

Optimization of goose breast meat tenderness by rapid ultrasound treatment using response surface methodology and artificial neural network.

Animal science journal = Nihon chikusan Gakkaiho
The aim of this study was to develop a prediction model on tenderization of goose breast meat by response surface methodology (RSM) and artificial neural network (ANN). The experiments were operated on the basis of a three-level, three-variable (ultr...

FoodBase corpus: a new resource of annotated food entities.

Database : the journal of biological databases and curation
The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora re...

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses.

Food research international (Ottawa, Ont.)
There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described...

Machine-learned modeling of PM exposures in rural Lao PDR.

The Science of the total environment
This study presents a machine-learning-enhanced method of modeling PM personal exposures in a data-scarce, rural, solid fuel use context. Data collected during a cookstove (Africa Clean Energy (ACE)-1 solar-battery-powered stove) intervention program...

A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composi...

Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughpu...

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network.

Journal of oleo science
Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total p...