Fuzzy models to predict consumer ratings for biscuits based on digital image features

Davidson, Valerie; Ryks, Joanne; Chu, Thi Huong

doi:10.1109/91.917115

Cited by 44 publications

(19 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm procedure was able to classify muffins at an accuracy level better than 88% in most cases whereas quality decisions among inspectors varied by 20 to 30% (Abdullah et al 2000). Davidson et al (2001) measured the physical features of chocolate chip biscuits, including size, shape baked dough colour, and fraction of top surface area that was chocolate chip using image analysis. Four fuzzy models were developed to predict consumer ratings based on three of the features.…”

Section: Discussionmentioning

confidence: 99%

Colour change of bakery products influenced by used additions

et al. 2014

View full text Add to dashboard Cite

This study deals with the effects of selected additions of vegetable origin on the colour of whole grain breads. The colour was assessed using model samples which were made of mixtures containing various wholemeal flour types (wheat, spelt, and rye flour) and increasing amounts of additions in the form of buckwheat, oat, and barley flour. The additions were 10, 20, 30, 40 and 50 per cent. Colour measurement was performed instrumentally, using an image analysis method which was modified for the purposes of this study. It was found out that, regardless of the flour/addition ratio, both factors in the form of wheat, spelt, and rye wholemeal flour, and barley, oat and buckwheat flour additions and their interactions exhibited a significant influence on the colour of the bakery products (P < 0.05). As far as dependence of colour on the flour/addition ratio is concerned, significant differences (P < 0.05) were found for the following combinations: mixture of wheat flour with buckwheat, barley, and oat; mixture of spelt flour with buckwheat and oat; and mixture of rye flour with buckwheat and barley. The proposed general regression model which was created using the data obtained in the experiment, showed colour variability of more than 95 per cent.

show abstract

Section: Discussionmentioning

confidence: 99%

Colour change of bakery products influenced by used additions

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Works are more especially focused on the power of fuzzy logic to represent the semantics of human assessment in the field of sensory analysis. For example, Davidson et al [20] suggests a linguistic format for the sensory assessment of foods as well as processing methods to analyse taste panel opinions within the framework of the fuzzy set theory. Tan et al [82] describes an initial attempt to examine fuzzy formalism for sensory analysis and demonstrates how fuzzy sets may lead to a natural way of interpreting sensory data.…”

Section: Representation Of Descriptive Sensory Evaluation Performed Bmentioning

confidence: 99%

“…The fuzzy mathematical tool used can also be a fuzzy rule basis. The classical Mamdani [51] format and Takagi-Sugeno [81] formats are often handled, as in [20,23,31,76]. For example, in Harris [31], different types of milk are classified on the basis of two biochemical measurements and two microbiological measurements.…”

Section: Indirect Measurement or Estimation Of Foodstuff Qualitymentioning

confidence: 99%

Fuzzy concepts applied to food product quality control: A review

Perrot

Ioannou²,

Allais

et al. 2006

Fuzzy Sets and Systems

View full text Add to dashboard Cite

“…Davidson et al (2001) measured the physical features of chocolate chip biscuits, including size, shape baked dough colour, and fraction of top surface area that was chocolate chip using image analysis. Four fuzzy models were developed to predict consumer ratings based on three of the features.…”

Section: Processed Food Productsmentioning

confidence: 99%

Machine vision system: a tool for quality inspection of food and agricultural products

et al. 2011

View full text Add to dashboard Cite

Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.

show abstract

Fuzzy models to predict consumer ratings for biscuits based on digital image features

Cited by 44 publications

References 5 publications

Colour change of bakery products influenced by used additions

Colour change of bakery products influenced by used additions

Fuzzy concepts applied to food product quality control: A review

Machine vision system: a tool for quality inspection of food and agricultural products

Contact Info

Product

Resources

About