Design of experiments and regression modelling in food flavour and sensory analysis: A review

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

doi:10.1016/j.tifs.2017.11.013

Cited by 135 publications

(68 citation statements)

References 108 publications

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“…In this the mean case sum of squares of error (MS E ) can be calculated because degrees of freedom are different than zero (2 k (n-1) = 2 3 (2-1) = 8). ANOVA result for 2 3 factorial design with two replicates is shown in Table 10. Regression model that represent predicted/fitted value for flash formation is given below, and includes only significant factor and interactions.…”

Section: 78mentioning

confidence: 99%

“…The usage of DOE has been expanded across many industries as part of decision making process either along a new product development, manufacturing process and improvement. It is not used only in engineering areas it has been used in administration, marketing, hospitals, pharmaceutical [2], food industry [3], energy and architecture [4] [5], and chromatography [6]. DOE is applicable to physical processes as well as computer simulation models [7].…”

Section: Introductionmentioning

confidence: 99%

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Design of experiments application, concepts, examples: State of the art

Duraković

2017

PEN

202

144

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Design of Experiments (DOE) is statistical tool deployed in various types of system, process and product design, development and optimization. It is multipurpose tool that can be used in various situations such as design for comparisons, variable screening, transfer function identification, optimization and robust design. This paper explores historical aspects of DOE and provides state of the art of its application, guides researchers how to conceptualize, plan and conduct experiments, and how to analyze and interpret data including examples. In addition, this paper reveals that in past 20 years application of DOE have been growing rapidly in manufacturing as well as nonmanufacturing industries. It was most popular tool in scientific areas of medicine, engineering, biochemistry, physics, computer science and counts about 50% of its applications compared to all other scientific areas. Keyword:Design of Experiments, full factorial design; fractional factorial design; product design; quality improvement;

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Section: 78mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of experiments application, concepts, examples: State of the art

Duraković

2017

PEN

202

144

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“…Four milk knots were evaluated in sessions, with an enforced 2‐min rest between samples. Replications were evaluated on different days (Yu, Low, & Zhou, 2018).…”

Section: Methodsmentioning

confidence: 99%

Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP

Wei

Zhan

et al. 2020

Food Science & Nutrition

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Self-made milk knots in Xinjiang Kazakh ethnic group were used as material to establish the quality assessment system of flavor quality. The fuzzy analytic hierarchy process based on the optimal consistency matrix was used to evaluate the quality of the samples qualitatively and quantitatively. Its result is consistent with the cluster analysis of the SOM neural network. The results showed that the milk knot samples of Altay had differences with the milk knot samples of Yili. The comprehensive evaluation system is feasible and can evaluate the quality of milk knot samples by flavor characteristics. This can provide a reference for further research on the origin of differences between two types of milk knot samples. K E Y W O R D Sfuzzy analytic hierarchy process, sensory evaluation, SOM neural network, Xinjiang traditional milk knots

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“…Although NIR HSI has not been extensively implemented in industry, it shows great potential for the development of an evaluation system to assess cereal grains, especially regarding variety discrimination and grading/classification properties [23][24][25][26].…”

Section: Milk and Milk Productsmentioning

confidence: 99%

A Short Update on the Advantages, Applications and Limitations of Hyperspectral and Chemical Imaging in Food Authentication

et al. 2018

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Around the world, the food industry needs to maintain high quality and safety standards in order to satisfy consumers demand for healthy foods and to trace the origin of raw materials and products that are used during food manufacture. These objectives can be achieved by applying analytical methods and techniques that are able to provide information about composition, structure, physicochemical properties, and sensory characteristics of foods. Modern techniques and methods based on spectroscopy (near infrared (NIR), mid infrared (MIR), Raman) are highly desirable due to their low cost and easy to implement, and often requiring minimal sample preparation. This paper reviews some of the advantages and recent applications of hyperspectral and chemical imaging to discriminate and authenticate foods.

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Design of experiments and regression modelling in food flavour and sensory analysis: A review

Cited by 135 publications

References 108 publications

Design of experiments application, concepts, examples: State of the art

Design of experiments application, concepts, examples: State of the art

Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP

A Short Update on the Advantages, Applications and Limitations of Hyperspectral and Chemical Imaging in Food Authentication

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