Artificial neural network for determining the hedonic score of texture of and distinguishing different grades of ham sausages

Lin-tao, Zhu; Wu, Wen-Biao

doi:10.1590/fst.31018

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…A 9‐point hedonic scale system was used to evaluate sensory attributes of the cookies and the sensorial procedures reported were followed (Amerine et al, 1965; Hayes et al, 2014; Zhu & Wu, 2020). Total numbers of 300 cookies were tasted by 60 panelists which included 10 females and 10 males being >35–50 years old, 26–35 years old, and 19–25 years old, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Effect of defatted soy and peanut flour obtained by new aqueous method on quality of gluten‐free cookies

Wang

2022

Food Processing Preservation

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Gluten-free cookies (GFCs) are promoted for avoiding gluten-related celiac disease.Requirement of protein/lysine enhancement in rice GFCs encouraged this study.Partly substituting rice flour by defatted peanut (DPF) or soy (DSF) and their mixture (1 + 1) significantly increased protein/lysine content in GFCs. DSF had the most effect of increasing lysine content. Adding DPF or DSF and their mixture significantly improved GFC hardness and hedonic score. The hedonic score of overall acceptability, taste, crispiness, texture/grain, flavor, and appearance/color of all cookies studied were within very-like scale. Adding DPF or DSF or their mixture produced GFCs with deeper/darker color while DSF had the greatest effect. DPF or DSF or their mixture had dosage-dependent effect of increasing GFC antioxidant capacity while DSF had the greatest effect. DPF, DSF, or defatted peanut and soy flour mixture was also good ingredient to make gluten-free and sugar-free cookies sweetened by meso-erythritol blended with stevia glycosides. Practical applicationsThe finding of this study provided scientific data supporting nutritional or other functional fortification by using defatted peanut or soy flours during commercial production of gluten-free cookies. The finding also indicated that new aqueous method can produce defatted peanut or soy flour with good quality suitable for fortifying gluten-free cookies.How to cite this article: Wang, S., & Wu, W. (2022). Effect of defatted soy and peanut flour obtained by new aqueous method on quality of gluten-free cookies.

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Section: Methodsmentioning

confidence: 99%

“…A 9-point hedonic scale system was used to evaluate sensory attributes of the cookies and the sensorial procedures reported were followed (Amerine et al, 1965;Hayes et al, 2014;Zhu & Wu, 2020).…”

Section: Sensory Testmentioning

confidence: 99%

Effect of defatted soy and peanut flour obtained by new aqueous method on quality of gluten‐free cookies

Wang

2022

Food Processing Preservation

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“…In recent years, with the development of spectral detection technologies, hyperspectral imaging has become widely used in modern agricultural production because it offers rapid measurements and high accuracy (Wu et al, 2021;Xiao et al, 2021;Lu et al, 2021;Wei, 2021;Zhu & Wu, 2020;Yikmiş, 2020;Rambo et al, 2020;Cui et al, 2020). Zhang et al used hyperspectral remote sensing to predict the nutrient content of black soil.…”

Section: Introductionmentioning

confidence: 99%

Spectral inversion model of the crushing rate of soybean under mechanized harvesting

Chen

Jin

et al. 2022

Food Sci. Technol

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Rapid and timely acquisition of the crushing rate can help in assessing the performance of combine harvesters, which is very important for agricultural production. The spectral reflectance of soybean provides an alternative method to the classical physical and chemical analysis of the crushing rate of soybean in laboratory. Therefore, hyperspectral imaging can be used to rapidly obtain the crushing rate of soybean. In this study, the hyperspectral method was employed, and the application of inter-correlation analysis was explored in the optimization and quantitative analysis of hyperspectral bands. The crushing rate of 130 soybean samples collected from a combine harvester was investigated through physical analysis in the laboratory. Subsequently, the raw hyperspectral reflectance of soybean samples was measured using a spectroradiometer equipped with a high intensity contact probe under darkroom conditions. Next, the raw spectral reflectance (REF) and the logarithmic reciprocal pretreatment spectrum data (LR) were analyzed and compared. The effective wavelengths were selected according to the results of the inter-correlation analysis. Regression models of the crushing rate with different indices were established using a least squares support vector machine (LS-SVM). The inversion results of the model were validated and compared with each other. The experimental results show that sensitive bands from REF

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“…Some of the recent works include the segmentation method of stacked and glued grain images, 7,8 the feature information extraction of grain images, 9,10 and the grain component recognition model. 11,12 At present, there are few studies on the detection of soybean crushing rate and impurity rate, and the relevant technology has still been immature. 13 However, the research on the crushing rate and impurity rate of wheat and rice based on image processing technology can provide technical reference for the research of this paper.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semantic segmentation-based mechanized harvesting soybean quality detection

2022

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Crushing rate and impurity rate are important quality indicators of mechanically harvested soybeans. Intelligent quality detection of mechanically harvested soybeans based on machine vision is of great significance to evaluate soybean quality accurately and rapidly. This study proposes an improved U-Net method for identifying intact soybean grains, crushing soybean grains, and impurities. Based on the accurate identification of soybean components and using the quantitative model of soybean crushing rate and impurity rate, the quality of soybean samples can be detected in real-time. To this end, a soybean quality inspection system is designed to realize the dynamic collection and detection of soybean samples. The test results show that the comprehensive evaluation index values of the improved U-Net segmentation algorithm in identifying intact soybean grains, crushing soybean grains, and impurities are 93.04%, 89.40%, and 96.49%, respectively. Compared with the traditional U-Net model, the performance of the indicators is improved by 3.23%, 0.17% and 0.72%, respectively. Compared with manual detection, the maximum absolute error of the crushing rate detection of the soybean quality detection system is 0.57%, and the maximum absolute error of the impurity rate detection is 0.69%. The proposed soybean quality inspection system can be used as an effective tool for real-time online inspection of soybean quality.

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Artificial neural network for determining the hedonic score of texture of and distinguishing different grades of ham sausages

Cited by 7 publications

References 13 publications

Effect of defatted soy and peanut flour obtained by new aqueous method on quality of gluten‐free cookies

Effect of defatted soy and peanut flour obtained by new aqueous method on quality of gluten‐free cookies

Spectral inversion model of the crushing rate of soybean under mechanized harvesting

Semantic segmentation-based mechanized harvesting soybean quality detection

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