Sensory Preference Modeling of Probiotic Shrikhand Employing Soft Computing

Meena, Ganga Sahay; Kumar, Navneet; Parmar, Pankaj T.; Banerjee, Rintu; Majumdar, Gautam; Khetra, Yogesh

doi:10.1007/s40003-016-0221-y

Cited by 7 publications

(12 citation statements)

References 26 publications

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“…However, fifth day onwards, fuzzy model was able to resolve linguistic scores to provide substantial information to make a decision for ranking the paneer samples. This might be due to normalization of sensory scores on fuzzy scale of the linguistic data, and as such with large semi‐trained sensory panelist the minor product variations were extremely challenging to distinguish (Meena et al., 2016). We also observed that quality ranking subsets formed higher values post NFMF compared to JMF for initial days of storage.…”

Section: Resultsmentioning

confidence: 99%

“…and dependent (e.g., acceptance, rejection, ranking, strong, and weak) attributes of food for sensory‐based comparisons of food products. Though fuzzy model is extensively used to optimize product considering sensory likeness (Jaya & Das, 2003; Kardile et al., 2018; Meena et al., 2016; Mukhopadhyay et al., 2013; Singh et al., 2012) using single point assessments of experts on a given time (common practice is to evaluate as soon as product is manufactured), it fails to account the sensory likeness of products during/throughout the storage.…”

Section: Introductionmentioning

confidence: 99%

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Prediction and qualitative analysis of sensory perceptions over temporal vectors using combination of artificial neural networks and fuzzy logic: Validation on Indian cheese (paneer)

Chaturvedi

Khubber

Singha

et al. 2020

J. Food Process. Preserv.

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The present study investigated the feedforward-backpropagation artificial neural network (ANN) architectures to predict the sensory scores at different moisture levels (40%-50%) of paneer. Paneer was produced aseptically and packed in low density polyethylene (LDPE) bags, in a semi-organized commercial dairy plant. Samples were evaluated at regular intervals (8 days) for biochemical content and microbial counts, while subjected every day for sensory evaluation. Three layered (input-hiddenoutput) ANN was able to produce similar sensory responses using biochemical and microbiological data, for both single (best combination 10-7-1; R 2 ≈ 0.99) and conjugated (10-9-4; R 2 ≈ 0.97) parameters for an extent of 25 sensory output nodes (10-35-25; R 2 ≈ 0.90). The comparison of ANN and predicted sensory scores using linear regression model (with R 2 = 0.99) suggests that paneer at 42%-44% and 47%-49% moisture was best for consumption as fresh and for storage. The developed method for predicting and analyzing sensory data of produced paneer opens new possibilities for improving food product's likeness. Practical applications Current fuzzy models probe quality ranking of complex sensory responses as discrete values, normally onset of manufacturing. However, products are stored for a longer period before reaching to the consumers. The results of the present findings suggest applications of machine learning, such as artificial neural network, for predicting sensory performance of the product throughout the shelf using few microbiological and biochemical parameters. The developed fuzzy scale for approaching qualitative performance of the product in terms of sensory likeness would enable manufacturers to account changes throughout the shelf, providing new solutions for problems in sensory data analysis. Henceforth, opening possibilities of combining fuzzy logic and machine learning to obtain robust and conclusive data analysis for quality comparison for stored product, while enabling ANN to predict them.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction and qualitative analysis of sensory perceptions over temporal vectors using combination of artificial neural networks and fuzzy logic: Validation on Indian cheese (paneer)

Chaturvedi

Khubber

Singha

et al. 2020

J. Food Process. Preserv.

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“…Further, such packages are unable to highlight the data pertaining to the strength and weakness of individual quality characteristics of specific product that may ultimately decide its acceptance or rejection [17]. Fuzzy logic is a well-established decision-making tool that performs important functions such as development, improvement and comparison of the new products with similar existing products and also identify the impact of a specific quality attribute on the final quality of the developed product [18]. A mathematical relation is developed between independent (e.g.…”

Section: Necessity Of Sensory Evaluation and Significance Fuzzy Logic In Consumer Preference Analysismentioning

confidence: 99%

Fuzzy Analysis of Sensory Attributes of Gluten Free Pasta Prepared From Brown Rice, Amaranth, Flaxseed Flours and Whey Protein Concentrates

Meena¹,

Dewan²,

Upadhyay³

et al. 2019

J Food Sci Nutr Res

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Pasta is a cereal based, ready to cook, staple food, known for its affordable price, easy cooking, preferable sensory appeal and better storage stability, but its popularity is now growing as a healthy food worldwide. It is generally made from durum wheat semolina. Pasta made from gluten containing cereals creates problem for celiac patients.Hence, current study was undertaken (i) to prepare gluten free pasta from optimized levels of brown rice, amaranth flour, flaxseed flour and whey protein concentrate (WPC-70) and, (ii) to compare sensorial quality of gluten free pasta vis-a-vis available market samples of pasta to avoid market failure using fuzzy logic soft computing tool.Sensory evaluation was performed by a trained panel of sixteen judges. 'In general' ranking of pasta samples and their quality attributes was determined in linguistic term as (in decreasing order): Sample 4 (very good)> Sample 2 (very good)>Sample 3 (good)>Sample 1 (satisfactory) and Texture (highly important)>Flavor (highly important)> Appearance (important)>Color (important), respectively. However, exact ranking of pasta samples was obtained on the basis of maximum similarity value through fuzzy logic as shown in descending order: Sample 4 'very good'> Sample 2 'very good'>Sample 1 'good'>Sample 3 'good'. Gluten free pasta meets consumer's preference in terms of 'good' sensorial quality as revealed by fuzzy logic; contains higher dietary fibre, minerals and superior milk proteins than traditional pasta made from durum wheat. Therefore, it can be considered as a better and nutritional choice for celiac patients and general consumers.

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“…Among dairy products, fermented dairy products are getting more attention fromconsumers due to their nutritive as well as therapeutic value [1][2][3][4][5][6][7]. In India, seven percent of total milk production is utilized for the preparation of fermented dairy products such as curd, buttermilk and Shrikhand [8]. Shrikhand is a traditional Indian dairy product which is prepared by fermentation of whole milk by lactic acid bacteria followed by straining of curd (Chakka or strained dahi) and then mixing with powdered sugar and flavor.…”

Section: Introductionmentioning

confidence: 99%

“…Superior quality with increased shelf life shrikhandis obtained if a post-processing heat treatment is given to the shrikhand. Presently, there is an increase in demand forshrikhand containing some functional ingredient [11,12].…”

Section: Introductionmentioning

confidence: 99%

Effect of guar fiber on physicochemical, textural and sensory properties of sweetened strained yoghurt

2020

Biointerface Res Appl Chem

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Naturally, milk & milk products are devoid of dietary fiber hence there is scope of dietary fiber fortification of dairy products. In the present study, sweetened strained yoghurt was fortified with dietary fiber using partially hydrolyzed guar gum (PHGG). PHGG is prepared by enzymatic hydrolysis of guar gum & exhibit low viscosity & molecular weight as compared to guar gum. PHGG was added to sweetened strained yoghurt at four levels i.e. 2.5%, 5.0%, 7.5% & 10% (w/w basis). Control & PHGG fortified sweetened strained yoghurt samples were subjected to textural (firmness, adhesiveness & cohesiveness) and sensory evaluation. Results revealed that PHGG can be utilized for fiber fortification of sweetened strained yoghurt. PHGG fortification in sweetened strained yoghurt decreased the firmness & adhesiveness whereas increased the cohesiveness. Sweetened strained yoghurt fortified with PHGG at 5% level showed overall sensory acceptability equivalent to control sweetened strained yoghurt.

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Sensory Preference Modeling of Probiotic Shrikhand Employing Soft Computing

Cited by 7 publications

References 26 publications

Prediction and qualitative analysis of sensory perceptions over temporal vectors using combination of artificial neural networks and fuzzy logic: Validation on Indian cheese (paneer)

Prediction and qualitative analysis of sensory perceptions over temporal vectors using combination of artificial neural networks and fuzzy logic: Validation on Indian cheese (paneer)

Fuzzy Analysis of Sensory Attributes of Gluten Free Pasta Prepared From Brown Rice, Amaranth, Flaxseed Flours and Whey Protein Concentrates

Effect of guar fiber on physicochemical, textural and sensory properties of sweetened strained yoghurt

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