A review on intelligent sensory modelling

Tham, Heng Jin; Tang, Songping; Teo, Kenneth Tze Kin; Loh, Su Peng

doi:10.1088/1755-1315/36/1/012065

Cited by 4 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, some authors advocated the use of fuzzy cluster analysis techniques (Shinde et al, 2017) as these methods enjoy appealing properties such as fuzzy membership and flexibility. However, the understanding and applications of the fuzzy theory are still scarce in the field of sensory data (Tham et al, 2016). Pinto et al, (2014) suggested the use of Cronbach's alpha (CA) coefficient as an analytically simple method to identify panelists familiar with welldefined attributes and rank the panelists as their consensus.…”

Section: Discussionmentioning

confidence: 99%

“…The rationale behind this strategy has been to assign a unique weight to each assessor and thereby to identify good performers and bad performers. Rational methods like fuzzy logic, neural network, data aggregation, classification, and clustering have been proposed to solve the vagueness related to this field (Tham et al, 2016). Clustering of Latent Variables (CLV) which aimed at clustering sensory descriptors along with a latent component has been the strategy used in the study by Vigneau et al (2003).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Clustering around latent variables approach to detect panel disagreement in three–way tea sensory evaluation

Fernando¹,

Samita²,

Peiris³

2020

IJSRP

View full text Add to dashboard Cite

Sensory evaluation plays a remarkable role in maintaining the quality standards of beverages such as tea. The quality, which determines the price of tea, is evaluated by professional tea tasters. Uncertainty and vagueness of sensory evaluation has been a serious issue in selection of good quality tea. An issue existing when analyzing sensory data to detect panel disagreement is that data of three dimensional (three-way) or higher are often reduced to two-way data. Present study aimed to investigate the possibility of using Clustering around Latent Variables for three-way data (CLV3W) method to detect panel disagreement in sensory data of Sri Lankan tea. A three-way data set, 8 tea tasters× 13 tea growing regions×6 attributes, for each month with four replicates (4 factories per region), for a period of one calendar year were used for the study. When CLV3W analysis was performed separately for each month data, it was found two-clusters (two-latent components) exist for the data. Attribute loadings of colour and strength indicated that they are represented by the first latent component, and those loadings for brightness, flavour, aroma, and quality indicated that they are represented by the second component. Region scores for the two components revealed grouping of regions indicating some regions have similar tea quality attributes. Assessor weights varied across latent components and months. However, by examining assessor weights it was possible to identify assessors those who were in agreement and those who were in disagreement for each component. This finding demonstrated the fact that certain assessors are more sensitive to certain attributes and thus in order to detect tea with certain attributes, appropriate assessors can be employed. Therefore, CLV3W method is a useful method to detect disagreement between assessors, especially in 3-way data, and it can effectively be used when selecting assessors for sensory evaluation.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Clustering around latent variables approach to detect panel disagreement in three–way tea sensory evaluation

Fernando¹,

Samita²,

Peiris³

2020

IJSRP

View full text Add to dashboard Cite

show abstract

“…Some commonly used instruments for sensory analysis include (i) Appearance (e.g., colour, shape): Spectrophotometer, colorimeters and electronic eyes for colour and shape evaluation, (ii) Smell: electronic nose, gas chromatography, (iii) Taste: Electronic tongues and (iv) Texture: Texture analyser (Marie M üller von Blumencron ( 2015)). However, the consumer acceptance cannot be evaluated from instrumental analysis as they only assign a numerical value for the sensory attributes (Tham et al, 2016).…”

Section: Mangomentioning

confidence: 99%

Effect of drying techniques on quality and sensory properties of tropical fruits

Petikirige

Karim

Millar

2022

Int J of Food Sci Tech

View full text Add to dashboard Cite

Summary Drying is a popular food preservation technology that reduces food waste and promotes food preservation and food security. Though drying reduces microbial growth and increases shelf life of fruits, it alters sensory properties both favourably and unfavourably depending on the nature of the drying process applied and significantly influences the final product quality. However, there is currently no review article on the impact of the drying process on the sensorial quality attributes of tropical fruits. This review attempts to describe the relative merits and challenges of some widely used advanced drying techniques with a particular focus on intermittent microwave convective drying (IMCD) techniques which offer high sensory properties with excellent drying efficiency. The paper aims to provide an insight into the effect of these drying techniques on the sensory qualities of tropical fruits to identify the best drying method for future food applications.

show abstract

“…Since product quality and human responses to products are assessed in sensory evaluations, organoleptic properties play an important role in food quality concerns (Silva et al, 2014). Hence, a sensory evaluation involves the application of well-established experimental design together with statistical analysis of sensory data (Tham et al, 2016). The use of organoleptic evaluation to identify food quality is rapid (Duan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Data obtained from sensory tests are naturally fuzzy and uncertain. Therefore, rational methods like fuzzy logic, neural network, data aggregation, classification, and clustering methods have been proposed to screen the inconsistency and also to solve the vagueness related to the sensory field (Tham et al, 2016). Due to its adaptability, the analysis of variance (ANOVA) has been employed frequently in the sensory analysis (Peiris et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identifying Panel Inconsistency in Sensory Profiles using Multivariate Analysis of Variance (MANOVA) and follow–up Canonical Variate Analysis (CVA)

2021

View full text Add to dashboard Cite

Inconsistency in sensory evaluation is a serious problem and it often leads to loss of large revenue. Screening tasters before the sensory evaluation is the only remedy to overcome this inconsistency. This paper aims at showing how the above requirement can be achieved by using Multivariate Analysis of Variance (MANOVA) and follow-up Canonical Variate Analysis (CVA). The approach was illustrated using sensory profiles of Sri Lankan tea. The principle behind the approach is that any discrepancy between assessors on several attributes is detected simultaneously using MANOVA, and the discrepancy interacts with the factors such as products or region is detected using CVA. Data used for the study consisted of sensory scores given by 8 tea tasters for 13 tea growing regions on 6 attributes; colour, brightness, strength, flavour, aroma, quality. Samples from four factories represented a region, and data were collected for a one-year period on a monthly basis. Data from each month were analyzed separately. The Wilk's Lambda statistics of MANOVA revealed assessor effect as well as assessor × region interaction effect (P < 0.05) in every month indicating the inconsistency among assessors. The CVA for each region, specifically the 95% confidence regions of CV bi-plots, clearly identified clusters of assessors. Based on the location of these clusters in bi-plots, assessors who are suitable for different attributes were also identified. MANOVA followed by CVA, can effectively be used to identify discrepancies between assessors, discrepancy interacts with factors such as geographical region, and selecting consistent assessors depending on product or region and season.

show abstract

A review on intelligent sensory modelling

Cited by 4 publications

References 38 publications

Clustering around latent variables approach to detect panel disagreement in three–way tea sensory evaluation

Clustering around latent variables approach to detect panel disagreement in three–way tea sensory evaluation

Effect of drying techniques on quality and sensory properties of tropical fruits

Identifying Panel Inconsistency in Sensory Profiles using Multivariate Analysis of Variance (MANOVA) and follow–up Canonical Variate Analysis (CVA)

Contact Info

Product

Resources

About