Determining sensory drivers of complex metadescriptors through regression modelling
In sensory science, terms such as creaminess often lack precise definitions due to their multi-modal nature. Least absolute shrinkage and selection operator (LASSO), a regression technique known for automatic predictor selection, and partial least squares regression, which handles multicollinearity,...
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Elsevier
2025-03-01
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| Series: | Science Talks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772569325000052 |
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| author | Emily Fisher Charles Diako Rebecca Shingleton Sidsel Jensen Joanne Hort |
| author_facet | Emily Fisher Charles Diako Rebecca Shingleton Sidsel Jensen Joanne Hort |
| author_sort | Emily Fisher |
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| description | In sensory science, terms such as creaminess often lack precise definitions due to their multi-modal nature. Least absolute shrinkage and selection operator (LASSO), a regression technique known for automatic predictor selection, and partial least squares regression, which handles multicollinearity, were compared for their ability to accurately identify the underlying sensory attributes driving creaminess perception.Twenty-eight sensory attributes were selected after discussions with milk consumers. Thirty-two milk samples were chosen to represent these attributes, spanning a wide range of creaminess. Quantitative descriptive analysis, with trained panellists, and a consumer study (n = 117 New Zealand milk drinkers) assessed the sensory attributes and creaminess ratings, respectively. LASSO and PLSR were compared for their predictive ability and attributes retained using sensory attributes (trained panel) as predictors and creaminess ratings (consumers) as the response variable.LASSO identified four key sensory attributes with a good model fit (R2 = 0.951), while PLSR suggested thirteen (R2 = 0.933). LASSO is effective in uncovering pertinent attributes within a complex sensory experience enabling cost-effective research. PLSR offers a comprehensive model for extensive product development. This research provides an alternative approach for determining pertinent attributes in complex metadesciptors. Resulting models offer clearer targets for product development, thus increased commercial gains. |
| format | Article |
| id | doaj-art-932f1d36907243d6b85ed5dc8b9de767 |
| institution | Kabale University |
| issn | 2772-5693 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Science Talks |
| spelling | doaj-art-932f1d36907243d6b85ed5dc8b9de7672025-02-08T05:01:40ZengElsevierScience Talks2772-56932025-03-0113100423Determining sensory drivers of complex metadescriptors through regression modellingEmily Fisher0Charles Diako1Rebecca Shingleton2Sidsel Jensen3Joanne Hort4Product Experience Sciences, Fonterra Research and Development Centre, Palmerston North 4472, New Zealand; Food Experience and Sensory Testing (Feast) Lab, Massey University, Palmerston North 4410, New Zealand; Corresponding author at: Product Experience Sciences, Fonterra Research and Development Centre, Palmerston North 4472, New Zealand.School of Food Technology and Natural Sciences, Massey University, Palmerston North 4410, New Zealand; Food Experience and Sensory Testing (Feast) Lab, Massey University, Palmerston North 4410, New ZealandCategory Innovation – Dairy Foods and Food Service, Fonterra, Palmerston North 4472, New ZealandProduct Experience Sciences, Fonterra Research and Development Centre, Palmerston North 4472, New ZealandSchool of Food Technology and Natural Sciences, Massey University, Palmerston North 4410, New Zealand; Food Experience and Sensory Testing (Feast) Lab, Massey University, Palmerston North 4410, New Zealand; Riddet Institute, Massey University, 4410 Palmerston North, New ZealandIn sensory science, terms such as creaminess often lack precise definitions due to their multi-modal nature. Least absolute shrinkage and selection operator (LASSO), a regression technique known for automatic predictor selection, and partial least squares regression, which handles multicollinearity, were compared for their ability to accurately identify the underlying sensory attributes driving creaminess perception.Twenty-eight sensory attributes were selected after discussions with milk consumers. Thirty-two milk samples were chosen to represent these attributes, spanning a wide range of creaminess. Quantitative descriptive analysis, with trained panellists, and a consumer study (n = 117 New Zealand milk drinkers) assessed the sensory attributes and creaminess ratings, respectively. LASSO and PLSR were compared for their predictive ability and attributes retained using sensory attributes (trained panel) as predictors and creaminess ratings (consumers) as the response variable.LASSO identified four key sensory attributes with a good model fit (R2 = 0.951), while PLSR suggested thirteen (R2 = 0.933). LASSO is effective in uncovering pertinent attributes within a complex sensory experience enabling cost-effective research. PLSR offers a comprehensive model for extensive product development. This research provides an alternative approach for determining pertinent attributes in complex metadesciptors. Resulting models offer clearer targets for product development, thus increased commercial gains.http://www.sciencedirect.com/science/article/pii/S2772569325000052Regression modellingCreaminessMultimodalitySensoryConsumerComplex terms |
| spellingShingle | Emily Fisher Charles Diako Rebecca Shingleton Sidsel Jensen Joanne Hort Determining sensory drivers of complex metadescriptors through regression modelling Science Talks Regression modelling Creaminess Multimodality Sensory Consumer Complex terms |
| title | Determining sensory drivers of complex metadescriptors through regression modelling |
| title_full | Determining sensory drivers of complex metadescriptors through regression modelling |
| title_fullStr | Determining sensory drivers of complex metadescriptors through regression modelling |
| title_full_unstemmed | Determining sensory drivers of complex metadescriptors through regression modelling |
| title_short | Determining sensory drivers of complex metadescriptors through regression modelling |
| title_sort | determining sensory drivers of complex metadescriptors through regression modelling |
| topic | Regression modelling Creaminess Multimodality Sensory Consumer Complex terms |
| url | http://www.sciencedirect.com/science/article/pii/S2772569325000052 |
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