Determination of Carbohydrate Composition in Lentils Using Near-Infrared Spectroscopy
[EN] Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a s...
| Autores: | , , , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Universidad de Salamanca (USAL) |
| Repositório: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/160582 |
| Acesso em linha: | http://hdl.handle.net/10366/160582 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Near Infrared Spectroscopy (NIR) Carbohydrates Fibre Lentil Starch Sugars Espectroscopía de infrarrojo cercano (NIR) Carbohidratos Fibra Lenteja Almidón Azúcares 2209.21 Espectroscopia 3309.24 Almidón 3309.26 Azúcar |
| Resumo: | [EN] Carbohydrates are the main components of lentils, accounting for more than 60% of their composition. Their content is influenced by genetic factors, with different contents depending on the variety. These compounds have not only been linked to interesting health benefits, but they also have a significant influence on the techno-functional properties of lentil-derived products. In this study, the use of near-infrared spectroscopy (NIRS) to predict the concentration of total carbohydrate, fibre, starch, total sugars, fructose, sucrose and raffinose was investigated. For this purpose, six different cultivars of macrosperm (n = 37) and microsperm (n = 43) lentils have been analysed, the samples were recorded whole and ground and the suitability of both recording methods were compared. Different spectral and mathematical pre-treatments were evaluated before developing the calibration models using the Modified Partial Least Squares regression method, with a cross-validation and an external validation. The predictive models developed show excellent coefficients of determination (RSQ > 0.9) for the total sugars and fructose, sucrose, and raffinose. The recording of ground samples allowed for obtaining better models for the calibration of starch content (R > 0.8), total sugars and sucrose (R > 0.93), and raffinose (R > 0.91). The results obtained confirm that there is sufficient information in the NIRS spectral region for the development of predictive models for the quantification of the carbohydrate content in lentils. |
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