Food Technology Area, Escuela Politécnica Superior de Zamora, Universidad de Salamanca, Avenida Requejo, 33, 49022 Zamora, Spain.
Centro de Investigaçao de Montanha (CIMO), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253 Bragança, Portugal.
Sensors (Basel). 2024 Jun 29;24(13):4232. doi: 10.3390/s24134232.
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.
碳水化合物是小扁豆的主要成分，占其组成的 60%以上。它们的含量受遗传因素的影响，不同品种的含量也不同。这些化合物不仅与有趣的健康益处有关，而且对小扁豆衍生产品的技术功能特性有重大影响。在这项研究中，使用近红外光谱（NIRS）来预测总碳水化合物、纤维、淀粉、总糖、果糖、蔗糖和棉子糖的浓度。为此，分析了 6 种不同的大孢子（n = 37）和小孢子（n = 43）小扁豆，对整个和粉碎的样品进行了记录，并比较了两种记录方法的适用性。在使用修正偏最小二乘回归法开发校准模型之前，评估了不同的光谱和数学预处理方法，并进行了交叉验证和外部验证。开发的预测模型对于总糖和果糖、蔗糖和棉子糖具有出色的决定系数（RSQ > 0.9）。粉碎样品的记录允许获得更好的淀粉含量（R > 0.8）、总糖和蔗糖（R > 0.93）以及棉子糖（R > 0.91）的校准模型。所得到的结果证实，NIRS 光谱区域有足够的信息可用于开发小扁豆碳水化合物含量定量的预测模型。
