PDF(Pubmed)
Abstract：
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.
摘要：
碳水化合物是扁豆的主要成分，占其组成的60%以上。它们的含量受遗传因素的影响，根据品种不同的内容。这些化合物不仅与有趣的健康益处有关，但它们对扁豆衍生产品的技术功能特性也有重大影响。在这项研究中,使用近红外光谱（NIRS）来预测总碳水化合物的浓度，纤维，淀粉，总糖，果糖,研究蔗糖和棉子糖。为此,分析了六个不同品种的大精子（n=37）和小精子（n=43）小扁豆，对样本进行了整体和地面记录，并比较了两种记录方法的适用性。在使用改进的偏最小二乘回归方法开发校准模型之前，对不同的光谱和数学预处理进行了评估。交叉验证和外部验证。所开发的预测模型对总糖和果糖显示出优异的测定系数(RSQ>0.9)，蔗糖,还有棉子糖.地面样品的记录可以获得更好的模型来校准淀粉含量（R>0.8），总糖和蔗糖（R>0.93），和棉子糖(R>0.91)。获得的结果证实，NIRS光谱区中有足够的信息来开发用于量化小扁豆中碳水化合物含量的预测模型。
