CIRAD, P.O. Box 946, Port-Vila, Vanuatu.
J Agric Food Chem. 2009 Nov 25;57(22):10539-47. doi: 10.1021/jf902675n.
Tropical root and tuber crops (cassava, sweet potato, taro, and yam) are staples in developing countries where rapid urbanization is strengthening the demand for flour based foods. Quality control techniques are still under development, and when available, laboratory analyses are too expensive. The objectives of this study were to calibrate Near-infrared spectroscopy (NIRS) for routine analysis of flours and to test its reliability to determine their major constituents. Flours prepared from 472 accessions (traditional varieties and breeding lines) were analyzed for their starch, total sugars, cellulose, total nitrogen, and ash (total minerals) contents. The near-infrared (350-2500 nm) spectra of all samples were measured. Calibration equations with cross and independent validation for all analytical characteristics were computed using the partial least squares method. Models were developed separately for each of the four crop species and by combining data from all spp. to predict values within each of them. The quality of prediction was evaluated on a test set of 94 accessions (20%) by standard error of prediction (SEP) and r2 parameters between the measured and the predicted values from cross-validation. Starch, sugar, and total nitrogen content could be predicted, respectively, with 87%, 86%, and 93% confidence, whereas ash (minerals) could be predicted with 71%, and cellulose was not predictable (r2=0.31). The statistical parameters obtained for starch, sugars, and total nitrogen are of special interest for flour quality control. These constituents are quantitatively the most important in the chemical composition of flours, and starch content is negatively correlated with sugars and total nitrogen. NIRS is a low cost technique well adapted to the conditions in developing countries and can be used for the high-throughput screening of a great number of samples. Possible applications are discussed.
热带块根和块茎作物(木薯、甘薯、芋头和山药)是发展中国家的主食,快速的城市化正在加强对面粉食品的需求。质量控制技术仍在开发中,即使有可用的技术,实验室分析也过于昂贵。本研究的目的是校准近红外光谱(NIRS)以常规分析面粉,并测试其可靠性以确定其主要成分。从 472 个品系(传统品种和育种系)制备的面粉分析了其淀粉、总糖、纤维素、总氮和灰分(总矿物质)含量。测量了所有样品的近红外(350-2500nm)光谱。使用偏最小二乘方法计算了用于所有分析特性的交叉和独立验证的校准方程。为每个作物物种分别建立了模型,并通过组合所有物种的数据来预测它们各自的数值。通过预测的交叉验证来评估在 94 个品系(20%)测试集上的预测质量,通过标准预测误差(SEP)和 r2 参数来评估预测值和测量值之间的差异。淀粉、糖和总氮含量的预测置信度分别为 87%、86%和 93%,而灰分(矿物质)的预测置信度为 71%,纤维素不可预测(r2=0.31)。获得的淀粉、糖和总氮的统计参数对面粉质量控制特别感兴趣。这些成分在面粉的化学成分中定量上是最重要的,淀粉含量与糖和总氮呈负相关。NIRS 是一种适应发展中国家条件的低成本技术,可用于高通量筛选大量样品。讨论了可能的应用。