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Abstract：
Cottonseed is rich in oil and protein. However, its antinutritional factor content, of phytic acid (PA), has limited its utilization. Near-infrared (NIR) spectroscopy, combined with chemometrics, is an efficient and eco-friendly analytical technique for crop quality analysis. Despite its potential, there are currently no established NIR models for measuring the PA content in fuzzy cottonseeds. In this research, a total of 456 samples of fuzzy cottonseed were used as the experimental materials. Spectral pre-treatments, including first derivative (1D) and standard normal variable transformation (SNV), were applied, and the linear partial least squares (PLS), nonlinear support vector machine (SVM), and random forest (RF) methods were utilized to develop accurate calibration models for predicting the content of PA in fuzzy cottonseed. The results showed that the spectral pre-treatment significantly improved the prediction performance of the models, with the RF model exhibiting the best prediction performance. The RF model had a coefficient of determination in prediction (R2p) of 0.9114, and its residual predictive deviation (RPD) was 3.9828, which indicates its high accuracy in measuring the PA content in fuzzy cottonseed. Additionally, this method avoids the costly and time-consuming delinting and crushing of cottonseeds, making it an economical and environmentally friendly alternative.
摘要：
棉籽富含油和蛋白质。然而,其抗营养因子含量，植酸（PA），限制了它的使用。近红外(NIR)光谱,结合化学计量学，是一种高效、环保的作物品质分析技术。尽管有潜力,目前还没有建立测量模糊棉籽中PA含量的NIR模型。在这项研究中,共456份模糊棉籽样品作为实验材料。光谱预处理,包括一阶导数(1D)和标准正变量变换(SNV)，被应用，和线性偏最小二乘(PLS)，非线性支持向量机(SVM),利用随机森林（RF）方法开发了准确的校准模型，用于预测模糊棉籽中PA的含量。结果表明,光谱预处理显著提高了模型的预测性能,RF模型表现出最佳的预测性能。RF模型的预测决定系数（R2p）为0.9114，残差预测偏差（RPD）为3.9828，表明其在测量模糊棉籽中PA含量方面具有很高的准确性。此外,这种方法避免了昂贵且耗时的棉籽脱皮和破碎，使其成为一种经济和环保的替代品。
