Abstract This study was designed to quantify the phenolics and flavonoids in wild Lycium ruthenicum Murr. using portable near-infrared (NIR) spectroscopy integrated with chemometric algorithms. Synergy interval partial least squares with ant colony optimization (Si-ACO-PLS) was applied to optimize and capture the target variables for the determination of phenolics and flavonoids. The constructed models were evaluated using the correlation coefficients of the calibration (Rc) and prediction (Rp), the root mean square error of prediction (RMSEP) and the residual predictive deviation (RPD). The Si-ACO-PLS models yielded 0.8522 ≤ Rc ≤ 0.9500, 0.8226 ≤ Rp ≤ 0.9448, 1.43 ≤ RMSEP ≤ 3.77 and 2.09 ≤ RPD ≤ 2.88. Conclusively, portable NIR spectroscopy coupled with Si-ACO-PLS was demonstrated to be simple, rapid and cost-effective to quantify the phenolics and flavonoids in L. ruthenicum samples.

中文翻译：

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野生枸杞中酚类化合物的快速筛选。使用便携式近红外 (NIR) 光谱耦合多变量分析

摘要 本研究旨在量化野生枸杞中的酚类和黄酮类化合物。使用与化学计量算法集成的便携式近红外 (NIR) 光谱。应用协同区间偏最小二乘法和蚁群优化 (Si-ACO-PLS) 来优化和捕获用于测定酚类和黄酮类化合物的目标变量。使用校准 (Rc) 和预测 (Rp) 的相关系数、预测的均方根误差 (RMSEP) 和残余预测偏差 (RPD) 评估构建的模型。Si-ACO-PLS 模型得出 0.8522 ≤ Rc ≤ 0.9500、0.8226 ≤ Rp ≤ 0.9448、1.43 ≤ RMSEP ≤ 3.77 和 2.09 ≤ RPD ≤ 2.88。最后，便携式 NIR 光谱与 Si-ACO-PLS 相结合被证明是简单的，