Chronic kidney disease (CKD) is becoming a major public health problem worldwide, and excessive potassium intake is a health threat to patients with CKD. In this study, visible–short-wave near-infrared (Vis–SWNIR) spectroscopy and chemometric algorithms were investigated as nondestructive methods for assessing the potassium concentration in fresh lettuce to benefit the CKD patients’ health. Interactance and transmittance measurements were performed and the competencies were compared based on the multivariate methods of partial least-square regression (PLS) and support vector machine regression (SVR). Meanwhile, several preprocessing methods [first- and second-order derivatives in combination with standard normal variate (SNV)] and wavelength selection method of competitive adaptive reweighted sampling (CARS) were applied to eliminate noise and highlight the spectral characteristics. The PLS models yielded better prediction than the SVR models with higher correlation coefficients ([Formula: see text]) and residual predictive deviation (RPD), and lower root-mean-square error of prediction (RMSEP). Excellent prediction of green leaves was obtained by the interactance measurement with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and [Formula: see text]; while the transmittance spectra of petioles provided optimal prediction with [Formula: see text], [Formula: see text][Formula: see text]mg/100[Formula: see text]g, and RPD[Formula: see text]=[Formula: see text]3.34, respectively. Therefore, the results indicated that Vis–SWNIR spectroscopy is capable of intelligently detecting potassium concentration in fresh lettuce to benefit CKD patients around the world in maintaining and enhancing their health.

中文翻译：

---

用 Vis-SWNIR 光谱法定量新鲜生菜中的钾浓度

慢性肾脏病 (CKD) 正在成为全球主要的公共卫生问题，钾摄入过多对 CKD 患者的健康构成威胁。在这项研究中，研究了可见-短波近红外 (Vis-SWNIR) 光谱和化学计量学算法作为评估新鲜生菜中钾浓度的无损方法，以有益于 CKD 患者的健康。进行了相互作用和透射率测量，并基于偏最小二乘回归 (PLS) 和支持向量机回归 (SVR) 的多元方法比较了能力。同时，应用了几种预处理方法[一阶和二阶导数结合标准正态变量（SNV）]和竞争自适应重加权采样（CARS）的波长选择方法来消除噪声并突出光谱特征。PLS 模型比 SVR 模型产生更好的预测，具有更高的相关系数（[公式：见文本]）和残差预测偏差（RPD），以及更低的预测均方根误差（RMSEP）。通过与[公式：见文]、[公式：见文][公式：见文]mg/100[公式：见文]g和[公式：见文]的交互作用测量，获得了良好的绿叶预测；而叶柄的透射光谱提供了最佳预测[公式：见文]，[公式：见文][公式：见文]mg/100[公式：见文]g，和 RPD[公式：见正文]=[公式：见正文]3.34。因此，结果表明 Vis-SWNIR 光谱能够智能地检测新鲜生菜中的钾浓度，有利于世界各地的 CKD 患者维持和增强健康。