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–shortwave 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 (R²) 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 R² = 0.93, RMSEP = 24.86 mg/100 g, and RPD = 3.69; while the transmittance spectra of petioles provided optimal prediction with R² = 0.92, RMSEP = 27.80 mg/100 g, and RPD=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.

为了研究宁夏地区土壤的水分迁移机理以及对土壤水分快速无损检测, 利用高光谱成像(光谱范围900~1 700 nm)技术对土壤的含水率进行了研究。通过高光谱成像系统采集了208个土样, 比较了不同天数下土壤含水率与光谱的变化、不同质量含水量光谱的差异。对采集到的土样进行PLSR模型建立, 对比分析不同光谱预处理方法、不同方法提取特征波长(UVE、CARS、β系数、SPA)、不同建模方法(MLR、PCR、PLSR)建立的模型, 优选出最佳模型。结果表明: 在一定的土壤含水量范围内, 光谱曲线的反射率与土壤含水率成反比; 当增大到超过田间持水率时, 光谱曲线的反射率与土壤含水率成正比。对比分析了不同预处理方法, 优选出单位向量归一化预处理方法。对比不同的模型, 优选出SPA提取的特征波长的MLR模型。最优的特征波长为987, 1 386, 1 466, 1 568, 1 636, 1 645 nm, 最优模型的预测相关系数Rp=0.984, 预测均方根误差RMSEP为0.631。因此, 今后可采用不同波段对土壤含水率进行定量分析。

A rapid predictive method based on near-infrared reflectance spectroscopy (NIRS) of paddy rice was developed to measure the pasting properties of rice. The paddy rice samples were scanned by a near-infrared reflectance spectrometer in the wavelength region of 1400–2400 nm and preprocessed by mathematical pretreatments prior to pasting properties analysis using a rapid visco-analyzer (RVA). The results indicated that the developed models of setback (SB), peak viscosity (PV), breakdown (BD) and consistency (CS) provided good prediction results with relatively high correlation coefficients (0.81–0.96). In addition, the validity of the calibration models was statistically tested. Standard error of prediction (SEPT and bias were small enough without any significance at 95% confidence interval. Nonetheless, this study proved that the use of NIRS for predicting pasting properties was feasible in paddy rice and could be applied in commercial trade and research.