偏最小二乘回归
校准
化学计量学
近红外光谱
离群值
相关系数
稳健性(进化)
计算机科学
生物系统
数学
分析化学(期刊)
人工智能
化学
统计
色谱法
光学
机器学习
物理
基因
生物
生物化学
作者
Pao Li,Shangke Li,Guorong Du,Liwen Jiang,Xia Liu,Shenghua Ding,Yang Shan
摘要
Abstract A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near‐infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave‐one‐out cross‐validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content.
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