Li Pao, Li Shangke, Du Guorong, Jiang Liwen, Liu Xia, Ding Shenghua, Shan Yang
College of Food Science and Technology Hunan Provincial Key Laboratory of Food Science and Biotechnology Hunan Agricultural University Changsha China.
Hunan Agricultural Product Processing Institute Hunan Academy of Agricultural Sciences Changsha China.
Food Sci Nutr. 2020 Apr 14;8(5):2543-2552. doi: 10.1002/fsn3.1550. eCollection 2020 May.
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.
采用便携式可见-近红外光谱仪(Vis/NIRS)的反射模式并结合适当的化学计量学方法,建立了一种简单且无损的柑橘可溶性固形物含量分析方法。通过便携式Vis/NIRS直接获取光谱,无需破坏样品。采用留一法交叉验证(LOOCV)和3σ准则进行异常值检测,并通过偏最小二乘法(PLS)算法建立校准模型。此外,在校准前使用不同的数据预处理方法来消除噪声和背景干扰,以确定哪种方法能获得更好的模型精度。然而,相关系数均<0.62,所有预处理结果仍不尽人意。讨论了变量选择方法以提高精度,并使用可变自适应增强偏最小二乘法(VABPLS)获得更高稳健性的模型。结果表明,标准正态变量(SNV)变换是最佳的预处理方法,而VABPLS即使不进行预处理也能显著简化计算并改善结果。最佳预测模型的相关系数为0.82,而原始数据的值为0.48。高性能表明便携式Vis/NIRS技术结合适当的化学计量学方法用于测定柑橘可溶性固形物含量的可行性。