Quantitative analysis of organic acids in pomelo fruit using FT-NIR spectroscopy coupled with network kernel PLS regression

Abstract Pomelo is a high-class characteristic food in China. The fruit quality of pomelo is much important for people’s health and sense of taste. After being picked, the fruit quality deteriorates because of the spontaneous physiological reactions due to the metabolism and changes of organic acids (OA). The detection of OA concentration is essential for evaluating the pomelo fruit quality. In this paper, the OA concentrations in pomelo fruit samples were quantitatively determined by using Fourier transform near-infrared (FT-NIR) spectroscopy coupled with the kernel partial least square (kernel PLS) method. The network architectural kernel was proposed for extracting the spectral feature data in the machine learning manner. The number of network hidden nodes were tuned in combination of the optimally selection of the PLS latent variables. An error-feedback iteration mechanism was used for self-adaptively training the network linking weights. Model calibration results show that the most suitable network structure was identified with 130 hidden nodes and 20 output nodes. The optimal kernel improved the PLS model with 8 feature latent variables. The model training results were acquired with the root mean square error as 0.834 g/kg, and the correlation coefficient as 0.936. In comparison with common kernel functions, the network kernel performed better in the model training part and in the testing part. The model prediction results indicated that the proposed automatically training of the network architectural kernel is feasible to improve the PLS modeling effects for the FT-NIR rapid analysis of OA in pomelo fruit.