为了评估微型近红外光谱仪应用于现场水果糖度检测的可行性, 采用粒子群算法结合反向传播(BP)神经网络建立了苹果糖度的无损高精度快速检测方法, 研究了微型近红外光谱仪NIRscan以单波长和阿达玛变换两种测量模式获得的光谱数据, 应用多种不同的数据预处理方法和多元线性回归、偏最小二乘法、粒子群算法(PSO)、BP神经网络等算法建立分析模型。结果表明, 以阿达玛变换工作模式测得的光谱数据更好, 以1阶导数结合Savizky-Golay平滑算法作数据预处理, 应用PSO结合BP神经网络建立的苹果糖度预测模型具有更高的预测精度, 预测相关系数和均方根误差分别为0.9911和0.1502。该微型近红外光谱仪NIRscan用于苹果糖度的现场快速和高精度无损检测具有可行性。

In order to evaluate the feasibility of miniature near infrared spectroscopy (NIRS) in detecting sugar content of fruits in situ, non-destructive, high-precision and fast detection method of apple sugar content was established by combining particle swarm optimization with back propagation (BP) neural network. The spectral data obtained by NIRscan(micro-NIRS) using single wavelength measurement mode and Hadamard transform measurement mode were studied. A variety of different data preprocessing methods and multiple linear regression, partial least squares, particle swarm optimization (PSO), BP neural network and other algorithms were used to establish the analysis model. The results show that the spectral data obtained by the working mode of Hadamard transform are better. First derivative combined with Savizky-Golay smoothing algorithm is used for data preprocessing. The prediction model of apple sugar content based on PSO and BP neural network has higher prediction accuracy. Predictive correlation coefficient and root mean square error are 0.9911 and 0.1502, respectively. NIRscan (micro-NIRS) is feasible for rapid and high-precision non-destructive testing of apple sugar content in the field.