基于可见-近红外光谱分析技术开发了手持式水果糖度检测装置, 并用于水果糖度的现场实时分析。 硬件系统主要包括微型光谱仪、 卤素灯、 OLED显示屏、 单片机及驱动电路等。 采用Keil 5开发工具, 用C语言开发单片机程序。 配合上位机以LabView编写的光谱采集程序, 实现光谱信息的采集。 以苹果和大桃作为检测对象, 对装置的检测精度和模型在2台装置(主机、 从机)间的传递效果进行了探讨。 在实验室和果园环境下, 分别获取了苹果、 大桃样本在600~950 nm范围的可见-近红外光谱。 对实验室条件下采集的主机校正集光谱进行分析, 经过平滑、 最大值归一化、 二阶导数等预处理后, 利用偏最小二乘算法分别建立了苹果、 大桃的糖度检测模型。 模型导入主机装置后, 对预测集样本进行检验。 对苹果、 大桃的预测集相关系数和预测均方根误差分别为0.925, 0.587%和0.821, 0.613%。 采用分段直接校正和基于典型相关分析算法的模型传递方法将模型由主机传递到从机。 对比后发现, 基于典型相关分析算法取得了更好的模型传递结果。 从机对苹果、 大桃糖度的预测集相关系数和预测均方根误差分别为0.883, 0.641%和0.805, 0.626%。 将实验室条件下建立的模型用于树上采集到的水果光谱数据分析, 得到预测集相关系数和预测均方根误差分别为0.866, 0.741%和0.816, 0.627%。 整体检测结果表明, 该装置可以满足对苹果、 大桃糖度的有效检测, 借助模型传递算法, 实现了模型在不同装置间的共享和有效传递, 且实验室环境下采集的数据建立的模型可以用于树上水果糖度的有效检测, 该装置具有较大的经济价值和应用前景。

A handheld portable device for fruit sugar content was developed based on visible/near-infrared spectral analysis. The device consists of a micro-spectrometer, halogen lamps, OLED screen and microcontroller. The real-time analysis and control software of the microcontroller was written in C language with the help of the Keil 5 development tool. Combined with the spectrum acquisition program written by LabView, the spectra of fruit samples were collected by the developed device. Apples and big peaches were used to explore the detection accuracy of the device and the transfer of the model between two devices (master and slave). The visible-near infrared spectra of the apple and peach were collected in the spectral range of 600~950 nm under laboratory conditions and in the field. The spectral data of calibration set collected by the master device under laboratory conditions were preprocessed by smoothing, maximum normalization, second derivative and other preprocessing methods, followed by the sugar content models developed using partial least squares algorithm for apples and peaches respectively. The models were then imported to the custom software, making it possible for the master device to predict the sugar content of apples or peaches directly. The correlation coefficient and the root mean square error of the prediction set were 0.925, 0.587% and 0.821, 0.613% for apples and peaches, respectively. The models were transferred from the master device to the slave device by using the piecewise direct standardization (PDS) and canonical Correlation Analysis (CCA) algorithm. After comparison, it was found that better model transfer results were achieved based on the CCA algorithm. The correlation coefficient and root mean square error of the prediction set were 0.883, 0.641% and 0.805, 0.626% for apples and peaches, respectively. The model established under laboratory conditions was used to analyze the fruit spectral data collected on the tree, the correlation coefficient and root mean square error of the prediction set were 0.866, 0.741% and 0.816, 0.627% for apples and peaches, respectively. The results showed that the developed device had considerable potential to detect fruit sugar content under lab conditions, and in the field. With the help of the model transfer algorithm, the model can be shared and effectively transferred between different devices. The developed device could meet the demand for rapid, non-destructive, and on-site detection of internal fruit quality.