Authors

Abstract

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

Abstract in Chinese

为了快速、准确地测定苹果可溶性固形物含量，研制了基于USB2000+微型光谱仪的便携式苹果可溶性固形物含量检测仪。该仪器可以通过网络端口、蓝牙等方式与计算机终端和移动应用程序进行通信，实现苹果光谱信息的快速采集。首先采集了160份苹果样品的可见光/近红外光谱数据和可溶性固形物含量信息；其次，对光谱数据预处理方法进行了比较，结果表明，经过平均平滑预处理后，基于偏最小二乘法（PLS）的糖含量预测模型是准确的。预测模型的相关系数（RP）和均方根误差（RMSEP）分别为0.902和0.589°Brix。最后，在平均平滑预处理的基础上，采用竞争自适应重加权采样（CARS）和连续投影算法（SPA）对光谱数据的波长进行优化，并根据选取的17个特征波长构建了PLS模型，提高了可溶性固形物含量预测模型的精度，使RP提高到0.912，RMSEP降低到0.511°Brix。基于该仪器和方法的便携式可见/近红外光谱可溶性固形物预测模型具有较高的精度，该检测仪能够快速、准确地测定苹果可溶性固形物含量。