Effect of ambient temperature on the model stability of handheld devices for predicting the apple soluble solids content

The soluble solids content (SSC) is an important factor for determining the harvest time and the optimal storage time of apple fruit. However, changes in environmental temperatures cause the apple spectrum to fluctuate, which affects the robustness and accuracy of the model for predicting the apple fruit SSC. A portable detection device applicable for different temperatures was developed using a micro-spectrometer and temperature sensor. The detection time for the device was less than 2 s. The circuit board for collecting and analysing the spectrum signal, the detection light path and the housing of the device were designed, and the optimal integration time (6 ms) and light source power (4.5 W) of the device were determined. A total of 420 apple samples were examined in three ambient temperature ranges (0-2, 10-13, and 19-24 degrees C). The results showed that the model built independently in a single temperature environment can make a good prediction of the samples in the same temperature range. The application of d-value curve method reduced the influence of temperature on model accuracy to a certain extent. The d-value curve is a variable selection method independent of the reference value y, which describes the characterisation ability of each wavelength to samples in different sampling environments. These results conform that removing temperature-sensitive wavelengths from the independent variables or adding temperature factors can both improve the prediction ability of the device in different temperature environments. The model established by incorporating the temperature factor produced the most accurate predictions, with a predicted correlation coefficient, root mean square error of prediction, and ratio of standard deviation to RMSEP of 0.871, 0.402%, and 2.038, respectively.

Automated summary

A portable detection device for apple fruit soluble solids content (SSC) was developed, which can be used in different temperature environments. Both independent modeling and incorporating temperature factors into the model can improve the accuracy of predicting apple fruit SSC in different temperature ranges. The d-value curve method can reduce the impact of temperature on model accuracy to a certain extent.