A portable NIR system for nondestructive assessment of SSC and firmness of Nanguo pears

An accurate, nondestructive, and low-cost measurement system was developed using a portable near-infrared (NIR) spectrometer (DLP NlRscan Nano), a Raspberry Pi board, a display, a lithium battery, and a self-made three-dimensional printed shell. NIR data were collected through two measurement modes (column and Hadamard transform) based on digital light processing. With this equipment, detection models of soluble solid content (SSC) and firmness, essential quality indicators of the fruit, were established via quantitative analysis using chemometrics and a hybrid wavelength selection strategy. The SSC and firmness prediction model established through the combination of the synergy interval partial least squares and genetic algorithm (Si-GA-PLS) showed higher prediction accuracy, with coefficient of determination of prediction (R-P(2)) values of 0.9406 and 0.9119, respectively, and mot-mean-square error of prediction (RMSEP) values of 0.1655 and 5.5003, respectively. A comparison of the model performance of different monochromator principles was also explored; they were found to be non-statistically significant differences from one another. Finally, data fusion was used to improve prediction ability. The results obtained by mid-level data fusion presented a better performance than using models based on one technique. Overall, the developed novel handheld detector exhibits potential for smart software applications with high accuracy.

Automated summary

A portable near-infrared spectrometer, Raspberry Pi board, display, lithium battery, and 3D printed shell were used to develop an accurate, nondestructive, and low-cost measurement system. Detection models for fruit quality indicators were established using chemometrics and a hybrid wavelength selection strategy, and data fusion improved prediction ability.