Abstract
The soluble solids content (SSC) is a key parameter to affect the quality of Korla fragrant pears. Having reliable and rapid measurement of the SSC is crucial for producers and technicians. However, the equipment necessary to meet these requirements is frequently complex and expensive. This paper developed a portable nondestructive SSC detector, primarily composed of a handheld near-infrared (NIR) spectrometer in the wavelength of 900–1700 nm, a display, a lithium battery, and a Raspberry Pi board. In addition, the whole detector was only about 425 g in total. To test the performance of the prototype, we chose 100 Korla fragrant pears as our fruit samples. Different spectral pre-processing methods were combined with principal component regression (PCR) and partial least squares regression (PLSR) to obtain accurate models. To further simplify the model, the synergy interval (Si), genetic algorithm (GA), and random frog (RF) were used to choose characteristic wavelengths. Laboratory tests show that compared with PLSR and PCR based on the full-spectrum, the prediction models obtained after conducting RF selection have yielded satisfactory results. The number of wavelength features was reduced from 228 to 10, and the R2 of the model was improved from 0.942 to 0.966. The mean relative error rate in the field test was 1.41%, indicating that the developed SSC detector was also effective in the field. It can be verified that the NIR diffuse reflectance spectroscopy is a reliable tool for the nondestructive measurement of SSC in Korla fragrant pears. The appropriate pretreatment methods and selection of characteristic wavelengths can increase NIR spectroscopy accuracy in actual measurement.
Published Version
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