In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

ABSTRACT
 Introduction
 Materials and Methods
  1 Hardy kiwi sample preparation
  2 Sugar content measurements
  3 Visible and near-infrared spectroscopic analysis
  4 Sugar content prediction model
 Results and Discussion
  1 Optimal pre-processing for hardy kiwi sugar contentprediction
  2 The effect of spectral range and sampling intervalon hardy kiwi sugar content prediction
 References

• Sangyoon Lee(Department of Bio-Industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Shagor Sarkar(Department of Bio-Industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science))
• Youngki Park(Forest Medicinal Resources Research Center, National Institute of Forest Science)
• Jaekyeong Yang(Department of Environmental Materials Science, Gyeongsang National University (Institute of Agriculture and Life Science))
• Giyoung Kweon(Department of Bio-Industrial Machinery Engineering, Gyeongsang National University (Institute of Agriculture and Life Science)) Corresponding author