A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 1. Calibration Models for the Prediction of Soluble Solids Content and Firmness
2012
Suh, S.R., Chonnam National University, Gwangju, Republic of Korea | Lee, K.H., Chonnam National University, Gwangju, Republic of Korea | Yu, S.H., Chonnam National University, Gwangju, Republic of Korea | Shin, H.S., Chonnam National University, Gwangju, Republic of Korea | Choi, Y.S., Chonnam National University, Gwangju, Republic of Korea | Yoo, S.N., Chonnam National University, Gwangju, Republic of Korea
Purpose: This study was conducted to investigate the potential of interactance mode of NIR spectroscopy technology for the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from local greenhouses in three different harvesting seasons (experiments 1, 2, and 3). The fruit attributes were measured at the 6 points on an equator of each sample where the spectral data were collected. The prediction models were developed using the original spectral data and the spectral data sets preprocessed by 20 methods. The performance of the models was compared. Results: In the prediction of SSC, the highest coefficient of determination (Rcv²) values of the cross-validation was 0.755 (standard error of prediction, SEP=0.89°Brix) with the preprocessing of normalization with range in experiment 1. The highest coefficient of determination in the robustness tests, Rrt²=0.650 (SEP=1.03 °Brix), was found when the best model of experiment 3 was evaluated with the data set of experiment 2. The best Rcv² for the prediction of firmness was 0.715 (SEP=3.63 N) when no preprocessing was applied in experiment 1. The highest Rrt² was 0.404 (SEP=5.30 N) when the best model of experiment 3 was applied to the data set of experiment 1. Conclusions: From the test results, it can be concluded that the interactance mode of VIS/NIR spectroscopy technology has a great potential to measure SSC and firmness of thick-skinned muskmelons.
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