A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 2. Design Factors for Optimal Interactance Measurement Setup

• Accepted : 2012.06.29
• Published : 2012.06.30
• 42 22

Abstract

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

Keywords

Firmness;Interactance configuration;Muskmelon;Soluble solids content (SSC);VIS/ NIR spectroscopy

References

1. Nicolai, B. M., K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron and J. Lammertyn. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology 46: 99-118. https://doi.org/10.1016/j.postharvbio.2007.06.024
2. Penchaiya, P., E. Bobelyn, B. E. Verlinden, B. M. Nicolai and W. Saeys. 2009. Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy. Journal of Food Engineering 94: 267-273. https://doi.org/10.1016/j.jfoodeng.2009.03.018
3. Schaare, P. N. and D. G. Fraser. 2000. Comparison of reflectance, interactance and transmission modes of visiblenear infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biology and Technology 20:175-184. https://doi.org/10.1016/S0925-5214(00)00130-7
4. Suh, S. R., K. H. Lee, S. H. Yu, H. S. Shin, Y. S. Choi and S. N. Yoo. 2012. A melon fruit grading machine using a miniature VIS/NIR spectrometer: 1. Calibration models for the prediction of soluble solids content and firmness. Journal of Biosystems Engineering 37(3):166-176. https://doi.org/10.5307/JBE.2012.37.3.166
5. Suh, S. R., K. H. Lee, S. H. Yu, S. N. Yoo and Y. S. Choi. 2011. Comparison of performance of measuring of VIS/NIR spectroscopic spectrum to predict soluble solids content and 'Shingo' pear. Journal of Biosystems Engineering 36(2):130-139 (In Korean, with English abstract). https://doi.org/10.5307/JBE.2011.36.2.130
6. Sun, T., K. Huang, H. Xu and Y. Ying. 2010. Research advances in nondestructive determination of internal quality in watermelon/melon: A review. Journal of Food Engineering 100:569-577. https://doi.org/10.1016/j.jfoodeng.2010.05.019
7. Zude, M., B. Herold, J. M. Roger, V. Bellon-Maurel and S. Landahl. 2006. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering 77:254-260. https://doi.org/10.1016/j.jfoodeng.2005.06.027

Cited by

1. A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 1. Calibration Models for the Prediction of Soluble Solids Content and Firmness vol.37, pp.3, 2012, https://doi.org/10.5307/JBE.2012.37.3.166
2. Prediction of Soluble Solids Content of Chestnut using VIS/NIR Spectroscopy vol.38, pp.3, 2013, https://doi.org/10.5307/JBE.2013.38.3.185
3. Near-infrared light penetration depth analysis inside melon with thick peel by a novel strategy of slicing combining with least square fitting method pp.01458876, 2018, https://doi.org/10.1111/jfpe.12886
4. The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy pp.1936-976X, 2018, https://doi.org/10.1007/s12161-018-1346-3

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)