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

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 (Rcv 2 ) values of the cross-validation were 0.717 (standard error of prediction, SEP=1.16 °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 Rcv 2 values were 0.736 (SEP=0.87 °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 Rcv 2 of 0.713 (SEP=0.88 °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.

A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 1. Calibration Models for the Prediction of Soluble Solids Content and Firmness

“…This paper mainly focused on the VIS/NIR spectral data acquisition system and the calibration models using the data sets measured in the interactance mode. Some factors to be considered in designing a melon fruit grading machine using the interactance mode of VIS/NIR spectroscopy technology are discussed in the other paper (Suh et al, 2012).…”

Fruit Size and Placement in Packaging Affect Firmness-Related Quality Attributes of Muskmelon (Cucumis melo L.) Fruit

“…In Korea, melon crops are generally grown during the winter-spring season in greenhouses where environmental conditions for the crops are precisely controlled with high energy input (Suh et al, 2012). The high production cost of melons and the consumer's preference for high-quality fruits demand a relevant postharvest packaging system.…”

Prediction of Soluble Solids Content of Chestnut using VIS/NIR Spectroscopy

“…NIR spectroscopy was first used in agricultural applications by Norris and Hart (1965) to measure moisture in grain. Since then it has been used for rapid analysis of mainly moisture, protein and fat contents of a wide variety of agricultural and food products (Gunasekaran, 2001;Suh et al, 2012). Early applications in horticulture focused on dry matter content of onions (Birth et al, 1985), soluble solids content (SSC) of apples (Zude et al, 2006) and water content of mushrooms (Roy et al, 1993), but since then many other applications have followed.…”