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NIR-Based Sensing Coupled with Physical/Color Features to Identify Brix Level of Florida Citrus

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  026037,  2002 ASAE Annual Meeting . (doi: 10.13031/2013.9399) @2002
Authors:   William M. Miller, Manuela Zude
Keywords:   Citrus, fruit quality, instrumentation

Preliminary tests were undertaken to assess the accuracy of near infra-red (NIR) technologies to measure Brix level of intact Florida grapefruit and Honey tangerines. Two units were utilized: (1) an on-line prototype unit (Mitsui QScope, Sumitomo Metal Mining Co., Tokyo, Japan) and (2) an hand-held unit developed for general fresh fruit quality assessment (Inst. of Agric. Engr.- Bornim, Potsdam, Germany). The on-line tests were conducted at a rate of ~5 fruit per second. Preconditioned Interior white grapefruit were tested at two temperatures, 5 and 20EC while commercial Indian River red grapefruit were tested at ambient conditions. Comparative measurements were obtained for both on-line and hand-held NIR units with the fruit subsequently juiced to obtain a refractive index measurement of the fruits Brix level. Linear regression relationships were developed between the non-destructive NIR techniques and the laboratory Brix measurements. The highest correlation, r2 = 0.67**, was found for Indian River red grapefruit of the smaller size of fruit tested (9.5-11.0 cm diameter). Percent correct classification analysis was ascertained at 9EBrix for Interior grapefruit and 10EBrix for Indian River red grapefruit. Higher classification rates were obtained for ambient temperature tests at ~20EC and for the smaller size of Indian River red grapefruit. A neural network (NN) model with combined inputs of physical and color attributes, plus predicted Brix, was evaluated. Corresponding Brix data for Honey tangerine also were obtained utilizing a handheld NIR probe. Accuracy and repeatability were measured for grapefruit only in a second year test program. Percent correct classification averaged ~90% for 10oBrix and ~80% for a 11oBrix setpoint.

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