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United States Department of Agriculture

Agricultural Research Service

Title: Measuring Fig Quality Using Near-Infrared Spectroscopy

Authors
item Burks, Charles
item Dowell, Floyd
item Xie, F. - KANSAS STATE UNIVERSITY

Submitted to: Journal of Stored Products Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 3, 1999
Publication Date: July 1, 2000
Citation: BURKS, C.S., DOWELL, F.E., XIE, F. MEASURING FIG QUALITY USING NEAR-INFRARED SPECTROSCOPY. JOURNAL OF STORED PRODUCTS RESEARCH. 2000. Vol. 36 Issue 3 289-296.

Interpretive Summary: Inspection of dried figs is a complex and labor-intensive process. Automating inspection or sorting procedures could increase the cost- effectiveness of packinghouse sorting operations and decrease highly seasonal dependence on a small pool of skilled laborers for both sorting and grading operations. We examined the ability of near-infrared spectroscopy to automate sorting and inspection of dried figs. For two fi types, Calimyrna and Adriatic, we examined approximately 100 passable figs and 100 figs each in the infested, rotten, sour, and dirty defect categories. Passable figs were distinguished from defective figs with approximately 90% accuracy, and defective figs were identified to the proper defect category with accuracies of 80 to nearly 100%. Large numbers of wavelengths were used to make these predictions, indicating that using near-infrared spectroscopy to sort dried figs may require an instrument capable of reading many wavelengths rather than a more economical filter- based instrument.

Technical Abstract: Inspection of dried figs is a complex and labor-intensive process. We examined the potential of using near-infrared spectroscopy (NIRS) to automate sorting and inspection of dried figs. Calimyrna and Adriatic types were inspected by hand using established criteria. For both varieties, approximately 100 passable figs and 100 figs each for the infested, rotten, sour, and dirty defect categories were examined using NIRS and partial least-squares regression (PLS). Correct classifications for these varieties ranged from 83 to 100%. About twenty PLS factors were used to make the predictions. These observations indicate that the use of NIRS to help automate inspection for dried fig processing is feasible. However, the large number of wavelengths needed for prediction, as indicated by PLS beta co-efficients, indicates that implementing NIRS in fig sorting may require an instrument capable of reading numerous wavelengths rather than a more economical filter-based instrument.

Last Modified: 11/20/2014
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