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Hyperspectral imaging for non-destructive prediction of total nitrogen concentration in almond kernels

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Gama, T., Wallace, H. M., Trueman, S. J., Tahmasbian, I. and Bai, S. H. (2017) Hyperspectral imaging for non-destructive prediction of total nitrogen concentration in almond kernels. Acta Horticulturae, 1219 . pp. 259-264. ISSN 9462612161

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Article Link: https://doi.org/10.17660/ActaHortic.2018.1219.40

Abstract

There is increasing awareness of the need to consume high-quality foods because of health concerns. Food safety and health awareness campaigns have provided an impetus for non-destructive and real-time methods for food quality assessment. Total nitrogen is used as an indicator of crude protein content in foods and we examined the potential of hyperspectral imaging to predict total nitrogen concentration in four brands of almonds purchased from commercial retailers. A hyperspectral imaging system in the wavelength range 400-1000 nm was used in the study. A partial linear squares regression (PLSR) model was developed, which predicted total nitrogen concentration with a determination coefficient (R2p) of 0.82 and a root mean error square of calibration (RMSEC) of 0.16. These results indicated that hyperspectral imaging has great potential to predict total nitrogen concentration of almond kernels.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Animal Science
Additional Information:VII International Symposium on Almonds and Pistachios
Keywords:almond, crude protein, food quality, nutritional composition, nuts, rapid assessment
Subjects:Agriculture > Agriculture (General) > Special aspects of agriculture as a whole > Inventions
Agriculture > Agriculture (General) > Agricultural chemistry. Agricultural chemicals
Plant culture > Food crops
Plant culture > Fruit and fruit culture > Nuts
Live Archive:27 May 2024 05:35
Last Modified:27 May 2024 05:35

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