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Article

Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds

1
Department of Biosystems Machinery Engineering, College of Agriculture and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
2
Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea
3
Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA
4
Department of Smart Agriculture System, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(18), 6569; https://doi.org/10.3390/app10186569
Received: 18 August 2020 / Revised: 16 September 2020 / Accepted: 18 September 2020 / Published: 20 September 2020
Almonds are nutrient-rich nuts. Due to their high level of consumption and relatively high price, their production is targeted for illegal practices, with the intention of earning more profit. The most common adulterants are based on superficial matching, and as an adulterant, the apricot kernel is comparatively inexpensive and almost identical in color, texture, odor, and other physicochemical characteristics to almonds. In this study, a near-infrared hyperspectral imaging (NIR-HSI) system in the wavelength range of 900–1700 nm synchronized with a conveyor belt was used for the online detection of added apricot kernels in almonds. A total of 448 samples from different varieties of almonds and apricot kernels (112 × 4) were scanned while the samples moved on the conveyor belt. The spectral data were extracted from each imaged nut and used to develop a partial least square discrimination analysis (PLS-DA) model coupled with different preprocessing techniques. The PLS-DA model displayed over a 97% accuracy for the validation set. Additionally, the beta coefficient obtained from the developed model was used for pixel-based classification. An image processing algorithm was developed for the chemical mapping of almonds and apricot kernels. Consequently, the obtained model was transferred for the online sorting of seeds. The online classification system feedback had an overall accuracy of 85% for the classification of nuts. However, the model presented a relatively low accuracy when evaluated in real-time for online application, which might be due to the rough distribution of samples on the conveyor belt, high speed, delaying time in suction, and lighting variations. Nevertheless, the developed online prototype (NIR-HSI) system combined with multivariate analysis exhibits strong potential for the classification of adulterated almonds, and the results indicate that the system can be effectively used for the high-throughput screening of adulterated almond nuts in an industrial environment. View Full-Text
Keywords: hyperspectral imaging; online sorting system; food adulteration; almond; apricot hyperspectral imaging; online sorting system; food adulteration; almond; apricot
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MDPI and ACS Style

Faqeerzada, M.A.; Perez, M.; Lohumi, S.; Lee, H.; Kim, G.; Wakholi, C.; Joshi, R.; Cho, B.-K. Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds. Appl. Sci. 2020, 10, 6569. https://doi.org/10.3390/app10186569

AMA Style

Faqeerzada MA, Perez M, Lohumi S, Lee H, Kim G, Wakholi C, Joshi R, Cho B-K. Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds. Applied Sciences. 2020; 10(18):6569. https://doi.org/10.3390/app10186569

Chicago/Turabian Style

Faqeerzada, Mohammad A., Mukasa Perez, Santosh Lohumi, Hoonsoo Lee, Geonwoo Kim, Collins Wakholi, Rahul Joshi, and Byoung-Kwan Cho. 2020. "Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds" Applied Sciences 10, no. 18: 6569. https://doi.org/10.3390/app10186569

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