Abstract
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.
Highlights
The almond (Prunus dulcis, Syn.) originated in Central Asia
The aim of this study was to develop an industrial or large-scale application of HSI technology combined with multivariate classification partial least square discrimination analysis (PLS-DA) to distinguish between almond and apricot kernels based on Near Infrared (NIR) hyperspectral imaging in a real-time sorting system
The industrial application of the near-infrared hyperspectral imaging (NIR-HSI) system combined with multivariate analysis demonstrated the classification of almonds adulterated with apricot kernels
Summary
The almond (Prunus dulcis, Syn.) originated in Central Asia. It was later cultivated in a variety of countries and regions, such as the Mediterranean Basin, a large area of the Middle East, SouthwesternAppl. The almond (Prunus dulcis, Syn.) originated in Central Asia. It was later cultivated in a variety of countries and regions, such as the Mediterranean Basin, a large area of the Middle East, Southwestern. The USA, as well as in Australia [1]. According to reports in the years of 2014–2015, the total almond production based on kernels was 1077.00 metric tons (MT) worldwide, with 77.43% of almonds being produced in the USA, followed by Australia with 7.8% and Spain with 4.4%, as reported by the International Nut and Dried Fruit Council [2]. Almonds are the most widely produced [3]. Almonds are used for different purposes across the world, for instance, as a snack food, in milk production, in bakeries, and as a diet supplement [4]
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