Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

Abstract

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and compared with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

“…These indicated that for the two individual models, the limit of detection was 0.5%, while for the general model, the limit of detection was 1%. Mishra et al [3,32,33] studied the feasibility of the NIR HSI technique combined with principal component analysis (PCA), spectral band math, or independent component analysis (ICA) to detect peanut, hazelnut, and walnut particles (particle size of 1000-500 um) in wheat flour (particle size of 125-100 um and 212-160 um). The results of their Performance of the best PLSR models for (a) peanut-contaminated flour and (b) walnut-contaminated flour applied on prediction sets based on full spectra (an enlarged view of the green circle part was shown in the green pane).…”

Near Infrared Spectra and Hyperspectral Imaging of Medieval Fortress Walls in Carcassonne: A Comprehensive Interdisciplinary Field Study

“…It measured from 1000 nm to 2500 nm in 256 channels. 2 Linear movement was made possible by a computer-controlled stepping motor moving on a rail (Figure 4). The targetto-camera distance was 1 m so that the scanned width was about 25 cm and the size of each pixel was 408 × 261 μm.…”

NIR hyperspectral imaging for animal feed ingredient applications

“…Recent studies conducted on wheat and cereal flours used NIR hyperspectral imaging for detection of talcum powder (Fu et al, 2020), benzonyl peroxide (Fu et al, 2020), peanuts (Laborde et al, 2020;Zhao et al, 2018;Mishra et al, 2016;Mishra et al, 2015) and tree nuts (Zhao et al, 2018;Mishra et al, 2015), all of which may cause serious health risks to humans. In one of these studies, a SWIR push-broom HSI system (1000 to 2500 nm spectral range) was used along with preprocessing and independent component analysis (ICA) (Mishra et al, 2016).…”