Wheat (Triticum spp.) is a widely grown cereal crop and is one of the most important staple foods internationally. Grading is an imperative step in wheat production to ensure that the grains are of acceptable quality and safety for food processing and consumption. Current wheat grading practices are performed manually which is tedious, time-consuming, and subjective. This study aimed to investigate the feasibility of NIR hyperspectral imaging (HSI) and chemometrics to discriminate sound wheat from four common defects. Defective wheat included heat-damaged, Fusarium-damaged, sprout-damaged, and immature kernels. A wide variety of pre-processing techniques and classification algorithms (logistic regression, partial least squares-discriminant analysis, linear discriminant analysis, k-nearest neighbours, decision trees, random forests and support vector machines) were evaluated for both two-way and multiclass analyses. For the two-way classifications, accuracies between 98.6% and 100% were achieved for each defective category. The multiclass analyses had a decreased performance, where the best model attained an accuracy of 84.6%. Given that agricultural products vary significantly in inherent characteristics due to genetics, cultivation practices, handling and storage, the overall results achieved are highly successful. This study shows that HSI is capable of effectively discriminating sound wheat from common occurring defects, offering a promising alternative grading technique to the cereal industry.
In this study, NIR hyperspectral imaging and multivariate data analysis were used to classify game meat species, namely Springbok (Antidorcas marsupialis) and Blesbok (Damaliscus pygargus phillipsi). The animals (6 blesbok and 6 springbok) were harvested in Witsand and Elandsberg in the Western Cape, South Africa. Longissimus thoracis et lumborum (LTL) muscles were excised and left to bloom for ca. 30 minutes prior to imaging. Thereafter, the moisture was wiped off the surface to avoid specular reflectance. NIR hyperspectral images were collected with a linescan system (HySpex SWIR-384) in the spectral range 950 – 2500 nm. Data were pre-processed with Savitzky-Golay smoothing and derivatives (2nd order polynomial, 2nd derivative, 15 point smoothing) and noisy regions in the spectra, specifically between 1884.9nm -2500nm, were removed. In addition, two data analysis methods, the pixel and object wise approach, were evaluated. In the object wise approach the muscles were segmented into ca 2 cm ROI’s, of which the mean was computed. For both pixel and object wise approaches, there was no distinct separation of the species with PCA. When PLS-DA models were developed, the object wise approach proved to be superior with a classification accuracy of 96%, whereas that of the pixel wise approach was 62%. It is evident that NIR hyperspectral imaging can be used to distinguish between the two species, with the object wise being the optimal option of the two approaches, as it represents the mean spectra of each object.
Fungal pathogens constitute the greatest economical concern to corn farmers; they result in yield losses, grain quality reduction and production of mycotoxins. Improvement of detection methods are imperative. This work aimed to examine corn fungal pathogens with HSI.
Isolates of Fusarium spp. and Stenocarpella spp., were plated on growth media in glass Petri dishes in triplicate, and incubated at 25°C for 9 days. Images were acquired with a SisuChema short-wave infrared pushbroom imaging system in the spectral range 920 – 2514 nm. Principal component analysis (PCA), with various pre-processing methods, and multivariate curve resolution (MCR) were used to explore the data.
PCA with or without pre-processing, revealed chemical differences within and between fungal isolates. Differences were amplified with time. Examination of the mean spectra and PC loadings after spectral pre-treatment indicated variation primarily around bands associated with water/moisture (1450 & 1930 nm), protein (2180 & 2242 nm) and carbohydrates/starch (1090, 1360 & 2100 nm). This is expected since fungi are mainly comprised of these constituents and as the mycelium grows and ages, there is a change in carbohydrate (content or structure), moisture and protein. This was apparent in higher order components (PCs 4-6) and appeared as textured information.
MCR revealed similar results, however the concentration maps were clearer than PCA score images. In addition, these maps were textured illustrating the physical changes of the mycelium with time. These were due to the growing hyphae and possible spore formation. In addition, it is likely that these concentration maps indicated presence of mycotoxins.
Conference Committee Involvement (2)
Sensing for Agriculture and Food Quality and Safety XIV
3 April 2022 | Orlando, Florida, United States
Sensing for Agriculture and Food Quality and Safety XIII
12 April 2021 | Online Only, Florida, United States