C. B. Singh

Fungal Detection in Wheat Using Near-Infrared Hyperspectral Imaging

Different species of fungi infect grain in the field and storage facilities. Contamination by fungi in grain is detected and quantified by traditional methods, such as microbial incubation and microscopic detection, which are subjective, labor intensive, and time consuming. An accurate and timely detection technique for fungal growth in grain is needed to prevent grain from spoiling and to reduce quality loss. In this study, the potential of near-infrared hyperspectral imaging to detect fungal infection in wheat was investigated. Wheat kernels infected with storage fungi, namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger, were scanned using a hyperspectral imaging system, and a total of 20 image slices at evenly spaced wavelengths between 1000 to 1600 nm were acquired to form a hypercube. A multivariate image analysis (MIA) technique based on principal component analysis (PCA) was used to reduce the dimensionality of the image hypercubes. Two-class and four-class classification models were developed by applying k-means clustering and discriminant (linear, quadratic, and Mahalanobis) analyses. Two-class discriminant classification models gave maximum classification accuracy of 100%, and on average 97.8% infected kernels were correctly classified by the linear discriminant classifier. The four-class linear discriminant classifier correctly classified more than 95% of the kernels infected with Penicillium and 91.7% healthy kernels. However, the discriminant classifiers misclassified the kernels infected with A. niger and A. glaucus.

Detection of Sprouted and Midge-Damaged Wheat Kernels Using Near-Infrared Hyperspectral Imaging

ABSTRACT Sprout damage which results in poor breadmaking quality due to enzymatic activity of α-amylase is one of the important grading factors of wheat in Canada. Potential of near-infrared (NIR) hyperspectral imaging was investigated to detect sprouting of wheat kernels. Artificially sprouted, midge-damaged, and healthy wheat kernels were scanned using NIR hyperspectral imaging system in the range of 1000–1600 nm at 60 evenly distributed wavelengths. Multivariate image analysis (MVI) technique based on principal components analysis (PCA) was applied to reduce the dimensionality of the hyperspectral data. Three wavelengths 1101.7, 1132.2, and 1305.1 nm were identified as significant and used in analysis. Statistical discriminant classifiers (linear, quadratic, and Mahalanobis) were used to classify sprouted, midge-damaged, and healthy wheat kernels. The discriminant classifiers gave maximum accuracy of 98.3 and 100% for classifying healthy and damaged kernels, respectively.

Effect of Mercury on Photosynthesis in Nostoc calcicola: Role of ATP and Interacting Heavy Metal Ions

Summary Nostoc calcicola cells pre-exposed to Hg 2+ (0.20 and 0.25 AM) showed 50% inhibition of photosynthetic 0 2 -evolution and 14 CO 2 -uptake, respectively. Hg 2+ in combinations with either Cd 2+ or CH 3 Hg + , interacted synergistically, in contrast to antagonism with either Ni 2+ t or Cu 2+ . It is suggested that: (a) Hg 2+ inhibited photosynthesis at the very initial stage; (b) photosynthetic 0 2 -evolution is more sensitive to Hg 2+ -stress than 14 CO 2 -uptake, and (c) Hg 2+ ions predominantly attack the action sites of Mn 2+ and ATP generating steps of photosynthesis in the cyanobacterium.

Fungal Damage Detection in Wheat Using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging

Healthy and fungal-damaged wheat kernels infected by the species of storage fungi, namely Penicillium spp., Aspergillus glaucus, and A. niger, were scanned using a short-wave near-infrared hyperspectral imaging system in the 700–1100 nm wavelength range and an area scan colour camera. A multivariate image analysis was used to reduce the dimensionality of the hyperspectral data and to select the significant wavelength using principal component analysis. Wavelength 870 nm, which corresponded to the highest factor loading of first principal component, was considered to be significant. Statistical and histogram features from the 870 nm wavelength image were selected and used as input to statistical discriminant classifiers (linear, quadratic, and Mahalanobis). From the colour images, a total of 179 features (123 colour and 56 textural) were extracted and the top features selected from these features were used as input to the statistical classifiers. The linear discriminant analysis classifier correctly classified 97.3–100.0% healthy and fungal-infected wheat kernels, using the combined hyperspectral image features and the top ten features selected from 179 colour and textural features of the colour images as input.

An Experimental Study of Wheat Drying in Thin Layer and Mathematical Simulation of a Fixed-Bed Convective Dryer

Drying of wheat (Algerian cultivar: Hadba03) in thin layers was studied and mass flux phenomenon was used to characterize the thin-layer drying process. Thin-layer drying of wheat was determined for drying air temperature range of 40–60°C, relative humidity of drying air from 10 to 30%, air velocity of 0.7 m/s, and initial grain moisture from 26 to 31% (dry basis). Equilibrium moisture content of wheat was determined using desorption isotherms obtained from the thin-layer drying data. An equilibrium model for a stationary deep bed with drying air moving vertically upward was developed using mass and energy balance between grain and drying air in the bed and drying air characteristics obtained from thin-layer drying experiments. The developed model was validated by drying wheat in a laboratory dryer using different drying air temperatures and initial moisture contents.

Cu uptake in a cyanobacterium : fate of selected photochemical reactions

Cu uptake in the diazotrophic cyanobacteriumNostoc calcicola Bréb. was accompanied by inhibitions in the in vivo activities of photosystem (PS) II, PS I,14CO2-fixation, and decline in the ATP pool. Cyanobacterial cells, while saturated for Cu uptake within 1 h at 40 μM Cu, showed more than 50% inhibition of PS II and 95.4% of14CO2 fixation compared with only 15.5% decrease in the PS I activity. The total extractable ATP content also declined by 32.2% within 1 h. In a subsequent follow-up study lasting 72 h, PS II activity and14CO2 fixation showed complete inhibition, in contrast to 34.4% of PS I activity and 4.2% of ATP still remaining unaffected. The results have been discussed in the light of multiple effects of Cu during and subsequent to its uptake by the cyanobacterium.

Simulation of Coupled Heat and Mass Transfer in Granular Porous Media: Application to the Drying of Wheat

A mathematical model was developed to simulate the deep-bed convective drying of Algerian wheat and barley using the characteristics of the selected local varieties. The nonequilibrium model, composed of a system of partial differential equations (PDEs), was solved using temporal and spatial discretization with some simplifying assumptions. The simulated results were compared with experimental data obtained during drying of wheat in deep beds (1-, 5-, and 10-cm depths) in a laboratory dryer under the experimental conditions of 60°C temperature, 10% RH, 0.7 m/s air velocity, and initial grain moisture content of 25.0% (db). The simulation results obtained by the mathematical model were in good agreement with those obtained by experiments carried out on the Algerian wheat.

A Generalized Dimensionless Model for Deep Bed Drying of Paddy

A generalized dimensionless model of paddy drying was developed from a validated partial differential equation (PDE) drying model using the dimensional analysis of Buckingham theorem. This generalized dimensionless model considered all drying parameters in an equation to predict the grain moisture content during the drying process. Statistical parameters, namely, coefficient of determination (R 2), chi-square (χ2), mean relative deviation (MRD), and root mean square error (RMSE), were used as criteria to compare the dimensionless model with a validated PDE model. Based on these calculated parameters, it was concluded that the generalized dimensionless model fitted reasonably well with data from the PDE model and good agreement was found between the generalized dimensionless model and experimental drying data.

Microwave-Assisted Thin Layer Drying of Wheat

Drying characteristics of Canada Western Red Spring (CWRS) wheat were studied using a domestic microwave convective oven. The effects of microwave power level, grain bed thickness, and initial grain moisture on the drying kinetics were investigated. Wheat samples with initial moisture levels of 0.18 to 0.29 kg water/kg of dry matter were dried for different drying periods of 180 to 360 s. The moisture loss data were recorded at regular short intervals. Then moisture loss data were fitted to various models (Page equation, modified drying equation, and Midilli equation) to study the drying kinetics of wheat. The results showed that wheat moisture loss increased with increasing microwave power level. A mathematical model was developed by coupling mass and energy balances, resulting in a system of non-linear equations. The predicted moisture loss data from the developed model were compared by fitting to experimental microwave data that were in good agreement.

Near-infrared spectroscopy: Applications in the grain industry

Grains are among the most important staple foods for the world’s population. There is an increasing demand from consumers for the highest quality of food products and zero-tolerance for grain contamination. Grain quality is affected mainly by moisture content; soundness and vitreousness of the kernels; amount of foreign material; and presence of fungi, insects, and mites. The current visual methods for grain quality estimation are subjective and time consuming. The grain industry is in need of an automated, economical, and rapid means of grain quality estimation. The technique of near-infrared spectroscopy (NIRS) has demonstrated the potential to measure most of the grain quality attributes in real- time and it has been proven to be a fast, reliable, accurate, and economical analytical technique. Though the NIRS technology has been applied for quality analysis of food and bio-material, its real-time applications have been restricted due to complex spectra; highly overlapping, broad, low absorption band and there is a need for detailed calibration. Precise and robust NIRS calibration models can be developed using wavelet transform and artificial neural network (ANN). This paper reviews the applications of NIRS for grain quality evaluation.