V. Chelladurai

Detection of Sprout-Damaged Wheat Using Thermal Imaging

Sprout-damaged wheat affects the quality of flour resulting in poor qualities of bread, pasta, cookies, or any other product prepared from the wheat. The most common methods to determine sprout-damaged kernels include visual inspection, falling number, and rapid visco analyzer. These methods are either subjective or destructive and are time consuming. We tested the use of thermal imaging to detect sprout damage based on heat radiated by healthy and sprouted wheat kernels. An infrared thermal camera was used to collect images of healthy and sprout-damaged kernels and the images were analyzed using Matlab. Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Artificial Neural Network (ANN) were used to classify healthy and sprout-damaged kernels. The classification accuracies were: for LDA 88.2% and 98.1%, for QDA 88.7% and 95.1%, and for ANN 99.4% and 91.7%, respectively, for healthy and sprout-damaged kernels. The results have shown that thermal imaging has a potential to determine sprout-damaged wheat kernels from the healthy kernels.

Effect of microwave treatment on the cooking and macronutrient qualities of pulses

ABSTRACT The effect of microwave treatment to reduce the cooking times of five pulses, namely red lentil, chickpea, pigeon pea, mung bean, and pinto bean, were determined in this study. Pulses from 10 to 18% moisture contents were treated using 400 to 600 W microwaves for 14 to 56 s. The cooking times of microwave-treated pulses were significantly lower than that of the control samples. The lowest cooking time was observed for 18% moisture content chickpea and pigeon pea treated with 600 W for 56 s. The Fourier transform mid-infrared spectra in both lipids and fingerprint regions showed the macronutrients differences among the five pulses. Major changes were observed in the amide I region of microwave treated pulses. This effect of microwave treatment was higher in red lentil, chickpea, and mung bean than in pigeon pea and pinto bean at 10% moisture content. At 18% moisture content, the change of β-sheets to aggregates was observed in all pulses due to microwave treatment.

MEASUREMENT OF THERMAL PROPERTIES OF MUNG BEAN (VIGNA RADIATA)

The thermal properties of mung bean (Vigna radiata) are essential for designing postharvest handling equipment for mung bean processing. The specific heat, thermal conductivity, and thermal diffusivity of mung bean were determined as a function of moisture content and temperature. The specific heat and thermal conductivity were quantified using a specially built vacuum flask calorimeter and thermal conductivity probe, respectively. The specific heat was determined by mixing hot water with a sample maintained at lower temperature and incorporating graphical temperature-correction into the calculation. The thermal conductivity was determined by measuring the temperature response of the sample when heated with a line heat source. The thermal diffusivity was directly calculated from the specific heat and thermal conductivity data. The specific heat and thermal conductivity of mung bean increased linearly from 1.63 to 2.45 kJ kg-1 K-1 and from 0.092 to 0.141 W m-1 K-1, respectively, with the corresponding increase in moisture content from 9.9% to 18.3% w.b. and temperature from 10°C to 50°C. The thermal diffusivity of mung bean changed from 0.659 × 10-7 to 0.752 × 10-7 m2 s-1 with increase in moisture content and temperature. The thermal diffusivity increased with increase in moisture content and decreased with increase in temperature.

Near-infrared Imaging and Spectroscopy

In the electromagnetic spectrum, near infrared (NIR) region covers between 780 nm to 2500 nm. The developments of new NIR techniques like NIR imaging (NIR cameras, NIR hyperspectral imaging systems), Fourier Transform (FT)-NIR spectroscopy, NIR microscopes, and NIR thermal cameras have extended the application of near infrared band dramatically in the last 3 decades, because some of these techniques give spectral as well as spatial data which help to analyze chemical constituents as well as physical and textural parameters of a sample. When an object is illuminated with light, it absorbs, reflects and transmits light at various proportions based on the physical and chemical properties of object. In near infrared imaging systems, this absorbed, transmitted or reflected radiation only at NIR waveband is captured using a NIR detector or sensor. In NIR hyperspectral imaging technique, the object is imaged over a large number of spectral bands and complete reflectance spectrum with spatial (imaging) data are collected. NIR spectroscopy techniques yields only spectral data, and in Fourier transform-near infrared, Fourier transform is applied to convert the raw data into original spectrum. Nowadays, near-infrared imaging and spectroscopy techniques are commercially used for measurement of moisture and other chemical constituents of the cereal grains and oilseeds in grain industry. Meat industry has also started using NIR techniques for non-destructive quality monitoring. The development of multispectral imaging systems based on the indented use, and developments in analysis techniques will help the agricultural and food industry in implementing the NIR imaging and spectroscopy systems for rapid and in-line quality monitoring applications like foreign material detection, discrimination of agricultural and food products based on quality attributes and detection of defects, diseases, and food adulteration.