Identification of monofloral honey using voltammetric electronic tongue

Quality assessment of honey is often related to its floral origin which is a complex task to evaluate. Traditional technique of floral assessment is made by melissopalynological method. However, this method is quite time consuming and also often operator dependent. Thus, the fallout is a large range of error in interpretation of the result and hence there is considerable demand for instrumental methods to assess the identification of pollen in honey. In this pursuit, an electronic tongue based on cyclic voltammetry is developed to discriminate honey samples based on their floral types and is described in this paper. The technique has been investigated using platinum as the working electrode and the resultant current from the potentiostat has been considered for data analysis. The use of principal component analysis (PCA) and linear discriminate analysis (LDA) proves to be useful in clustering honey samples. Finally, classification performances are investigated using back-propagation multilayer perceptron (BP-MLP) and radial basis function (RBF) neural network models for identification of different floral origin of honey.     

Classification of black tea liquor using cyclic voltammetry

Tea quality evaluation is a complex task and is carried out qualitatively in the industry by experienced tea tasters. But, the unpredictable and inconsistent nature of human panel tasting demands instrumental methods to assess the quality of black tea in an objective manner. For discrimination between different black tea samples and instrumental evaluation of their quality, a new method employing the principle of cyclic voltammetry is proposed in this paper. The technique has been investigated using platinum and glassy carbon as working electrodes and the resultant current from the potentiostat has been considered for data analysis. First, principal component analysis (PCA) and linear discriminant analysis (LDA) has been performed for visualization of underlying clusters and finally, a neural network model has been used to classify the data. The performance of the classifier has been established using 10-fold cross-validation method.     

Monitoring of black tea fermentation process using electronic nose

Black tea fermentation is essentially an oxidation process. After the plucked tea leaves are treated by series of processes called withering (removal of moisture by air flow), pre-conditioning and CTC (essentially maceration and cutting of leaves), the leaves are subjected to the process of fermentation by exposing them to air by laying the cut tea leaves on floor, trough or moving conveyor under controlled temperature, humidity and air-flow conditions. During this process, the leaves change colour from green to coppery brown and the grassy smell gets transformed to floral smell. It is critical that the leaves be allowed to ferment only up to the desired limit and both under and over fermentation result in deteriorated quality of black tea. Out of the two detectable parameters (colour and smell), smell is very important since a strong, very specific fragrance emanates from the leaves once leaves are optimally fermented. A new electronic nose-based approach for monitoring of tea aroma during fermentation is proposed. Two methods namely the 2-Norm method (2NM) and the Mahalanobis distance method (MDM) were tested and the results were correlated with the results of colorimetric tests and human expert evaluation.     

Binary genetic algorithm-based pattern LUT for grayscale digital half-toning

Grayscale digital half-toning is a popular technique to reproduce grayscale images with devices that can support only two levels at output, i.e., black and white. Printers, LCD displays, etc. are some common examples of such devices. Considering 0 and 1 as black and white, respectively, this can be represented as an image-wise binary pattern generation process. The binary patterns are aimed to retain the local tonal and structural characteristics of grayscale image for a faithful illusion of the original grayscale image. Apart from tonal and structural characteristics retention, desired blue-noise characteristics also contribute significantly toward eye pleasant appearance of half-tone images. The paper presents a binary genetic algorithm-based approach to generate such binary patterns through optimizing randomly generated binary strings against a visual cost function. Paper also presents a pattern look-up-table (LUT)-based approach toward conventional clustered dot ordered dithering which is suitable for devices like laser or offset printers that cannot recognize individual pixels. The pattern LUT approach is driven toward green-noise characteristics instead of the blue-noise characteristics. The results obtained with test images are presented pictorially and evaluated through half-tone quality evaluation metrics. The evaluation results and comparison with state-of-art techniques shows the potential of presented technique for practical implementations.     

Instrumental testing of tea by combining the responses of electronic nose and tongue

In the tea industry, experienced tea tasters are employed for evaluation of tea quality and gradation of tea is done on the basis of their scores. This subjective method of assessment has numerous problems like inaccuracy and non-repeatability. Electronic nose and electronic tongue systems are recently being used for measurement of odor and taste of tea samples. As the senses of smell and taste are not independent, and both are interacting, the measured data from the individual systems are combined in this paper for improved estimation of black tea quality. It is found that for the combined system, both the clustering and classification rates improve when compared to the individual systems. With radial basis function neural network, the classification rate increases up to 93%, whereas with the independent systems, the classification rate obtained is 83–84% with electronic nose and 85–86% with electronic tongue.     

Application of Near-Infrared Spectroscopy for the Detection of Metanil Yellow in Turmeric Powder

Turmeric (Curcumina Longa) is a globally traded commodity which is subjected to economically motivated chemically unsafe adulteration, namely metanil yellow. In this work, we report a simplistic and convenient approach to find the adulteration of turmeric with metanil yellow by near-infrared (NIR) spectroscopy coupled with chemometrics. Pure turmeric sample was prepared in the laboratory and spiked with different concentrations of metanil yellow. The reflectance spectra of 248 pure turmeric, metanil yellow, and adulterated samples (1–25%) (w/w) were collected using NIR spectroscopy. The calibration models based on NIR spectra of 144 samples were built for two different regression models, principal component analysis (PCR), and partial least square (PLSR) methods. Another 72 samples were used for external validation. The coefficient of determination (R ²) and root mean square error of calibration for validation and prediction were found to be 0.96–0.99, 0.44–0.91, respectively, for most of the results depending upon different pre-processing techniques and mathematical models used. The original reflectance spectra, the 1st derivative plot, the plot of PLSR regression coefficient (β), and the first three principal component loadings revealed metanil-related absorption regions. To verify the robustness of the models, the figures of merit (FOM) of the models were calculated with the help of net analyte signal (NAS) theory. Overall, it was found that PLSR yielded superior results as compared to the PCR technique. These methods can be applied to other spices also to detect the adulteration rapidly and without any prior sample preparations and with low cost.     

Experimental study of diethyl ether addition on the performance and emissions of a diesel engine

Recently investigations were carried out on the utilisation of light fraction pyrolysis oil (LFPO) in diesel engine, which was obtained from a tyre recycling plant. The 40LFPO blend, which comprised 40% LFPO and 60% diesel composition gave better performance and lower emissions than the blends containing 20LFPO, 60LFPO and 80LFPO. The ignition delays of the blends were longer than that of diesel fuel, because of their lower cetane numbers. The aim of this investigation was to study effect the adding small quantities of Diethyl ether (DEE) whose cetane number is 125, to 40LFPO on the engine behaviour in terms of performance parameters and exhaust emissions. The percentage of DEE was varied from 1% to 4% in steps of 1% on a volume basis. The results of the performance and emission parameters of the engine run on the 40LFPO-DEE blends were evaluated, compared with the diesel operation of the same engine and presented in this article.     

Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Grayscale digital halftoning produces bi-level representation of original continuous tone images. This process plays pivotal role for devices like printers, plasma panels, LCD displays, etc. The bi-level images can be considered as binary images where ‘0’ and ‘1’ correspond to black and white, respectively. This paper investigates potential of binary particle swarm optimization (BPSO) to generate faithful binary halftone patterns. The cost function addresses important characteristics of original images and pleasant visual appearance of halftone images. The paper also shows the application of pattern look-up-table (p-LUT) approach to address the high processing time of BPSO optimization and simple gradient-based edge enhancement for improved edge retention. Results are evaluated subjectively by statistical measures and psychovisual test. Results are evaluated objectively using image quality evaluation metrics as well. The comparisons with state-of-the-art techniques are also drawn. The evaluation results along with the comparisons show the competitive potential of the presented technique.     

FT-NIR spectroscopy coupled with multivariate analysis for detection of starch adulteration in turmeric powder

This paper investigates the feasibility of Fourier transform near-infrared (FT-NIR) spectroscopy, a fast and easy method based on chemometric methods to detect corn starch illegally added to turmeric powder. In this work, the pure turmeric powders were blended with corn starch to generate different concentrations (1–30%)(w/w) of starch-adulterated turmeric samples. The reflectance spectra of total of 224 samples were taken by FT-NIR spectroscopy. The exploratory data analysis was done by principal component analysis (PCA). The starch related peaks were selected by variable importance in projection (VIP) method and were explored by examination of original reflectance spectra, 1^st derivative spectra, PCA loadings and β coefficients plot of the partial least square regression (PLSR) model. The coefficient of determination (R²) and root-mean-square error of partial least square regression (PLSR) models were found to be 0.91–0.99 and 0.23–1.3%, respectively, depending on the pre-processing techniques of spectral data. The figure of merit (FOM) of the model was found with the help of net analyte signal (NAS) theory.