Predicting the relative density from on-the-go horizontal penetrometer measurements at some arable top soils in Northern Switzerland

This study sought to develop empirical models to predict soil relative density (ρrel) from measurements of horizontal penetrometer resistance (PR) and soil water content (θg) in a wide range of soil textures. This permits the comparison of the state of soil compactness in different soil textures. It was hypothesised that model coefficients would be texturedependent when soil compactness was expressed as bulk density (ρd) and that a model with constant coefficients could be obtained when soil compactness was expressed in terms of ρrel (obtained as the ratio of ρd to reference bulk density (ρref)). Field measurements were conducted in 2014 using a horizontal penetrometer at 0.25 m depth in 10 fields in Switzerland with a wide range of soil textures covering sandy loam, silt loam, loam, clay loam and clay (clay concentration, (CC) = 153–585 g kg−1 and organic matter concentration, (OM) = 9–168 g kg−1). At selected locations along the penetrometer measurement transects, cylindrical soil cores were sampled for determination of soil texture, OM, θg and ρd. Soil water potential and effective stress (σ') were also estimated for each location. Standard Proctor tests were performed on eight soils with variable textures. Proctor density was well described as a function of CC and OM (R2adj = 0.97, RMSE = 0.046 Mg m−3) and was used as reference density to obtain ρrel. From this we developed a model for prediction of ρrel from PR and σ′ that allows comparisons between soils without changes in model coefficients. However, σ' cannot be obtained from on-the-go measurements and the model is therefore of limited value for soil compaction mapping. A model for estimating ρrel from PR and θg yielded satisfactory predictions (R2adj = 0.66, RMSE = 3.3%), although θg is a texture-dependent measure of soil water that cannot be compared across soils. Moreover, ρd was well predicted from PR and θg (R2adj = 0.93, RMSE = 0.05 Mg m−3), possibly because all our measurements were carried out at similar soil water potential, which implies that θg carries soil textural information. Future research should test the proposed equations for a wide range of soil water potential values. The findings presented can be of use in developing measurement systems for mapping soil compactness that combine the proposed prediction functions with horizontal penetrometer and water content sensor systems

Feasibility study on detecting different types of sugar solutions using a dielectric resonator sensor

One of the most important ingredients of most foods is sugar, so it is important to detect the type of sugar in foods. In this study, a cylindrical dielectric sensor using a function generator and a spectrum analyzer was used to measure dielectric spectra in the range of 0-150 MHz to detect different sugars in water-sugar solutions. Dielectric spectra were investigated by preparing a variety of water-sugar solutions including glucose, sucrose, fructose, invert, high fructose corn syrup and malt extract (dominantly maltose sugar) in four brix levels ranged within 3-12. Moreover, samples were tested with mixing the sugars in a solution. The statistical method of principal component analysis (PCA) was evaluated for detecting and discriminating different types of sugars from dielectric spectral data. PCA with two principal components PC1 and PC2, showed a distinct separation of sugars in three visual groups of sucrose and high fructose corn syrup, fructose and invert and glucose and malt. Irrespective of glucose, the mixed sugar solutions were discriminated from single sugar solutions in two groups. The results of this study showed a promising potential of dielectric method for detecting different sugars, however, the cumulative dielectric effect of the mixed sugars was not discriminable which is expected to be detected with greater range of frequency

Rapid detection of grape syrup adulteration with an array of metal oxide sensors and chemometrics

Among the different cases of emerging food fraud during the post-harvest processing, the adulteration in grape syrup is one. Typically, the grape syrup is adulterated with some illegitimate foreign materials such as grape paste (sauce), date syrup and even adding sugar-water solution to the pure grape syrup. The present study deals with assessing an electronic nose (e-nose) consisting of eight different metal oxide semiconductor (MOS) sensors for prompt detection of adulteration in the grape syrup. Three different adulterants i.e. grape paste, date syrup and sugar-water solution, each at three levels of 50, 60 and 75%, were tested. The collected data from MOS were normalised and visualised with the help of standard normal variate (SNV) and principal component analysis (PCA), respectively. Moreover, the scores obtained from PCA were used to perform hierarchal cluster analysis (HCA) to identify the similarities between different adulterated mixtures and pure grape syrup. Three different classification cases were considered to (i) address the presence of adulteration, (ii) detect the different adulterants and (iii) classify the amount of each adulteration. Linear discriminant analysis (LDA) and multi-class support vector machine (SVM) were used for classification analyses. Results showed that PCA identified provided separate clusters for the MOS data corresponding to different adulterants and their levels. The HCA showed a hierarchal of similarities between pure grape syrup and different levels of adulterations. LDA and SVM resulted in a successful classification modelling. However, the performance of SVM was considerably better than LDA with classification accuracies of 98.6 ± 0.10%, 98.9 ± 1.16% and 95.1 ± 1.39% for detecting adulteration, different adulterants and different concentrations of adulterants, respectively. MOS sensors coupled with chemometrics could provide a useful instrument and fast procedure for detection of adulteration in grape syrup

Potential of two dielectric spectroscopy techniques and chemometric analyses for detection of adulteration in grape syrup

Food adulteration is a widespread illegitimate procedure involving contamination of food with chemical and physical substances. The adulterated food products are not only of decreased quality but also may cause pathogenic effects that jeopardize the human health. Adulteration of liquid foods is majorly performed for economic gains by utilizing cheap adulterants which do not necessarily change the color, taste and appearance of the food to be easily detectable by human senses. In the present study, two different dielectric spectroscopy sensors (parallel plate capacitor (PPC) and cylindrical stub resonator (CSR)) were examined and compared for detection of adulteration in grape syrup. The aim was to address which sensor could be a more precise instrument for detecting the type and level of a variety of common adulterants in grape syrup. The different adulterants tested were the date syrup, grape paste and sugar-water solution mixed at 5, 10, 15, 20, 25 and 30% with pure grape syrup. The multivariate dielectric spectral data were visualized with principal component analysis (PCA). Furthermore, similarity between different adulterants and their levels was identified with the hierarchal cluster analysis (HCA). To perform classification analysis, two different classification techniques i.e., linear discriminant analysis (LDA) and multi-class support vector machines (SVM) were utilized and compared. The results showed that PCA provided clear visualization identifying different types of adulterants over the score plots. Classification of adulteration type using SVM and LDA resulted in 100% accuracy for either of the sensors. For classifying the level of adulteration, PPC sensor associated with SVM classifier resulted in the highest accuracy (100%). In conclusion, the adulteration detection in grape syrup was satisfactorily addressed by the dielectric spectroscopy techniques. As diagnosistic tools, both the instruments could be implemented with standards executed for food security assessments

Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation

This study was aimed to evaluate the freshness and quality of cultured shrimp (litopenaeus vannamei) during 9 days of storage on ice (i.e., at a temperature of 0°C) using image processing technique. A lighting chamber was used to provide uniform conditions for illumination. The shrimp freshness was evaluated using computer vision technique through color changes of head, legs and tail of the harvested shrimps. Thirty-six color parameters of the images such as mean and variance of red (r), green (g), blue (b), lightness hue (h), saturation (s), value (v), luma information (i and y), the luma component (y), chroma component (cr), lightness (L*), redness (a*), yellowness (b*), chroma (c), and hue (h) were analyzed. Some parameters, such as b*, from side pictures and r mean, b variance, v mean, y mean, b* mean and (L*) mean from top pictures changed with a rather similar trend during the storage period. Different computational expert approaches such as linear discriminant analysis, quadratic discriminant analy..

Application of Image Analysis Combined with Computational Expert Approaches for Shrimp Freshness Evaluation

This study was aimed to evaluate the freshness and quality of cultured shrimp (litopenaeus vannamei) during 9 days of storage on ice (i.e., at a temperature of 0°C) using image processing technique. A lighting chamber was used to provide uniform conditions for illumination. The shrimp freshness was evaluated using computer vision technique through color changes of head, legs and tail of the harvested shrimps. Thirty-six color parameters of the images such as mean and variance of red (r), green (g), blue (b), lightness hue (h), saturation (s), value (v), luma information (i and y), the luma component (y), chroma component (cr), lightness (L*), redness (a*), yellowness (b*), chroma (c), and hue (h) were analyzed. Some parameters, such as b*, from side pictures and r mean, b variance, v mean, y mean, b* mean and (L*) mean from top pictures changed with a rather similar trend during the storage period. Different computational expert approaches such as linear discriminant analysis, quadratic discriminant analy..