Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

Tanzanian Food Origins and Protected Geographical Indications

As the world`s population is constantly growing, food security will remain on the policy Agenda, particularly in Africa. At the same time, global food systems experience a new wave focusing on local foods and food sovereignty featuring high quality food products of verifiable geographical origin. This article argues that Geographical Indications (GI´s) hold the potential to help transform the Tanzanian agriculture-dependent economy through the tapping of value from unique products, attributing taste and colour to place or regional geography. This study aims to identify the existence and characteristics of food origin products in Tanzania that have potential for GI certification. The hypothesis was that there are origin products in Tanzania whose unique characteristics are linked to the area of production. Geographical indications can be useful policy instruments contributing to food security and sovereignty and quality within an efficient marketing system with the availability of government support, hence the need to identify key candidates for GI certification. Five Tanzanian origin products were selected from 14 candidate agricultural products through a scoping study. Rice from Kyela, Aloe vera, Coffee and Sugar from Kilimanjaro and Cloves from Zanzibar are some of the product cases investigated and provides for in-depth case study, as ´landscape´ products incorporating ´taste of place´. Interviews were conducted to collect quantitative and qualitative data. Data was collected on the production area, product quality perceived by the consumer in terms of taste, flavour, texture, aroma, appearance (colour, size) and perceptions of links between geography related factors (soil, land weather characteristics) and product qualities. A qualitative case study analysis was done for each of the (five) selected Tanzanian origin products investigated with plausible prospects for Tanzania to leapfrog into exports of Geographical Indications products. Framework conditions for producers creating or capturing market value as stewards of cultural and landscape values, environments, and institutional requirements for such creation or capturing to happen, including presence of export opportunities, are discussed. Geographical indication is believed to allow smallholders to create employment and build monetary value, while stewarding local food cultures and natural environments and resources, and increasing the diversity of supply of natural and unique quality products and so contribute to enhanced food security.