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Pasta yellowness is affected by different factors, the most important of which are intrinsic to the quality of semolina (natural carotenoid pigments, protein, ash, and lipoxygenase [LOX] activity) and processing conditions. Because all the parameters involved in pasta color are under the control of varietal and environmental factors, the role of the genotype, environment, and the interaction between genotype and environment on color expression were studied. Although the analysis of variance showed the genotype‐by‐environment interaction to be significant, a nonorthogonal analysis attributed a higher weight to genotype on parameters directly involved in color expression: β‐carotene content, yellow index, and LOX activity. Furthermore, the loss of pigments and yellow index after milling and processing was evaluated and correlated with all the parameters involved in the determination of final pasta color. The phase mainly responsible for pigment loss was pasta processing. A decrease of 16.3% in semolina β‐carotene content during pasta processing versus a 7.9% loss during milling was determined. The isoenzymatic forms LOX‐2 and LOX‐3, active at the pH of dough, were responsible for the loss of color in pasta products. Simple correlations and the linear multiple regression corroborated this finding. Hydroperoxidation activity at pH 6.6, bleaching activity, and ash content were responsible for 87% (R2 adjusted) of total variance, with each variable accounting for 57, 61, and 22% of the variation, respectively. This confirms that LOX activity is the main factor involved in the loss of color, while a secondary and lesser role can be seen for ash content. Therefore, a high pigment content, located in the interior of the whole grain, and a lower LOX activity in semolina must be the selection characteristics by which breeding programs obtain a bright yellow pasta.

In order to assess the effect of genotype, location and their interaction on total phenolic content (TPC) of chemical extracts, the whole grains of durum and soft wheat, oat, barley and triticale were evaluated. Data showed differences in phenolic content of chemical extracts among cereal species and the analysis of variance confirmed the key role of location. Besides TPC and trolox equivalent antioxidant capacity (TEAC) values assessed by chemical extraction were compared with those obtained with an in vitro digestive enzymatic extraction. Differences were found between methanolic and enzymatic extracts, and data confirmed that enzymatic technique enhanced extraction of antioxidants but pointed out lesser differences among cereal types. The breads obtained by flours enriched with different levels of bran were also evaluated. Chemical extracts highlighted the increasing levels of antioxidants according to bran enrichments, without pointing out changes caused by baking. The enzymatic extraction instead did not show differences regarding to bran enrichments, but documented a loss in antioxidant properties of breads in respect to corresponding flours. On the other hand the scarce differences between flours and corresponding breads did not allow asserting that baking modified the TPC and TEAC, independently of the extraction methods used. Indeed, during baking process, also the observed phenolic acids profile variations did not vary the antioxidant properties of breads.

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One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to:(1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 9 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a nonparametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.

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