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A rapid method based on digital image analysis and machine learning technique is proposed for the detection of milk adulteration with water. Several machine learning algorithms were compared, and SVM performed best with 89.48 % of total accuracy and 95.10 % precision. An increase in the classification performance was observed in extreme classes. Better quantitative determination of the extraneous water was achieved using SVMR with R2(CV) and R2(P) of 0.65 and 0.71 respectively. The proposed technique can be used to screen raw milk based on the level of added extraneous water without the necessity of any additional reagent.

Durum wheat semolina is the only raw material of choice for high quality pasta products. Still the products have lower nutrient quality (protein, vitamins and minerals), high glycaemic response and high gluten allergenicity effect. In addition to durum wheat low yield capacity, it leads the shortage of supply due to low durum wheat local production in developing countries. The aim of this review to assess the replacement of durum wheat with better nutritional quality and locally available cereals and legumes for producing pasta with good nutritional, sensorial, cooking and textural quality. Development of legume fortified pasta products enhance the nutritional characteristics of processed products in terms of protein, dietary fiber and micro-nutrients. In addition, the inclusion of legume flour, as ingredients in pasta products which have lowering glycaemic indices (GI) and allergenicity compared with semolina pasta. Gluten-free spaghetti (unripe plantain, chickpea and maize flours) were similar diameters, water absorption values and overall sensorial acceptability but higher hardness, cohesiveness and chewiness than the control. It can be concluded that nutritional value of pasta can be improved by optimum level of legume and cereals incorporation without any significant effect on cooking, textural and sensorial quality.