Pasta from blend of wheat semolina and pearl millet flour (50:50) having average particle size of 425 µm supplemented with defatted soy flour (DSF) (5, 15, and 25%), carrot powder (CP) (5, 10, and 15%), mango peel powder (MPP) (5, 10, and 15%), and moringa leaves powder (MLP) (3, 5, and 8%) was prepared. With increase in the substitution level of DSF, CP, MPP, and MLP, cooking time (CT) decreased from 8.01 to 5.38 min and cooking loss (CL) increased from 8.06 to 11.90%. Pasta showed better hardness prepared from 5% DSF (14.43 N), 15% DSF (13.99 N), 25% DSF (13.07 N), and 3% MLP (11.73 N) than control pasta (9.90 N). Sensory evaluation revealed that samples were within the acceptable range (except 5% and 8% MLP). Maximum incorporation of 15% DSF, 10% CP, 5% MPP, and 3% MLP was found suitable in terms of color, cooking loss (<8%), hardness, and sensory quality. Practical application Pasta consumption ever increasing in domestic as well as international market. Functional ingredients such as cereals, pseudocereals, legume flour, fruit and vegetable powder contains plentiful of antioxidants, vitamins, dietary fiber, minerals, protein, flavonoids, carotenoids and phenolic compounds. Incorporation of such ingredients in pasta increases the nutritional as well as functional properties. In the present study, defatted soy flour (DSF) (5%, 15%, 25%), mango peel powder (MPP) (5%, 10%, 15%), carrot powder (CP) (5%, 10%, 15%) and moringa leaves powder (MLP) (3%, 5%, 8%) was incorporated into wheat semolina and pearl millet flour pasta. Substitution of DSF, MPP, CP and MLP up to 15%, 10%, 5% and 3%, respectively, yielded acceptable pasta with overall improved nutritional characteristics. Thus, DSF, MPP, CP and MLP can be successfully utilized for the preparation of pasta with improved qualities.

The correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post-harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s-curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R 2 = 0.956) and ellipsoid volume (R 2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracy. Practical applicationMass based fruit grading is one of the important aspects of packaging as it not only reduces the wastage of handling and transportation resources by optimizing packaging formations but also enhances the marketability of commodity. Consumers generally prefer the fruits of uniform size, weight, and shape. Grading of horticultural produce is usually based on its appearance, size, and weight. The automatic fruit grading techniques generally use mass as a grading parameter due to its accuracy and effectiveness of the operation. The available kinnow grading systems primarily grade the fruits based on their dimensional attributes. Hence, the study was aimed at mass modeling of kinnow mandarin based on the selected engineering attributes such that results might be helpful to develop an accurate automatic grading system for grading based on the combined approach of size and mass. This study provides information about relationships between fruit mass and axial dimensions, projected areas, and volume, which are useful for the development of mass, and size based kinnow grading systems.

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This study investigated the influence of extrusion operating condition [barrel temperature (BT) (50-90°C), feed moisture content (FM) (25-35%, w.b.), feeder speed (FS) (8-16 rpm) and screw speed to feeder speed ratio (SS:FS) (8-12)] on quality of pearl millet pasta [cooking time (CT), cooking loss (CL), hydration capacity (HC), swelling capacity (SC), hardness, springiness (SP), chewiness (CH) through response surface methodology (CCRD design)]. The results indicated that raising BT and FM reduced CT, CL, but increased HC, SC, hardness, SP, CH of pearl millet based pasta. HC, SC, hardness, SP and CH of pasta were increased as FS and SS:FS increased, whereas, CT and CL showed decreasing trend. The optimum operating conditions for pear millet based pasta was obtained at BT of 70°C, FM of 30% (w.b.), FS of 12 rpm and SS:FS ratio of 10 with low CT (B 5.25 min), CL (B 7.45%) and high HC (C 2.30 g g-1), SC (C 3.14 ml g-1), good hardness (C 11.11 N), SP (C 1.24 N) and CH (C 6.09 N mm).

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