Feasibility of near infrared and Raman hyperspectral imaging combined with multivariate analysis to assess binary mixtures of food powders

Achata, Eva M.; Esquerre, Carlos; Gowen, Aoife; O'Donnell, Colm P.

doi:10.1016/j.powtec.2018.06.025

Cited by 20 publications

(14 citation statements)

References 33 publications

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“…Where: V is the total volume of the mixture; Vo is the volume of samples taken from the mixture. Thus, in the case when (Pt)E is taken as the final value for a satisfactory mixing result, the desired time can be obtained by solving equation (3) relatively [10], [17]. In practice, the mixing efficiency is estimated by the coefficient of variation of the distribution of the key component in the micro-volumes of the mixture [11], [12], [13]: (4) Where:…”

Section: Resultsmentioning

confidence: 99%

“…The process of mixing a multicomponent bulk mixture is a probabilistic process, the study of which involves statistical methods and probability theory. Fischer F. K. gives the diffusion mixing equation for a horizontal cylindrical mixer [16], [17], [18], [19]: (5) Where: W is the probability distribution density, meaning the particle concentration; D0 and Dz are the coefficients of axial and radial diffusion; r and z are the distance in the radial and axial directions; t is the mixing time.…”

Section: Resultsmentioning

confidence: 99%

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Mixing of flour mixture components in the production of pasta from nontraditional raw materials

Ospanov

Muslimov²,

Timurbekova³

et al. 2022

Potr. S. J. F. Sci.

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Along with a balanced amino acid composition and high protein digestibility, food products should contain complex carbohydrates and ballast substances (dietary fibers) that ensure the normal functioning of the digestive organs. In this regard, fresh raw foods for dietary pasta are of interest for modern pasta production. It is to such raw materials for the manufacture of dough that flour and starch from some cereals, triticale flour, and stale deformed bread are referred. When forming pasta dough from nontraditional flour raw materials, an important technological value for giving the best rheological properties of the dough is the uniform distribution of the binder component in the mixture – dry wheat gluten (DWG). As an object of experimental research to study the mixing process, a loose flour mixture of cereals (barley, oats, corn, buckwheat, millet, peas, and soybeans) was used, compiled based on calculating the recipe using computer programs. The prepared mixtures are a valuable source of nutrients and minerals. At the same time, to improve the technological properties of the dough as a biologically active additive, 25% DWG is introduced to improve the rheological properties of the dough.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Mixing of flour mixture components in the production of pasta from nontraditional raw materials

Ospanov

Muslimov²,

Timurbekova³

et al. 2022

Potr. S. J. F. Sci.

View full text Add to dashboard Cite

show abstract

“…PCA and PLSR were used for data analysis. The main aim of the study was to compare the potential of two different techniques for food powder analysis, while slightly better results were obtained with NIR-HSI [90]. Line-scan Raman hyperspectral imaging (RHI) employing a 785 nm laser line was used to visualize and determine multiple wheat flour adulteration with benzoyl peroxide, alloxan monohydrate, and L-cysteine.…”

Section: Cereal and Cereal Productsmentioning

confidence: 99%

A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Temiz¹

2021

Photochem

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Applications of hyperspectral imaging (HSI) methods in food adulteration detection have been surveyed in this study. Subsequent to the research on existing literature, studies were evaluated based on different food categories. Tea, coffee, and cocoa; nuts and seeds; herbs and spices; honey and oil; milk and milk products; meat and meat products; cereal and cereal products; and fish and fishery products are the eight different categories investigated within the context of the present study. A summary of studies on these topics was made, and articles reported in 2019 and 2020 were explained in detail. Research objectives, data acquisition systems, and algorithms for data analysis have been introduced briefly with a particular focus on feature wavelength selection methods. In light of the information extracted from the related literature, methods and alternative approaches to increasing the success of HSI based methods are presented. Furthermore, challenges and future perspectives are discussed.

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“…Advanced analytical tools such as Raman spectroscopy have gained interest in recent years for assessing the molecular properties of foods and in linking those with final product properties (Cheng et al 2010;Danting and Yibin 2011;Achata et al 2018;Zhang et al 2019). Raman spectroscopy examines molecular information of a sample without requiring sample preparation, thus allowing rapid assessment of samples, offering potential to use as part of at-line or inline systems (Danting and Yibin 2011).…”

Section: Introductionmentioning

confidence: 99%

Raman spectra of micellar casein powders prepared with wet blending of glycomacropeptide and micellar casein concentrate

Panthi

Shibu

Ochalski

et al. 2022

Int J of Dairy Tech

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Understanding the molecular properties and interactions of components within high-protein dairy powders is important in predicting rehydration performance. Raman spectroscopy was used to generate molecular information which provided insights into interactions between caseins in micellar casein concentrate (MCC) powder formulated with varying proportions of glycomacropeptide (GMP). The Raman bands showed intense peaks at 1446 to 1476 cm À1 (due to CH 2 vibration) and 1653 to 1668 cm À1 (due to C=O of Amide I and C=C bond) in MCC powder samples blended with GMP. Enhanced rehydration characteristics of MCC powder containing GMP matched with these differences in Raman peak intensity.

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Feasibility of near infrared and Raman hyperspectral imaging combined with multivariate analysis to assess binary mixtures of food powders

Cited by 20 publications

References 33 publications

Mixing of flour mixture components in the production of pasta from nontraditional raw materials

Mixing of flour mixture components in the production of pasta from nontraditional raw materials

A Review of Recent Studies Employing Hyperspectral Imaging for the Determination of Food Adulteration

Raman spectra of micellar casein powders prepared with wet blending of glycomacropeptide and micellar casein concentrate

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