Blast furnace slag is a key large-tonnage waste product of metallurgical production, which is considered to be a promising alternative material in construction. In order to determine the scope of potential use of slag as a marketable product, it is necessary to study its structure and composition, which is determined by means of modern analytical instrumental methods. This paper analyzes the application of Fourier transform infrared spectroscopy (FTIR) and chemometrics methods to develop calibration models for identifying pelletized slag by elemental composition. In a comparative analysis of FTIR-spectra of slag the characteristic frequencies of absorption bands responsible for the content of calcite, silicates and aluminosilicates in the composition of samples were determined. Multivariate regression methods (principal components regression, partial least squares regression) and data of elemental composition results by EDX method were used to develop calibration models for determining elemental composition of granulated blast furnace slag. Using the developed PLS models with high performance (R² from 0.91 to 0.96 for different components), the prediction of the elemental composition (Ca, Si, O, Mg) of the test sample was carried out and a low deviation of the prediction in contrast to the EDX reference data was obtained. The use of PLS calibration models for rapid and nondestructive determination of the quantitative content of components of the composition of granulated blast furnace slag has been proposed.

An innovative approach is reported for recovering Fe and Zn resources from hazardous zinc-bearing electric arc furnace dusts (ZBDs) in a sustainable manner. A combination of carbothermal and H₂ reduction were used to overcome challenges associated with the high temperatures of carbothermal reduction and the high costs/limited supplies of hydrogen. In-depth reduction studies were carried out using zinc-rich (17 wt.%), iron-poor (35 wt.%) ZBD; coke oven battery dry quenching dust (CDQD) was used as reductant. Briquettes were prepared by mixing ZBD and CDQD powders in a range of proportions; heat treatments were carried out in flowing H₂ gas at 700 °C–900 °C for 4 h. The reduced products were characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD) and inductively coupled plasma (ICP). The Fe content of the reduced briquettes showed increases between 50 and 150%, depending on composition and reduction temperature; Zn, Pb, Cl, Na, K and S were completely absent. The gaseous elements were collected in cooled traps at the furnace outlet to recover metallic zinc and other phases. The volatile products collected at the outlet (900 °C) contained more than 70% zinc and 6% lead; small amounts of zinc were also present in the metallic phase. The processing temperatures were significantly lower in the combined approach as compared to 100% carbothermal reduction. While reducing energy consumption and limiting the generation of greenhouse gases, this approach has the potential for enhancing the reutilization of hazardous industrial wastes, resource recovery, and economic and environmental sustainability.

Classification of buckwheat grains is important because the absence of defective grains is a guarantee of yield and quality. Buckwheat grains were randomly selected from a batch with grains that varied in quality. The identification and classification of buckwheat grains according to the degree of fulfillment was carried out by a combination of microfocus X-ray and hyperspectral image analysis and multivariate analysis techniques. Using microfocus radiography, buckwheat grains were categorized into groups according to the degree of fulfillment. Hyperspectral image of buckwheat grains in the range of 935–1720 nm was acquired using a Specim FX17 camera. Using the polygon selection function, the averaged spectra were obtained and a data matrix of grain samples was generated. The bands of the spectrum contributing most to the grading of the grain samples by the degree of fulfillment were identified using the principal component analysis. The classification model of grading buckwheat grain into groups by the degree of fulfillment was constructed by partial least squares discriminant analysis method. The results showed that hyperspectral image is a potential tool for rapid and accurate identification of buckwheat grains, which can be used in large-scale grain classification and grain quality determination.

Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties.

Пшеничная мука является одним из основных ингредиентов для производства хлебобулочной пр о- дукции, в связи с чем актуально исследование ее технологических свойств. Рассмотрено применение методов мно- гомерного анализа для контроля и оценки показателей технологических свойств пшеничной хлебопекарной муки высшего сорта и ее градации на группы качества. Идентификация и классификация пшеничной муки сведена к по- строению классификационной функции. Исследованы 34 образца пшеничной муки, различающиеся по 9 показате- лям технологических свойств, включая показатели, полученные на фаринографе. Установлены средние значения по показателям технологических свойств для каждой из 3 групп качества пшеничной муки. Методами кластериза- ции (k-средних, иерархическим агломеративным кластерным анализом) и методом главных компонент подтверждена градация образцов пшеничной муки на группы качества. Методом пошагового дискриминантного анализа выявле- ны 3 показателя технологических свойств, вносящих наибольший вклад в градацию образцов на группы качества по силе муки: разжижение теста через 10 мин после начала тестирования, разжижение теста через 12 мин после времени развития и число качества. Методом дискриминантного анализа построена система классификационных функций градации образцов пшеничной муки по силе муки на 3 группы качества: первая – с хорошими хлебопе- карными свойствами, вторая – с пониженными, третья – с низкими. Точность модели составила 100%. Полученная система функций классификации перспективна для предсказания принадлежности новых образцов пшеничной муки к одной из 3 групп. Предлагаемый метод определения группы качества пшеничной муки, различающейся по по- казателям силы муки, может быть высокоэффективен для практического применения в пищевой промышленности. Wheat fl our is one of the main ingredients for the production of bakery products, and therefore the study of its technological properties is relevant. The article considers the use of multivariate analysis methods for monitoring and assessing the indicators of technological properties of premium wheat bakery fl our and its gradation into quality groups. Identifi cation and classifi cation of wheat fl our is reduced to the construction of a classifi cation function. 34 samples of wheat fl our, differing in 9 indicators of technological properties, including indicators obtained on a farinograph, were studied. Average values for the indicators of technological properties for each of the 3 quality groups of wheat fl our were established. Clustering methods (k- means, hierarchical agglomerative cluster analysis) and the principal component method confi rmed the gradation of wheat fl our samples into quality groups. The step-by-step discriminant analysis method was used to identify 3 indicators of technological properties that make the greatest contribution to grading samples into quality groups by fl our strength: dough liquefaction 10 minutes after the start of testing, dough liquefaction 12 minutes after the development time, and quality number. The discriminant analysis method was used to construct a system of classifi cation functions for grading wheat fl our samples by fl our strength into 3 quality groups: the fi rst with good baking properties, the second with reduced properties, and the third with low properties. The accuracy of the model was 100%. The resulting system of classifi cation functions is promising for predicting the belonging of new wheat fl our samples to one of the 3 groups. The proposed method for determining the quality group of wheat fl our, differing in fl our strength indicators, can be highly effective for practical application in the food industry.