Determination of significant factors in high-shear granulation process of sucrose with aqueous solution of sodium lauryl sulphate using partial least square regression approach

Sucrose as one of the most commonly used raw materials in pediatric formulations is soluble and sticky excipient and its manipulation in high shear granulators may be very difficult. Therefore, to determine the correct amount of liquid binder is very important because it falls in a very narrow range and may vary due to small variations in the material properties or environmental conditions. The possibility of using the sugar as powder for granulation may be very challenging because of solubility and moisture adsorption properties of crystalline sugar, especially if the binder solution is water. The aim of this study was trying to solve these problems and produce sucrose granules using high share granulation and water as a binding liquid, with properties required for final product good performance. By reducing the sucrose particle size and improving the uniformity of the size distribution, the differences of the processes of nucleation and growth for small and large particles might be reduced. According to the variable importance or VIP scores from the developed partial least square (PLS) model, raw material particle size is as influential variable as the quantity and composition of the granulation liquid (Gra), granulation time (Grn) and impeller rate (Imp). Keywords: sucrose, particle size distribution, high-shear granulation, partial least square

Download Full-text

Combustion Synthesis of β-SiAlON Using 3D Ball Milling

Materials Science Forum ◽

10.4028/www.scientific.net/msf.898.1717 ◽

2017 ◽

Vol 898 ◽

pp. 1717-1723 ◽

Cited By ~ 1

Author(s):

Xue Mei Yi ◽

Shota Suzuki ◽

Xiong Zhang Liu ◽

Ran Guo ◽

Tomohiro Akiyama

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Combustion Synthesis ◽

Ball Mill ◽

Raw Materials ◽

Raw Material ◽

Size Distributions ◽

Particle Size Distributions ◽

Material Particle

Combustion synthesis (CS) of β-SiAlON was conducted using a 3D ball mill, with a focus on the effect of the 2D/3D ball mill premixing conditions on the CS raw material particle size as well as on the yield and grain shape of the final products. The results showed that the particle size distribution of the raw materials was significantly affected by the premixing conditions. Various particle sizes and particle size distributions could easily be obtained by using a 3D mill instead of a 2D mill due to the complex biaxial rotation movement of 3D milling. The particle size was more sensitive to the rotation ratio (vertical spin/horizontal spin, Vv/Vh) than the rotation rate when using 3D milling. Finally, β-SiAlON with less than 5 mass% unreacted Si was obtained using premix milling conditions of 135×200 [vertical spin (rpm) × horizontal spin (rpm)]. The grain shapes of the final products were clearly influenced by the particle size distribution of the raw mixtures.

Download Full-text

Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis

MATEC Web of Conferences ◽

10.1051/matecconf/202133307010 ◽

2021 ◽

Vol 333 ◽

pp. 07010

Author(s):

Masaaki Konishi ◽

Kazuki Watanabe ◽

Seiga Tachibana

Keyword(s):

Raw Materials ◽

Industrial Process ◽

Partial Least Square ◽

Least Square ◽

Raw Material ◽

Partial Least Square Regression ◽

Gas Chromatography Mass Spectrometry ◽

Volatile Components ◽

Targeted Analysis ◽

Natural Media

Natural media are often used for various commercial bioprocesses by manufacturers to cut raw material cost. However, the components of the raw materials varies between lot-to-lots and brand-to-brands. The varieties of raw materials influence to the cell growths and materials productivities, and results in unstable production across batches in manufacturing processes. To ensure the quality of raw materials among batches, it is necessary to perform a laboratory screening to purchasing the optimal one, and ensure a desirable performance in industrial process. To solve the serious problems in bioprocesses, it is developing that a modelling methodology using composition of raw materials, named us “substratome”, obtained by non-targeted metabolomicslike methods can estimate the cell growth and bio-productions. Here, we will present that two model studies: [1] Escherichia coli growths have been estimated from hydrophilic components in yeast extract obtained by gas chromatography-mass spectrometry (GC-MS), and [2] bioethanol production have been estimated by the volatile components in corncob and corn stover hydrolysates obtained by GC-MS; by partial least square regression (PLS-R). Additionally, we will present preliminary results to solve the same issues by using artificial intelligence.

Download Full-text

Looking beyond chemistry – XRD for exploration and processing of ores

Acta Crystallographica Section A Foundations and Advances ◽

10.1107/s2053273314090470 ◽

2014 ◽

Vol 70 (a1) ◽

pp. C952-C952

Author(s):

Uwe König ◽

Martijn Fransen

Keyword(s):

High Speed ◽

Raw Materials ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

X Ray Diffraction ◽

Ore Bodies ◽

Adequate Sampling ◽

The Difference

Decreasing ore qualities and increasing prices for raw materials require a better control of processed ore and a more efficient use of energy. Traditionally quality control in mining industries has relied on time consuming wet chemistry or the analysis of the elemental composition. The mineralogy that defines the physical properties is often monitored infrequently, if at all. The use of high speed detectors has turned X-ray diffraction (XRD) into an important tool for fast and accurate process control. XRD data and their interpretation do make the difference in the identification of minerals, in describing their distribution in ore bodies and in their beneficiation during processing. The use of modern techniques such Partial Least Square Regression (PLSR), Principal Component Analysis (PCA) or full pattern Rietveld quantification will be discussed during the presentation as well as the importance of adequate sampling and the correlation with sample chemistry. The practical use will be illustrated on case studies.

Download Full-text

Effects of raw material particle size distribution on the characteristics of Scots pine sawdust fuel pellets

Fuel Processing Technology ◽

10.1016/j.fuproc.2008.06.001 ◽

2008 ◽

Vol 89 (12) ◽

pp. 1324-1329 ◽

Cited By ~ 78

Author(s):

Dan Bergström ◽

Samuel Israelsson ◽

Marcus Öhman ◽

Sten-Axel Dahlqvist ◽

Rolf Gref ◽

...

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Scots Pine ◽

Raw Material ◽

Material Particle ◽

Pine Sawdust ◽

Fuel Pellets

Download Full-text

From Machining Chips to Raw Material for Powder Metallurgy—A Review

Materials ◽

10.3390/ma14185432 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5432

Author(s):

Catarina Duarte Batista ◽

Adriana André Martins das Neves de Pinho Fernandes ◽

Maria Teresa Freire Vieira ◽

Omid Emadinia

Keyword(s):

Particle Size ◽

Powder Metallurgy ◽

Solid State ◽

Size Distribution ◽

Essential Role ◽

Raw Materials ◽

Raw Material ◽

Clean Technologies ◽

Metal Chips ◽

Powder Particles

Chips are obtained by subtractive processes such as machining workpieces and until recently considered as waste. However, in recent years they are shown to have great potential as sustainable raw materials for powder technologies. Powder production from metal chips, through the application of solid-state processes, seems to be an alternative to conventional atomization from liquid cooled with different fluids. However, chip material and processing have an essential role in the characteristics of powder particles, such as particle size, shape, size distribution and structure (4S’s), which are essential parameters that must be considered having in mind the powder process and the metallurgy applications. Moreover, different approaches refereed in the application of this new “powder process” are highlighted. The goal is to show how the actual research has been transforming subtractive processes from a contributor of wastes to clean technologies.

Download Full-text

STUDYING OF THE CHEMICAL COMPOSITION OF THE POWDER FROM BROCCOLI CABBAGE AS RAW MATERIAL FOR PRODUCTION OF FUNCTIONAL FOODS

Известия вузов. Пищевая технология ◽

10.26297/0579-3009.2019.1.5 ◽

2019 ◽

Author(s):

Т.И. КРЯЧКО ◽

В.Д. МАЛКИНА ◽

В.В. МАРТИРОСЯН ◽

С.А. СМИРНОВА ◽

Н.А. ГОЛУБКИНА ◽

...

Keyword(s):

Particle Size ◽

Chemical Composition ◽

Particle Size Distribution ◽

Size Distribution ◽

Raw Materials ◽

Raw Material ◽

Size Of Particles ◽

Average Size ◽

Food Staples ◽

Physical And Chemical

Представлен сравнительный анализ гранулометрических, органолептических и физико-химических показателей качества порошков из капусты брокколи, полученных конвективным и лиофильным способами сушки из отечественного сорта Тонус и импортного гибрида Маратон F1. По показателям гранулометрического состава исследованные образцы порошков конвективной и лиофильной сушек относятся к грубодисперсным системам. Меньшие значения среднего размера частиц (0,14–0,15 мм) имеют порошки из брокколи гибрида Маратон F1 двух способов сушки. Проведен анализ морфологии частиц порошков из капусты брокколи по характеристикам распределения вытянутости, гладкости и яркости. По гранулометрическому составу и морфологии частиц из порошков капусты брокколи сорта Тонус и гибрида Маратон F1существенных различий не обнаружено. Органолептические показатели – вкус, запах, цвет порошков капусты брокколи сорта Тонус и гибрида Маратон F1соответствовали использованному сырьевому источнику. Содержание белков, жиров и углеводов в порошках, полученных конвективным и лиофильным способами сушки, практически одинаково. Установлена сохранность витамина С в порошках при переработке свежей капусты: сорта Тонус в среднем на 26%, гибрида Маратон F1на 53,5%. Сохранность полифенольных соединений в порошках из брокколи сорта Тонус и гибрида Маратон F1, выработанных двумя способами сушки, составила 57,8–67,8%. Комплексные исследования показали, что порошки из капусты брокколи, выработанные из отечественного и импортного сырья, имеют ценный химический состав, что позволяет отнести их к перспективному продовольственному сырью для использования в технологиях функциональных продуктов питания. The comparative analysis of granulometric, organoleptic and physical and chemical indicators of quality of powders of cabbage of the broccoli received convective and liofilny by ways of drying from a domestic grade the Tonus and an import hybrid Maraton F1is submitted. On indicators of particle size distribution the studied samples of powders of convective and liofilny drying belong to grubodispersny systems. Smaller values of the average size of particles of 0,14–0,15 mm have hybrid broccoli powders Maraton of F1 of two ways of drying. The analysis of morphology of particles of powders of cabbage of broccoli according to characteristics of distribution of elongation, smoothness and brightness is carried out. On particle size distribution and morphology of particles of powders of cabbage of broccoli of a grade the Tonus and a hybrid Maraton F1of essential distinctions isn’t revealed. Organoleptic indicators – taste, a smell, color of powders of cabbage of broccoli of a grade the Tonus and a hybrid Maraton of F1corresponded to the used raw source. Content of proteins, fats and carbohydrates in the powders received convective and liofilny by ways of drying were almost close. The safety of vitamin C in powders when processing fresh cabbage is established: grades the Tonus on average 26%, a hybrid Maraton F1– 53,5%. Safety of polyphenolic connections in grade powders the Tonus and a hybrid Maraton of F1developed on two ways of drying made 57,8–67,8%. Complex researches have shown that the powders of cabbage of broccoli produced from domestic and import raw materials have the valuable chemical composition that allows to carry them to perspective food staples for use in technologies of functional food.

Download Full-text

Relationship between the TC of Smart Meta-Superconductor Bi(Pb)SrCaCuO and Inhomogeneous Phase Content

Nanomaterials ◽

10.3390/nano11051061 ◽

2021 ◽

Vol 11 (5) ◽

pp. 1061

Author(s):

Honggang Chen ◽

Mingzhong Wang ◽

Yao Qi ◽

Yongbo Li ◽

Xiaopeng Zhao

Keyword(s):

Particle Size ◽

Transition Temperature ◽

Raw Materials ◽

Raw Material ◽

Particle Sizes ◽

Phase Content ◽

Critical Transition Temperature ◽

Material Particle ◽

Inhomogeneous Phase ◽

Zero Resistance

A smart meta-superconductor Bi(Pb)SrCaCuO (B(P)SCCO) may increase the critical transition temperature (TC) of B(P)SCCO by electroluminescence (EL) energy injection of inhomogeneous phases. However, the increase amplitude ΔTC (ΔTC=TC−TC,pure) of TC is relatively small. In this study, a smart meta-superconductor B(P)SCCO with different matrix sizes was designed. Three kinds of raw materials with different particle sizes were used, and different series of Y2O3:Sm3+, Y2O3, Y2O3:Eu3+, and Y2O3:Eu3++Ag-doped samples and pure B(P)SCCO were prepared. Results indicated that the TC of the Y2O3 or Y2O3:Sm3+ non-luminescent dopant doping sample is lower than that of pure B(P)SCCO. However, the TC of the Y2O3:Eu3++Ag or Y2O3:Eu3+ luminescent inhomogeneous phase doping sample is higher than that of pure B(P)SCCO. With the decrease of the raw material particle size from 30 to 5 μm, the particle size of the B(P)SCCO superconducting matrix in the prepared samples decreases, and the doping content of the Y2O3:Eu3++Ag or Y2O3:Eu3+ increases from 0.2% to 0.4%. Meanwhile, the increase of the inhomogeneous phase content enhances the ΔTC. When the particle size of raw material is 5 μm, the doping concentration of the luminescent inhomogeneous phase can be increased to 0.4%. At this time, the zero-resistance temperature and onset transition temperature of the Y2O3:Eu3++Ag doped sample are 4 and 6.3 K higher than those of pure B(P)SCCO, respectively.

Download Full-text

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Download Full-text

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

Download Full-text

Leaching Behaviour of Construction and Demolition Wastes and Recycled Aggregates: Statistical Analysis Applied to the Release of Contaminants

Applied Sciences ◽

10.3390/app11146265 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6265

Author(s):

Alessandra Diotti ◽

Giovanni Plizzari ◽

Sabrina Sorlini

Keyword(s):

Particle Size ◽

Statistical Analysis ◽

Raw Materials ◽

Primary Source ◽

Recycled Aggregates ◽

Critical Parameters ◽

Material Particle ◽

Analysis Methodology ◽

Construction And Demolition Wastes ◽

Leaching Behaviour

Construction and demolition wastes represent a primary source of new alternative materials which, if properly recovered, can be used to replace virgin raw materials partially or totally. The distrust of end-users in the use of recycled aggregates is mainly due to the environmental performance of these materials. In particular, the release of pollutants into the surrounding environment appears to be the aspect of greatest concern. This is because these materials are characterized by a strong heterogeneity which can sometimes lead to contaminant releases above the legal limits for recovery. In this context, an analysis of the leaching behaviour of both CDWs and RAs was conducted by applying a statistical analysis methodology. Subsequently, to evaluate the influence of the particle size and the volumetric reduction of the material on the release of contaminants, several experimental leaching tests were carried out according to the UNI EN 12457-2 and UNI EN 12457-4 standards. The results obtained show that chromium, mercury, and COD are the most critical parameters for both CDWs and RAs. Moreover, the material particle size generally affects the release of contaminants (i.e., finer particles showed higher releases), while the crushing process does not always involve higher releases than the sieving process.

Download Full-text