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Characterization of drum-dried thickeners for dysphagia-adapted liquid diets
(2025-06-01)
Wattanapan, Pattra
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Sungsinchai, Sirada
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Tanjor, Siriporn
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Yangyuen, Suphan
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Chiawchanwattana, Cherdpong
Dysphagia patients require suitable thickened liquid foods that are easy and safe to swallow. The use of thickeners is one possible approach to increasing the viscosity of liquid foods. This study aimed to develop a powdery thickener with high dispersibility and thickening capability using drum drying. The experimental design for thickener polysaccharides—namely xanthan gum (XG), carboxymethyl cellulose (CMC), and modified corn starch (MCS)—was conducted using the extreme vertices method (10 formulas), followed by mixing with maltodextrin, potassium chloride, and water. The solution was dried using a drum dryer at 120 °C. The dried samples were evaluated in terms of thickening capability, solubility, and stability in thickened liquid foods. The moisture content of the dried samples ranged from 4.40% to 6.06% (d.b.). The use of XG as the main thickener polysaccharide in the prepared thickeners resulted in higher viscosity and stability of all thickened liquid foods compared to CMC and MCS, respectively. In the cases of water, orange juice, milk, and cream soup thickened by the prepared thickener, the formulation containing only XG as the thickener polysaccharide exhibited the highest viscosities. Although orange juice and cream soup thickened with this formulation showed lower viscosities than those thickened with a commercial thickener, the same honey-like consistency was observed. Conversely, water and milk thickened with this formulation demonstrated higher viscosities than those thickened with the commercial product. The results of rheological, tribological, in vitro digestibility, and swallowing tests for this thickener were comparable to the commercial thickener, indicating its potential for use in dysphagia liquid diets.
1
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Synthesis and characterization of aluminosilicate and zinc silicate from sugarcane bagasse fly ash for adsorption of aflatoxin B1
(2024-12-01)
Niamnuy, Chalida
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Sungsinchai, Sirada
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Jarernsamrit, Prapaporn
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Devahastin, Sakamon
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Chareonpanich, Metta
Sugarcane bagasse fly ash, a residual product resulting from the incineration of biomass to generate power and steam, is rich in SiO<inf>2</inf>. Sodium silicate is a fundamental material for synthesizing highly porous silica-based adsorbents to serve circular practices. Aflatoxin B1 (AFB1), a significant contaminant in animal feeds, necessitates the integration of adsorbents, crucial for reducing aflatoxin concentrations during the digestive process of animals. This research aimed to synthesize aluminosilicate and zinc silicate derived from sodium silicate based on sugarcane bagasse fly ash, each characterized by a varied molar ratio of aluminum (Al) to silicon (Si) and zinc (Zn) to silicon (Si), respectively. The primary focus of this study was to evaluate their respective capacities for adsorbing AFB1. It was revealed that aluminosilicate exhibited notably superior AFB1 adsorption capabilities compared to zinc silicate and silica. Furthermore, the adsorption efficacy increased with higher molar ratios of Al:Si for aluminosilicate and Zn:Si for zinc silicate. The N<inf>2</inf> confirmed AFB1 adsorption within the pores of the adsorbent. In particular, the aluminosilicate variant with a molar ratio of 0.08 (Al:Si) showcased the most substantial AFB1 adsorption capacity, registering at 88.25% after an in vitro intestinal phase. The adsorption ability is directly correlated with the presence of surface acidic sites and negatively charged surfaces. Notably, the kinetics of the adsorption process were best elucidated through the application of the pseudo-second-order model, effectively describing the behavior of both aluminosilicate and zinc silicate in adsorbing AFB1.
1
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Enhancing probiotic encapsulation with konjac glucomannan hydrolysate
(2024-10-01)
Phumsombat, Putthapong
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Lekhavat, Supaporn
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Devahastin, Sakamon
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Chiewchan, Naphaporn
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Borompichaichartkul, Chaleeda
Feasibility of using konjac glucomannan hydrolysate (KGMH) as prebiotics and wall material for encapsulating probiotics was investigated. Prebiotic activity scores (PASs) were determined for KGMH, inulin, fructooligosaccharides and konjac glucomannan to assess their effectiveness in promoting growth of probiotics and inhibiting enteric mixture. Thermal tolerances of probiotics were also assessed. Lactobacillus rhamnosus L34 and L. acidophilus LA5 exhibited higher PASs and thermal tolerances and were selected for encapsulation; freeze drying and spray drying were used to form and dry encapsulated probiotics. Maltodextrin (MD), soy protein isolate (SPI) and KGMH, either individually or in combination, were comparatively used as wall materials. Freeze drying with SPI and KGMH resulted in highest survival rates for L. rhamnosus L34 (97.92%) and L. acidophilus LA5 (88.94%). In case of spray drying, MD, KGMH and combination of MD and KGMH resulted in drying yields of 55–71%; SPI-containing formula resulted in lower yields (20–23%). Spray-dried probiotics exhibited lower survival rates compared to their freeze-dried counterpart. KGMH, particularly in combination with SPI, emerged as promising material for enhancing probiotics survival during encapsulation.
1
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Rapid smartphone-based assays for pesticides inspection in foods: current status, limitations, and future directions
(2024-01-01)
Yun, Pheakdey
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Jinorose, Maturada
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Devahastin, Sakamon
Smartphone-based assays to inspect pesticides in foods have attracted much attention as such assays can transform tedious laboratory-based assays into real-time, on-site, or even home-based assay and hence overcoming the limitations of conventional assays. Although an array of smartphone-based assays is available, information on the use of these assays for pesticides inspection is scattered. The purposes of this review are therefore to compile, summarize and discuss state-of-the-art as well as advantages and limitations of the relevant technologies. Suggestions are provided for further development of smartphone-based assays for rapid inspection of pesticides in foods. Smartphone-based assays relying on enzyme inhibitions are noted to be nonselective qualitative, capable of reporting results in a quantitative manner only when a sample contains an individual pesticide. Smartphone-based assays relying on chemical reactions also target only individual pesticides. Smartphone-based visible spectroscopy can, on the other hand, inspect individual and multiple pesticides with the aid of appropriate colorimetry-, luminescence-, or fluorescence-based assay. Smartphone-based visible-near infrared and Raman spectroscopies are suitable for simultaneous multiple pesticides inspection. Raman spectroscopy is of particular interest as it can detect pesticides even at lower concentrations. This spectroscopic technique can also serve as a real-time assay with the aid of cloud network computations.
3
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Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
(2021-12-01)
Stienkijumpai, Pinpinat
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Jinorose, Maturada
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Devahastin, Sakamon
Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.
3
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A concise history of drying
(2020-01-01)
Devahastin, Sakamon
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Jinorose, Maturada
Drying has been conducted since time immemorial with the main purpose of preserving food and agricultural produce. This chapter provides a concise history of drying of pharmaceutical products. The Chinese book on roots and grasses Pen T'Sao, written by Emperor Shen Nung as early as 2500 BC, mentioned as many as 365 drugs obtained from dried parts of medicinal plants, many of which are still in use even today. The chapter also gives brief histories of some popular drying techniques for drugs and other relevant molecules. The selected drying technologies discussed are: Freeze drying, spray drying, fluidized, bed drying, and supercritical drying. Spray drying is probably the most mature alternative technology to freeze drying and can be applied to the production of many pharmaceutical products.
4
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Use of digital image analysis as a monitoring tool for non-uniform deformation of shrinkable materials during drying
(2017-01-01)
Jinorose, Maturada
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Stienkijumpai, Anyanun
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Devahastin, Sakamon
Deformation is a phenomenon that is common during drying. Generally, deformation is evaluated and reported in terms of volumetric shrinkage. However, volumetric shrinkage cannot be used to describe non-uniform deformation, which commonly takes place during drying of shrinkable materials such as biopolymers, fruits and vegetables. In the present study, algorithms and software were developed to first reconstruct 3D images from 2D images, and then to monitor non-uniform deformation of such images. Agar gel with different sugar contents were used as the test materials and were allowed to undergo drying at different rates. The developed image reconstruction algorithms were successfully validated with standard geometrical shapes. Various image-based parameters, both in two dimensions, i.e. projected area, perimeter, major axis length, minor axis length, equivalent diameter, extent and fractal dimension; and in three dimensions, i.e. volume, sphericity, Wadell's sphericity, Wadell's roundness, radius ratio and Hoffmann shape entropy, were then calculated. The results showed that extent and fractal dimension could be used to identify the onset and to monitor non-uniform deformation in two dimensions, while sphericity could be used for such tasks in three dimensions.
2
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Artificial neural network modeling of physicochemical changes of shrimp during boiling
(2012-01-01)
Niamnuy, Chalida
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Kerdpiboon, Soraya
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Devahastin, Sakamon
Frozen boiled shrimp and dried shrimp are among the high-value fishery products of Thailand. During the production of these products boiling is one of the most important steps that affects significantly the product physicochemical properties, especially the quantity and quality of proteins, which in turn affect other apparent properties perceived by consumers. The protein changes are, however, difficult to evaluate comparing to other typical physical properties of shrimp. The objective of this study was therefore to develop an artificial neural network (ANN) model to predict the protein changes of shrimp in terms of protein loss and protein denaturation as a function of the boiling conditions, namely, concentration of salt solution and boiling time, as well as a rather easily determined change of shrimp, that is, cooking loss. Other apparent physical changes of shrimp viz. shrinkage and hardness were also predicted. The optimized ANN structure for the prediction of protein changes consists of two hidden layers and twelve neurons per layer, while the optimized ANN structure for the prediction of physical changes consisted of two hidden layers and eight neurons per layer. The predicted protein and physical changes agreed very well with the experimental data with R<sup>2</sup> > 0.994 and 0.972, respectively. © 2011 Elsevier Ltd.

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