Quality Evaluation Of Products Research Articles

Emerging techniques for the trace elemental analysis of plants and food-based extracts: A comprehensive review

This comprehensive review investigates the dynamic landscape of trace elemental analysis methodologies applied to plants and food-based extracts. The exploration spans from the inception of techniques to the latest procedures, contributing to heightened precision and sensitivity in elemental detection. According to the WHO, herbal plants and medicine from varied soil compositions serve as crucial therapeutic agents for 70–80 % of the world's population. Yet, their susceptibility to trace element toxicity poses a significant risk to human health. Rising population and increased food demands have led to environmental pollution, contaminating the food chain through unintended activities like industrialization, mining, and pesticide production. The elemental composition of plants and derived extracts is central to comprehending nutritional profiles, evaluating product quality, and ensuring food safety. Methodological advancements, progressing from manual procedures to sophisticated technologies such as X-ray fluorescence (XRF), neutron activation analysis (NAA), inductively coupled plasma optical emission spectroscopy (ICP OES), and inductively coupled plasma mass spectrometry (ICP-MS), are delineated. Recent strides in paper-based electrochemical sensors are highlighted for their distinctive capabilities and elucidation of associated advantages and limitations.Moreover, the review delves into innovative sample preparation methodologies, encompassing microwave-assisted digestion and solid-phase microextraction, to amplify the efficiency of elemental extraction and subsequent analysis. Integrating data analytics and machine learning in elucidating complex elementary datasets is explored, underscoring the potential for heightened accuracy and automation in trace elemental analysis. This review compiles literature data, summarizing sample preparation methods for various herbal parts (roots, soil, stems, bark, fruits, food). Standard protocols from WHO, United States Pharmacopeia-National Formulary (USP-FR), Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy (AYUSH) are considered. Lead, Cadmium, Mercury, and Arsenic are the primary toxic elements of concern in herbal medicines. This review furnishes valuable insights tailored for researchers, analysts, and policymakers actively involved in advancing the domain of trace elemental analysis in plants and food-based extract.

Making Decisions About Purchases Based on Varying Consumer Behavior Concerning Product Quality Evaluations

The purpose of this study was to determine how consumer behavior and product quality influence purchasing decisions. The subject of this research is UD Putra TS Majalengka West Java, which is a company engaged in fashion in Majalengka. The analysis technique uses a bootstrapping resampling process to determine the hypothesis using SEM-PLS using a second-order approach. The results of this study indicate that consumer behavior and product quality are the two most important factors in influencing consumer choice. Customer behavior significantly influences the final purchase decision, with cultural influences being the most influential force. Culture includes values, norms, beliefs, and practices that influence individuals and groups. Factors such as product preferences, communication styles, decision-making processes, and customer expectations influence customer behavior. To maintain profitability and market share, manufacturers must consider factors such as product quality, features, and price. Belief in product excellence increases the likelihood of purchase and can increase sales volume by maintaining quality and innovating. Measuring consumer behavior, product quality, and purchasing decisions is expected to use different indicators to be able to compare research results

Advancements in artificial intelligence-imaging analysis (IA) systems technology for comprehensive quality evaluation of pet food productshensive Quality Evaluation of Pet Food Products

The increasing demand for high-quality pet food products and the need for strict safety standards have led to the exploration and development of technologies that can accurately and quickly assess the quality of these products. One such technology is Imaging Analysis (IA) systems, which offers automation, non-destructiveness, and costeffectiveness to meet these evolving requirements. Imaging Analysis (IA) systems electronically replicate human visual perception, enabling precise and efficient evaluation of images. Extensive research has highlighted its potential and demonstrated successful applications in examining and grading pet food products. This review paper introduces the fundamental components of computer vision systems, while also discussing their advantages and disadvantages. Additionally, it explores image processing techniques and provides a comprehensive analysis of recent advancements and potential applications in evaluating the quality of pet food products.

An Integrated Approach for Amazon Electronic Products Reviews by Using Sentiment Analysis

In our routine life, we interact a wide range of products, and frequently browse through digital media platforms to access their quality. Although the accessibility of online platforms, consumers often find it challenging to swiftly judge the quality of products on the basis of customer reviews. To cope this situation, the study addresses this problem by suggesting a machine learning-based solution to categorize product reviews. For this, we employ various machine learning techniques, including Random Forest, Naïve Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) Classifier, and Bidirectional Encoder Representations from Transformers (BERT). In our model, we incorporate pre-processing methods for prepare the dataset for training and utilize feature extraction techniques such as TF-IDF and word2vec which are then applied to different classifiers to analyze the reviews. Moreover, we conduct this study by using the Amazon Electronics category dataset, it reveals that BERT outperforms other classifiers with a performance score of 0.8896. Therefore, this technique not only streamlines the procedure of evaluating product quality but also enhances the accuracy of review classification, giving a real-world solution for consumers and businesses alike.

Comparable analysis of flavor compounds and quality assessment of fermented bean curd using HS-SPME-GC/MS and colorimetric sensor array

This study aims to develop an effective tool by integrating traditional analysis methods with advanced sensor array technology and multiple algorithms, to achieve rapid characterization, qualitative identification, and flavor quality evaluation of fermented bean products. Firstly, the flavor compounds and flavor profiles of fermented bean curd from different manufacturers were identified using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) and quantitative descriptive analysis (QDA). A total of 63 volatile compounds were identified, of which 13 volatile compounds were identified as the key differential compounds in selected samples. Eight descriptors were selected to represent the aroma characteristics of fermented bean curd. Based on the key differential compounds, a low-cost colorimetric sensor array (CSA) was designed and constructed. Furthermore, the feasibility of CSA combined with multiple algorithms was investigated for brand discrimination and flavor quality assessment. Linear discriminant analysis (LDA) exhibited a better performance in distinguishing different flavor quality samples, and the recognition accuracy of its prediction set was 97.22%. The study suggests that CSA combined with multiple algorithms can be an effective tool for flavor characterization and qualitative discrimination of fermented bean curd, and can provide a new multi-dimensional analysis method for food analysis.

The Effects of Price Context and Prior Product Knowledge on Consumers’ Product Evaluations

This research examines how consumers rely on price and prior product knowledge in product quality evaluations. To understand the interaction effects of price context and prior product knowledge on quality evaluations, a conceptual framework based on cue utilization theory and information processing theory was developed. It is proposed that low knowledgeable consumers are more likely to use price as a quality indicator in a low-priced context; moderate knowledgeable consumers tend to rely on their knowledge to make product evaluations; high knowledgeable consumers are more likely to use their product knowledge in a high-priced context. Experimental studies were conducted using student subjects to test the proposed hypotheses. Results of the experiments showed significant interaction effects between price context and prior knowledge on quality evaluations. A significant quadratic relationship between the use of price and the level of prior product knowledge was observed only in a low-priced context, providing support for the proposed hypotheses in the conceptual model.

Connecting Research and Practice for Software Product Quality Evaluation and Certification: A Software Laboratory’s 25-Year Journey

Connecting Research and Practice for Software Product Quality Evaluation and Certification: A Software Laboratory’s 25-Year Journey

Long-term assessment and analysis of the radiometric quality of standard data products for Chinese Gaofen-1/2/6/7 optical remote sensing satellites

The first Chinese Gaofen (GF) remote sensing satellite was launched in August 2013 and has been in orbit for more than 10 years, providing a rich variety of image product data for remote sensing applications in various industries, with other remote sensing satellites of the GF series. To ensure the reliability of the information generated via remote sensing applications, all remote sensing images must undergo systematic quality evaluation. The quality of GF satellites is evaluated almost comprehensively during in-orbit testing at the early stage after launch; however, the remaining evaluations of product quality including geometric or radiometric calibrations are performed only annually. Thus, long-term systematic image quality evaluations are lacking, and the quality level and long-term change trends of the image products are unknown. These deficits have introduced uncertainty in the application of these products. This study aimed to establish a radiometric quality evaluation index framework for GF satellites to comprehensively measure the radiometric quality of GF image products. Multiple types of sample data, such as uniform field, knife-edge target, and radiometric calibration field data with different reflectance features, were acquired globally. Four core indexes, namely, the relative radiometric correction accuracy (RRCA), absolute radiometric correction accuracy (ARCA), modulation transfer function (MTF), and signal-to-noise ratio (SNR), were used to analyze and evaluate the long-term radiometric quality of the images from four GF optical remote sensing satellites: GF1, GF2, GF6, and GF7. The four GF satellites had high SNR and RRCA but low dynamic MTF and variable ARCA for different reflectance features. For example, the average SNRs for the four satellites reached 42.66, 43.91, 44.33, and 47.46 dB; the average RRCA (@root-mean-square errors) values reached 1.6%, 1.0%, 1.6%, and 0.1%; the average MTF values (at Nyquist frequency) of the panchromatic bands were only 0.15, 0.12, 0.07 and 0.04; and the difference in ARCA for different reflectance targets was approximately 10%. In the long time series, the SNRs for each sensor of the four GF satellites were high and stable for high-reflectance features, whereas the RCA and SNR values for low-reflectance features and the MTF of the sensors fluctuated in a cyclic manner. Meanwhile, large dynamic fluctuations and episodic image quality problems occurred at certain local time points, which must be considered during practical applications. The findings presented here will lay a foundation for the in-depth application of GF images.

Determinants of Perceived Product Quality and its Impact on Distributor’s Performance: The Moderating Role of Brand Likeability

This study examines the complex relationship among brand association, image, loyalty, and attachment and perceived product quality. It also examines how brand likeability moderates this relationship. The study investigates these dynamics to determine how consumer-brand interactions affect product quality perceptions. Customers evaluate product quality beyond intrinsic qualities in competitive markets. Brand association, image, loyalty, and attachment form these values. Understudied is brand likeability's moderating role in these correlations. Analyzing consumer-brand interactions on product quality fills this gap. This study uses a quantitative-methods approach. A varied sample of consumers is asked about brand association, image, loyalty, attachment, product quality, and likeability. Analysis of direct and moderated effects uses structural equation modeling (SEM). The study found that brand association, image, loyalty, and attachment significantly impact perceived product quality. Consumers with strong positive associations, a positive brand image, great brand loyalty, and a profound emotional commitment to a brand see its items as higher quality. In particular, brand likeability moderates these relationships. Brand likeability enhances the effects of brand association, image, loyalty, and attachment on product quality. Brand-related characteristics greatly influence customer product quality perceptions when they like a brand. This study provides valuable insights for brand managers and marketers. First, it stresses the necessity of positive brand associations, images, loyalty, and attachment to boost product quality. Second, brand likeability amplifies these brand variables. Brands should provide engaging, realistic, and emotionally appealing experiences to increase likeability. Understanding these relationships can help marketers brand and communicate products, influencing consumer quality perceptions. This study adds to marketing literature by analyzing how brand connection, image, loyalty, and attachment affect product quality. Brand likeability moderates consumer-brand interactions, revealing their complexity. This research helps brands and marketers improve branding and consumer interactions, enhancing product quality and brand success.

An Efficient CNN-based Model for Meat Quality Assessment: The Role of Artificial Intelligence Towards Sustainable Development

The meat industry's pursuit of consistent and accurate quality assessment is essential to meet consumer demands and ensure food safety. Meat freshness is an important consideration when evaluating product quality and safety and has a significant impact on a consumer's purchasing decision. Traditional techniques that depend on visual inspection by humans are arbitrary and prone to discrepancies. In this paper, we propose a new deep learning architecture model based on convolutional neural networks (CNNs) for meat freshness classification. The model was trained on a meat images dataset to classify the red- meat images into two classes named "fresh" or "spoiled”. The proposed model performance is compared against a set of deep transfer learning models. The obtained results show that the proposed model outperformed other models and achieved the best accuracy of 100% and 100% precision and recall in meat images quality classifications.

Increasing Analytical Quality by Designing a Thin-Layer Chromatography Scanner Method for the Determination of the Radiochemical Purity of Radiopharmaceutical Sodium Iodide 131I Oral Solution.

The goal of this study was to apply the principles of analytical quality by design (AQbD) to the analytical method for determining the radiochemical purity (PQR) of the radiopharmaceutical sodium iodide 131I oral solution, utilizing thin-layer chromatography (TLC) with a radio-TLC scanner, which also enables the evaluation of product quality. For AQbD, the analytical target profile (ATP), critical quality attributes (CQA), risk management, and the method operable design region (MODR) were defined through response surface methodology to optimize the method using MINITAB® 19 software. This study encompassed the establishment of a control strategy and the validation of the method, including the assessment of selectivity, linearity, precision, robustness, detection limit, quantification limit, range, and the stability of the sample solution. Under the experimental conditions, the method parameters of the TLC scanner were experimentally demonstrated and optimized with an injection volume of 3 µL, a radioactive concentration of 10 mCi/mL, and a carrier volume of 40 µL. Statistical analysis confirmed the method's selectivity for the 131I iodide band Rf of 0.8, a radiochemical impurity IO3- Rf of 0.6, a linearity from 6.0 to 22.0 mCi/mL, and an intermediate precision with a global relative standard deviation (RSD) of 0.624%. The method also exhibited robustness, with a global RSD of 0.101%, a detection limit of 0.09 mCi/mL, and a quantification limit of 0.53 Ci/mL, meeting the prescribed range and displaying stability over time (at 0, 2, and 20 h) with a global RSD of 0.362%, resulting in consistent outcomes. The development of a method based on AQbD facilitated the creation of a design space and an operational space, with comprehensive knowledge of the method's characteristics and limitations. Additionally, throughout all operations, compliance with the acceptance criteria was verified. The method's validity was confirmed under the established conditions, making it suitable for use in the manufacturing process of sodium iodide 131I and application in nuclear medicine services.

Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality

The quality and safety of agricultural products involve a variety of risk factors, a large amount of risk information data, and multiple circulation and disposal processes, making it difficult to accurately trace the source of risks. To achieve precise traceability of agricultural product quality and safety, the monitoring and supervision level of agricultural product traceability systems needs to be improved. In this paper, the agricultural product quality- and safety-related detection data are taken as the research object, the agricultural product supply chain and business process are sorted out, the agricultural product risk factors under the influence of heavy metals are analyzed, and the agricultural product quality and safety risk evaluation model is established. The agricultural product risk traceability system was designed based on the six-layer architecture of the equipment layer, data layer, service layer, application layer, display layer, and user layer. Combined with the agricultural product quality and safety risk evaluation model, the system data were stored and modified by using the MySQL relational database, and the agricultural product risk traceability system was developed, which realized the accurate traceability of the risk link of the quality and safety of agricultural products. The research results of this paper provide an effective information means for the quality and safety supervision of agricultural products, and provide a technical reference for the development of a traceability system of risk factors related to agricultural products, which has good practical significance.

Application of a property prediction model based on the structure oriented lumping method in the fluid catalytic cracking process

Based on the Structure Oriented Lumping (SOL) method and the Artificial Neural Network (ANN) algorithm, a SOL-ANN property prediction model was constructed to predict the properties of molecules and products in the fluid catalytic cracking (FCC) process. The properties of each structural vector in the molecular composition matrices of gasoline and diesel were calculated. The influences of reaction temperature on the properties of gasoline and diesel were investigated from the perspective of molecular composition. When the reaction temperature increased from 490 °C to 510 °C, the content of aromatics and olefins in gasoline and the content of aromatics in diesel increased, resulting in the research octane number (RON) of gasoline increasing by 2.96 units and the cetane number (CN) of diesel decreasing by 1.37 units. Using the molecular composition information of products to calculate the properties of molecules and products could guide the product quality evaluation and process optimization.

Quality Evaluation of Satellite Sea Surface Salinity Products in the Pacific Ocean

Sea surface salinity (SSS) is one of the most important variables in ocean dynamics and atmospheric climate. The launch of three salinity satellites, Aquarius, SMAP and SMOS, has greatly expanded the global sea surface salinity data field. The latest ocean surface salinity (CCI+SSS) fusion project fully utilizes the satellite data from SMOS, supplemented by SMAP and Aquarius satellite data, to merge into the CCI fusion product. In this paper, the data accuracy of the four SSS products, Aquarius, SMAP, SMOS and CCI, is evaluated in terms of both average deviation and root mean square error (RMSE) by comparing with the in situ SSS monthly gridded EN4.2.2 dataset in the Pacific Ocean. The results show that compared with the in situ EN4.2.2 data, the average deviation and root mean square error of the four SSS products are relatively small in the Pacific subtropical region, while the errors are generally higher in the cold water and offshore regions. Overall, the CCI product has the smallest average deviation and RMSE in the Pacific Ocean, which is the best performance among the four products.

Effect of goji berry incorporation on the texture, physicochemical, and sensory properties of wheat bread.

The regular intake of Lycium barbarum (goji berry) is supposed to play an important role in the promotion of human health. Regarding, its incorporation into staple foods, including bread, seems to be effective. However, it requires the evaluation of dough behavior and final product quality. This study investigated the effect of goji berry incorporation at levels of 10, 15, 20, 25, and 30% ww-1 on the textural, physicochemical, and sensory properties of wheat bread. Results indicated a significant enhancement of water absorption and gelatinization temperature in composite flour via the inclusion of goji berry powder (p < 0.05). Using goji berry powder up to 20% ww-1 has shown to obtain the structure able to restore gases through the baking process and provide enhancement in a specific volume at about 10%. Alongside, the hardness of composite bread decreased, and the optimal hardness was observed at formulations containing 20% w/w goji berry powder with a value equal to 1199.95 ± 0.05 g, which is supposed to be induced by the higher specific volume and lower moisture content of bread samples. Moreover, color and sensory perception have been found to be significantly changed by goji berry substitution. Goji berry substitution up to 20% ww-1 is found to be preferred by the consumer, and a drop in overall acceptability was observed at its higher inclusion. The technological characteristic changes induced by goji berry incorporation are induced by its gluten dilution impact. However, the gel-like structure formed by the high fiber content of goji berries compensates for this adverse impact up to 20% w/w substitution level.

Radio frequency-assisted hot air drying of pacific white shrimp (Litopenaeus vannamei): drying kinetics, product quality and composition evaluation

ABSTRACT The influence of radio frequency-assisted hot air drying (RFHAD) on the drying kinetics, product quality and composition of Pacific white shrimp was evaluated and compared with radio frequency drying (RFD) and hot air drying (HAD). The results demonstrated that RFHAD showed faster drying rate and also ensured better product quality in terms of texture property, astaxanthin content, color quality, rehydration rate and microstructure of shrimp. Furthermore, RF wave could significantly reduce the formation of bitter amino acids (p < .05) and showed no significant difference in the content of umami amino acid. However, secondary structure changes were observed in RFHAD shrimp with a decrease in β-sheet and α-helix contents and increase in β-reverse fold, and RFHAD noticeably decreased the onset temperature, peak temperature and thermal enthalpy (ΔH) values of shrimp, which showed lower thermal stability. Therefore, the RFHAD method is a fast and efficient drying technology for shrimp with high-quality characteristic.

Penerapan Metode SWOT dan BOS dalam Menilai Peluang Usaha Sempol Ayam di Kota Mataram

MSMEs are productive businesses managed by individuals, groups, households, or small business entities that are realized according to assets and / or sales profits per year. The culinary industry is a type of MSME that has a fairly broad market. One of them is a traditional snack business that is quite popular in the community with low prices and raw materials that are easily obtained is chicken sempol. Sales of chicken sempol are dominated by micro and small enterprises. An effective strategy by MSME owners is needed through the use of SWOT (Strength, Weakness, Opportunity, Threat) and BOS (Blue Ocean Strategy) methods. SWOT aims to analyze the internal and external conditions of MSMEs, while BOS aims to improve product quality and innovation so that petitions can be minimized. This service activity will be carried out in October 2023, at MSMEs Cilok vs Sempol Goyang Senggol, Jl. Panji Tilar Negara, Kekalik, Mataram City, West Nusa Tenggara. The results of the analysis show that it is necessary to evaluate product quality, flavor variants, and marketing strategies. Based on this evaluation, it becomes a strategy by MSME owners in considering it as a variable to be innovated in marketed products.

Modified MABAC for MCGDM with interval-valued neutrosophic sets and application to design quality evaluation of digital cultural and creative products

With the development of digital creative industry and the use of more emerging digital technologies, the forms of digital cultural and creative design products are also increasingly diversified. Unlike traditional cultural and creative design products, digital cultural and creative design products are no longer limited to physical products, but appear more in the field of exhibition, virtual reality and product visualization. At the initial stage of the combination of digital information technology and cultural and creative content, digital cultural and creative design products, unlike ordinary cultural and creative design products, opened a new vision for users. The design quality evaluation of digital cultural and creative design products is viewed as a multi-criteria group decision-making (MCGDM). The single-value neutrosophic sets (SVNSs) concept and its interval-valued version (Interval-valued neutrosophic sets, IVNSs) are within the recent rapid developments for managing the uncertain representation problem in MCGDM. In SVNSs, decision makers (DMs) could portray membership, non-membership and hesitancy. IVNSs expands this useful feature through portraying intervals to these three information decision degrees. In this manner, the uncertainty, ambiguity and vagueness hidden in human judgements could be quantified more efficiently. IVNSs have been widely employed and researched in MCGDM. The main purpose of this paper is to proposed the Interval-valued neutrosophic number MABAC (IVNN-MABAC) technique based on prospect theory (PT) to address the MCGDM. Eventually, an example for design quality evaluation of digital cultural and creative design products and some comparative analysis was employed to demonstrate the superiority of the designed technique.

A software trustworthiness evaluation methodology for cloud services with picture fuzzy information

VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is one of the most commonly used decision-making technique. This work provides a software trustworthiness evaluation method based on VIKOR technique in a group decision-making (GDM) environment. Entropy is an important measure that refers to the amount of disorder in a decision support system. To quantify the decision quality of decision maker (DM), a new method for determining DM weights is proposed according to the entropy value of decision matrix, which is provided by that DM. A new normalization-projection measure is developed in order to test the closeness between two evaluation matrices in software trustworthiness evaluation process. The group regret measure in VIKOR-based GDM method is based on two negative reference matrices in this model. A new ranking method with classification is also developed in this work. Aiming at the complex and changeable environment, these new measures and methods are expected to enrich VIKOR techniques and to play a constructive role in group decision support systems. The feasibility and practicability of developed method are verified by some experiments. This study finds that the different methods (models) may have different results in situations where the same decision data are provided by DMs; and this study also finds that the different parameters may have different results in situation where the decision model is same. The practical implications of developed GDM method can apply not only to software trustworthiness evaluation, but also to many other product quality evaluations.

Recent progress of cataluminescence sensing based on gas-solid interfaces.

Cataluminescence (CTL) has emerged as a sensing transduction principle of gas-solid interface for constructing gas sensors that present fast response, high sensitivity, and online monitoring. It has thus been widely associated with the field of chemical analysis and catalytic science. Herein, the latest developments in CTL sensors are reviewed, and the status quo of CTL-based gas sensing systems is discussed. In particular, the basic principles and sensing systems of CTL are outlined, including performance enhancement strategies for specific targets and recognition methods for multiple targets. Moreover, the important applications of CTL sensors are listed and classified, including environmental pollutant monitoring, product quality control, clinical diagnosis, and evaluation of catalyst performance. Finally, based on abundant case reports, the current conundrums of CTL sensors are summarized and their future development trends are also put forward.