Quality Evaluation Of Products Research Articles

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.

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

AbstractThe 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.

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.

Development of a Rapid Adeno-Associated Virus (AAV) Identity Testing Platform through Comprehensive Intact Mass Analysis of Full-Length AAV Capsid Proteins.

Adeno-associated viruses (AAVs) are commonly used as vectors for the delivery of gene therapy targets. Characterization of AAV capsid proteins (VPs) and their post-translational modifications (PTMs) have become a critical attribute monitored to evaluate product quality. Liquid chromatography-mass spectrometry (LC-MS) analysis of intact AAV VPs provides both quick and reliable serotype identification as well as proteoform information on each VP. Incorporating these analytical strategies into rapid good manufacturing practice (GMP)-compliant workflows containing robust, but simplified, data processing methods is necessary to ensure effective product quality control (QC) during production. Here, we present a GMP-compliant LC-MS workflow for the rapid identification and in-depth characterization of AAVs. Hydrophilic interaction liquid chromatography (HILIC) MS with difluoroacetic acid as a mobile phase modifier is utilized to achieve the intact separation and identification of AAV VPs and their potential proteoforms. Peptide mapping is performed to confirm PTMs identified during intact VP analysis and for in-depth PTM characterization. The intact separations platform is then incorporated into a data processing workflow developed using GMP-compliant software capable of rapid AAV serotype identification and, if desired, specific serotype PTM monitoring and characterization. Such a platform provides product QC capabilities that are easily accessible in a regulatory setting.

Principle and applications of near‐infrared imaging for fruit quality assessment—An overview

SummaryThe quality (i.e. the degree of excellence and acceptance to the consumer) evaluation and safety of fruit and other agricultural products is by far the main challenging task in the food industries. Hence, the development of efficient non‐invasive, real‐time and rapid‐quality assessment tools and techniques is the need of the hour. This review paper aims to provide the basic or fundamental knowledge regarding the principle, potential and application of near‐infrared imaging‐based computer vision systems. Non‐invasive technologies, based on optical properties, are the most applicable and important for real‐time quality assessment. Absorption, reflection and scattering before the transmission of incident light are phenomenon properties of biological materials. The principle and application of NIR imaging (i.e. medical imaging) in agriculture for quality detection were studied from various published research papers. Thus, collected pieces of information are summarised, concluded and discussed in a unique way in this report. The NIR spectrum of electromagnetic waves has some basic benefits of light penetration into the upper layer and makes visualisation of the targeted area of objects. Several studies have proved that the non‐destructive system based on visible and non‐visible imaging in combination has a significant role in quality defect detection in fruits. In NIR imaging, the artefacts caused by light illumination can be reduced and it will help in the visualisation of biological tissues and hidden defects just below the peel.

Description of tea quality using deep learning and multi-sensor feature fusion

To address the problem of the lack of rapid discrimination technology for comprehensive evaluation of product quality and grade in the field of tea quality control, this study uses near-infrared spectroscopy, electronic eye, electronic tongue, and electronic nose technology to sense the external and internal quality sensing characteristics of tea leaves, and realizes comprehensive evaluation of four quality factors such as "color, aroma, taste and shape" of tea leaves through cross-sensor multimodal quality characteristics data fusion. For the first time, a comprehensive method for assessing the grade quality of tea based on deep learning algorithms combining cross-sensor multimodal fusion features such as color, texture, shape, optical, electronic tongue, and electronic nose is established. The results showed that the constructed deep convolutional neural network (CNN) classification model had a misclassification rate of only 0.86% in the prediction set, which was better than the modeling results based on traditional classification methods. This study reveals that the CNN algorithm can effectively evaluate the quality grade of tea samples and realize the digital evaluation of multiple evaluation factors of tea sensory quality.

Cascaded detection method for surface defects of lead frame based on high-resolution detection images

In the field of semiconductor production and manufacturing, the detection of defects on lead frame surfaces is a vital process. This process plays a key role in ensuring the quality of the final product. Using high-resolution detection images to detect multi-scale tiny surface defects is necessary, but this amplifies the impact of environmental noise. Therefore, suppressing both the false negative rate and false positive rate in practical detection scenarios is a challenge that needs to be overcome. Current research on lead frame surface defect detection is mostly concentrated on the downloaded standard original images, which limits its application in actual production lines. This paper presents a cascaded detection method for surface defects of lead frame based on high-resolution detection images. Firstly, this study presents the unit cell extraction module to convert the detection object from high-resolution image to hundreds of unit cells. The proposed module can handle real-time detection images in the production pipeline, especially addressing situations such as lighting imbalances and tilted detection images. Subsequently, this study proposes a lead frame surface defect detection network (LDD-net), which takes unit cells as inputs and can effectively detect multi-scale defects. Compared to other models, LDD-net can effectively capture the features of subtle defects. Additionally, this paper introduces the deviation in the central width direction into the CIoU localization loss, enhancing the accuracy of defect localization in LDD-net. The data set is constructed using the machine vision detection system and conducts training and testing. Specifically, experiments of LDD-net on the data set obtained 85.01% mean average precision (mAP) and 37 ms of inference time, respectively. The detection accuracy exceeds 95%, and the false negative rate can be controlled below 6%. This approach will assist manual monitoring personnel in evaluating product quality.

Construction of acceptance sampling schemes for exponential lifetime products with progressive type II right censoring

Ensuring product quality is crucial for enhancing a company's competitiveness, and product lifetime serves as a vital metric, particularly in the context of electronic goods, for evaluating product quality. To meet quality requirements and mitigate risks for both producers and consumers, thorough examinations of components and final products are necessary. Acceptance sampling plans are commonly employed to achieve these objectives. When assessing product lifetime under non-normal distributions, the lifetime performance index emerges as a valuable and frequently utilized metric. In practical scenarios, lifetime tests may encounter challenges such as the absence of experimenters to observe all product lifetimes and the possibility of accidental product breakage. To address these issues, this study introduces two sampling schemes: a single sampling plan and a repetitive group sampling plan. These plans are specifically designed for exponential lifetime products, utilizing the progressively type II right censored sample as the data type for lifetime testing. The study provides tabulated plan parameters for practical implementation and presents a comparative analysis between the two proposed plans. Furthermore, a practical example is presented to demonstrate the efficacy of the developed sampling plans.

Mitigation of polycyclic aromatic hydrocarbons (PAHs) in roasted beef patties by cold plasma treatment and products quality evaluation

Mitigation of polycyclic aromatic hydrocarbons (PAHs) in roasted beef patties by cold plasma treatment and products quality evaluation

Machine-vision based quality evaluation of meat and meat products- a review

Machine-vision based quality evaluation of meat and meat products- a review