Key Quality Characteristics Research Articles

Fuzzy Evaluation Model for Operational Performance of Air Cleaning Equipment

Global warming has led to the continuous deterioration of the living environment, in which air quality directly affects human health. In addition, the severity of the COVID-19 pandemic has further increased the attention to indoor air quality. Indoor clean air quality is not only related to human health but also related to the quality of the manufacturing environment of clean rooms for numerous high-tech processes, such as semiconductors and packaging. This paper proposes a comprehensive model for evaluating, analyzing, and improving the operational performance of air cleaning equipment. Firstly, three operational performance evaluation indexes, such as the number of dust particles, the number of colonies, and microorganisms, were established. Secondly, the 100(1− α)% upper confidence limits of these three operational performance evaluation indexes were deduced to construct a fuzzy testing model. Meanwhile, the accumulated value of ϕ was used to derive the evaluation decision-making value. The proposed model can help companies identify the key quality characteristics that need to be improved. Furthermore, the competitiveness of cooperative enterprises towards smart manufacturing can be strengthened, so that enterprises can not only fulfill their social responsibilities while developing the economy but also take into account the sustainable development of enterprises and the environment.

Innovative insights into the roasting-driven transformation of volatile compounds and quality markers in flaxseed (Linum usitatissimum L.) oil.

Roasting is essential for developing the characteristic aroma of flaxseed oil (FSO), yet its impact on oil quality remains underexplored. This study employed headspace-gas chromatography-mass spectrometry coupled with multivariate analysis to elucidate the dynamic changes in volatile compounds and quality characteristics of FSO subjected to varying roasting temperatures. Our findings revealed that seven key aroma compounds, identified through the variable importance in the projection scores of partial least square-discrimination analysis models and relative aroma activity value, served as molecular markers indicative of distinct roasting temperatures. These compounds included 2,5-dimethylpyrazine, 2-pentylfuran, (E)-2-pentenal, 2-ethyl-3,6-dimethylpyrazine, heptanal, octanal, and 2-hexenal. Notably, roasting at 200°C was found to enhance oil stability and antioxidant capacity, with phenolic compounds and Maillard reaction products playing synergistic roles in bolstering these qualities. Network analysis further uncovered significant correlations between these key aroma compounds and quality characteristics, offering novel perspectives for assessing FSO quality under diverse roasting conditions. This research not only enriched our understanding of the roasting process's impact on FSO but also provided valuable guidance for the optimization of industrial roasting practices. This study would provide important practical applications in aroma regulation and process optimization of flaxseed oil. .

Integrated multi-omics analysis provides molecular insights into flavor variation in melons grafted onto two different pumpkin rootstocks during fruit development

Melon fruit flavor is a key quality characteristic that influences consumer preference. Grafting is an effective technique to enhance fruit quality but yields divergent outcomes in terms of fruit flavor. To address this problem, we analyzed parallel changes in flavor-related metabolite accumulation and gene expression in two pumpkin rootstock grafted melons during four fruit developmental stages. We identified 26 061 expressed genes and 840 metabolites from 21 different compound classes, including carbohydrates, amino acids, and lipids. We also detected 50 aroma volatile compounds in the grafted melons. Results showed that genes and metabolites associated with metabolic pathways (carbohydrate, amino acid, lipid, and phenylpropanoid) play a key role in flavor formation. Compared with ‘Sizhuang 12’, ‘Tianzhen 1’ rootstock improved melon fruit flavor by upregulating sugar-related genes (HK, MPI, MIOX, and STP) and inducing metabolite accumulation (d-ribose-5-phosphate, d-galactose, and trehalose 6-phosphate), whereas decreasing bitterness-related amino acids (l-arginine, l-asparagine, and l-tyrosine) and associated genes (thrC, ACS, and GLUL) expression at ripening stage. Furthermore, ‘Tianzhen 1’ exhibited higher expression levels of enzyme-coding genes (4CL, CSE, and COMT) responsible for aroma volatile synthesis than ‘Sizhuang 12’ rootstock. Taken together, our results decipher the basis of the molecular mechanism underlying fruit flavor in grafted melons and provide valuable information for the melons genetic improvement.

Restoring historical buildings using additive manufacturing

This paper considers using additive manufacturing in restoration projects. This research proof that Polyblend Calcium Carbonate (Grout) powder can be used as a building material on the ZPrinter 450 Binder Jetting 3D printer. Such material has promising applications in restoring historical structures. Using statistically designed experiments, this research investigated the effect of the layer thicknesses, binder saturation, curing solution, and curing time on 3D-printed tiles. The breaking strength, deviation from the nominal dimension, and surface roughness were measured as the key quality characteristics of printed tiles. The results were confirmed using additional runs. Research findings could be instrumental in offering unprecedented capabilities to preserve and revitalize historical structures and architectural treasures. These innovative research efforts will be extremely instrumental in the field of 3D printed customized tiles, façades restoration, and decorative calligraphy tiles.

Effect of deodorization conditions on fatty acid profile, oxidation products, and lipid-derived free radicals of soybean oil

Oxidative stability is a key quality characteristic of edible oils, and the oil’s antioxidant capacity decreases during the deodorization stage. This study explores the changes in radical formation, molecular structure, oxidative characteristics, fatty acids, and main bioactive compounds in soybean oil during deodorization. The lag phase decreased, whereas the total amount of spins of free radicals increased as the deodorization time increased from 90 to 150 min. The total amount of spins and percentage of alkyl radicals varied dramatically under different times and temperatures (220 ∼ 260 ℃). Results showed that identifying and quantifying the formed radicals can provide useful information for monitoring and controlling oil oxidation in vegetable oil refining systems. Therefore, to control early oxidation events, maximize refined oil product yield, and reduce energy consumption in the refining plant, the priority should be to minimize temperature during the oil refining process and then shorten the deodorization time.

A connecting rod assembly deformation cognition method based on quality characteristics probability network

As the core component of marine diesel engines, the connecting rod quality directly affects the reliability and life of diesel engines. However, the deformation problem of the connecting rod assembly process, which caused the low pass rate of one-time assembly, has not been well solved. The deformation of connecting rod assembly is difficult to analyze by mechanism because it is influenced by many quality characteristics with complex and cross-process correlations. To address this issue, this paper proposes a connecting rod assembly deformation cognition method based on the quality characteristics probability network. Firstly, a quality characteristics probabilistic network model for the connecting rod assembly process is established. Secondly, based on the quality characteristics probability network model and the complexity of cross-process correlation, the K-shell entropy weight model is used to identify the key quality characteristics and construct their correlation model. Then, to deal with the uncertainty of the influence degree between quality characteristics, a Bayesian network is used to establish an influence analysis model of key quality characteristics and analyze their influence relationship from qualitative and quantitative perspectives. Finally, the simulation and actual assembly data of marine diesel engine connecting rod assembly process are used to train and verify the cognition method. The results show that the method can effectively analyze the relationship between the connecting rod assembly process quality characteristics and predict the connecting rod deformation degree. The proposed method can comprehensively consider the influence of multiple processes and quality characteristics, improve the accuracy of analysis and prediction of connecting rod deformation, and can be used to guide the dynamic adjustment of the connecting rod assembly process to increase the pass rate of one-time. Moreover, the proposed method can also provide a reference for other multi-process and multi-factor problem analyses.

Evaluation of different DEWMA schemes for monitoring the ratio of two normal random variables

In many fields such as baking industry and cosmetic factory, the ratio of two normal random variables (RZ) is one of the key quality characteristics that needs to be monitored. In this paper, we study the properties of three double exponentially weighted moving average (DEWMA) schemes for monitoring the RZ. The run length (RL) properties, average run length (ARL) and standard deviation of run length (SDRL), and charting parameters of the proposed DEWMA RZ schemes are simulated for different correlation parameter between the two normal variables. These numerous simulation results are presented in tables and figures to make comparisons with the existing exponentially weighted moving average (EWMA) RZ scheme. The comparisons show that the proposed DEWMA RZ schemes are more effective than the EWMA RZ in detecting small changes. Additionally, the proposed three one-sided DEWMA RZ schemes’ properties are compared in detail. A food industry example details the implementation of the proposed RZ schemes.

Reliability oriented key quality characteristics driven integrated built-in reliability activity chain and approach for manufacturing process

Product reliability is determined at the design phase and built into the product through manufacturing process. To effectively implement reliability activities at each stage, reliability data in product life cycle is integrated with KQC (key quality characteristics), and the concept of R-KQC (reliability-oriented KQC) is proposed. Based on the R-KQCs, this study presented an integrated Built-in reliability (BIR) approach to ensure that the product reliability formed losslessly conforms to the reliability goal determined in the design phase. First, the connotations of BIR and BIR-oriented RQR (manufacturing system Reliability, manufacturing process Quality, and product Reliability) chain are proposed by considering the evolution mechanism of product reliability in the life cycle driven by the R-KQCs. Second, a product BIR activity chain consisting of plan, synthesis, and improvement based on R-KQC is presented by the BIR-oriented RQR chain. Third, an R-KQC-driven BIR assurance technology framework based on the BIR activity chain is proposed, and a product reliability modeling considering the deviation of R-KQCs is established based on the framework. Finally, the validity of the proposed approach is verified using the reliability assurance for an automobile engine cylinder head as an example.

Properties, Structure, and Acceptability of Innovative Legume-Based Biscuits with Alternative Sweeteners.

The effects of legume incorporation and sweetener substitution on the quality characteristics of innovative biscuits were investigated. The wheat flour was substituted with chickpea and lentil flour at ratios ranging from 0 to 30% legume to whole-meal dicoccum wheat flour. The sugar was substituted by oligofructose at 50 and 100% levels. The quality characteristics, including physicochemical properties (moisture content, water activity, and color), sorption characteristics, structural and textural properties, and sensory properties, were significantly affected by the substitutions. Sorption phenomena were excellently described by the Guggenheim, Anderson, and de Boer (GAB) model, while its parameters were affected by substitutions. Scanning electron microscopy revealed a porous structure with starch granules embedded within the protein matrix, showing restricted gelatinization and keeping largely their form. The incorporation of legume flour increased the biscuit density, hardness, and spread ratio and decreased the color of the products. Furthermore, principal component analysis (PCA) analysis of instrumental and sensory characteristics showed that texture and sweetness were the key quality characteristics for product acceptance. It was found that highly acceptable legume-based biscuits with alternative sweeteners can be produced, with 50% oligofructose substitution and legume flour incorporation (chickpea or lentil) up to 30%.

Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy.

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

AAPM Task Group Report 311: Guidance for performance evaluation of fluorescence-guided surgery systems.

The last decade has seen a large growth in fluorescence-guided surgery (FGS) imaging and interventions. With the increasing number of clinical specialties implementing FGS, the range of systems with radically different physical designs, image processing approaches, and performance requirements is expanding. This variety of systems makes it nearly impossible to specify uniform performance goals, yet at the same time, utilization of different devices in new clinical procedures and trials indicates some need for common knowledge bases and a quality assessment paradigm to ensure that effective translation and use occurs. It is feasible to identify key fundamental image quality characteristics and corresponding objective test methods that should be determined such that there are consistent conventions across a variety of FGS devices. This report outlines test methods, tissue simulating phantoms and suggested guidelines, as well as personnel needs and professional knowledge bases that can be established. This report frames the issues with guidance and feedback from related societies and agencies having vested interest in the outcome, coming from an independent scientific group formed from academics and international federal agencies for the establishment of these professional guidelines.

Comprehensive extraction technology of key quality characteristics of meta-action unit for CNC machine tools design

The QCs (quality characteristics) of MAU (meta-action unit) determine the quality of CNC (computer numerical control) machine tools. For improving the accuracy of MAU QCs analysis, and solving the problems that the current methods either only have subjective analysis, or only have objective analysis, or the analysis process that includes both subjective and objective analysis methods is relatively complex, a comprehensive identification method of MAU QCs according to the improved MAHP (multiplicative analytic hierarchy process) method and Shannon entropy is proposed in this paper. Firstly, the QCs of the MAU are divided into quality layer and index layer. Secondly, the expert evaluation weight is introduced to improve the MAHP, then the sub quality layer of the meta-action unit was subjectively analyzed. Thirdly, the index layer of the MAU is objectively analyzed by the Shannon entropy. Finally, the results obtained by WMAHP (weighted multiplication analytic hierarchy process) method and Shannon entropy method are synthesized, and the Pareto principle is adopted to extract the key QCs of the MAU. The proposed method combines the advantages of both subjective and objective analysis, and it can reduce the fluctuation of the analysis results caused by data changes. Meanwhile, the analysis process is relatively simple. Its applicability and superiority have also been confirmed by an experiment and comparisons with various methods. The proposed method is of great significance to improve the quality of CNC machine tools.

Determination of quality traits and possible adulteration of molasses using FT-IR spectroscopy: A study from Turkish market.

We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n=27), grape (n=24), Juniper (n=13), and mulberry (n=12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R2Val≥0.96) and low standard error of prediction (SEP≤2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.

Microbial Community Succession and Its Correlation with Quality Characteristics during Gray Sufu Fermentation.

Gray sufu, a traditional fermented food derived from soybeans, undergoes a complex fermentation process. This study aimed to investigate the dynamics of the microbial community during sufu fermentation and its relationship with key quality characteristics. Through systematic sampling of sufu at different phases of fermentation, 143 bacterial genera and 84 fungal genera involved in the process were identified. Among these, Chishuiella, Enterococcus, Lactococcus, and Weissella emerged as the predominant bacterial communities. After seven days of ripening fermentation, Trichosporon supplanted Diutina as the predominant fungus, accounting for more than 84% of all fungi. Using redundancy analysis, significant correlations between microbiota and physicochemical properties were uncovered. Chishuiella and Empedobacter displayed positive relationships with pH, soluble protein, and amino nitrogen content. In addition, five biogenic amines were detected, and it was determined that tyramine accounted for more than 75% of the total biogenic amines in the final gray sufu products. Spearman correlation analysis revealed significant positive relationships between Lactococcus, Enterococcus, Tetragenococcus, Halanaerobium, and Trichosporon and the five biogenic amines examined. These findings shed light on the complex interactions between microorganisms and biogenic amines during the fermentation of gray sufu, thereby facilitating the development of microbial regulation strategies for better quality control.

Quality Characteristics of Rice-Based Ice Creams with Different Amylose Contents

Ice cream consumption has increased over the years. In this study, we investigated the potential of using rice varieties with varying amylose contents for ice cream production. We analyzed the physical and chemical properties and sensory quality characteristics (appearance, taste, texture, chewiness, aroma, and rice flavor) of rice-based ice cream made from five varieties with low and high amylose levels. To make the ice cream, we ground rice into a fine powder and combined it with skim milk powder, butter, sugar, glycerin esters of fatty acids, locust bean gum, and water to form a gelatinized mixture. This mixture was then aged, frozen, and hardened. The ice cream's key quality characteristics, such as viscosity (2170-25,030 cP), hardness (4.27-49.55 N cm-2), and overrun (17.95-46.99%), showed a wide range. Ice cream made from Saemimyeon (high amylose content rice variety) exhibited the highest hardness value (49.55 N cm-2) among the varieties tested, but had relatively low viscosity (4030 cP), overrun (17.95%), and drip-through (0.75 g/min) values. These findings suggest that rice varieties with different amylose contents are suitable for making ice cream and have the potential to expand the rice processing market and increase its value.

Integrated volatilomic profiles and chemometrics provide new insights into the spatial distribution and aroma differences of volatile compounds in seven Toona sinensis cultivars

As a popular vegetable, the unique and pleasant aroma is the key quality characteristic of Toona sinensis. To explore the sources and differences of aroma, the volatilomic profiling in the leaves and shoots of seven T. sinensis cultivars were investigated by chemometric analysis. The results indicated that aroma differences of each cultivar can be distinguished by W5S, W1S, W1W and W2S sensors during E-nose analysis. More than two thirds of all volatile organic compounds (VOCs) were derived from the leaves of most cultivars, except for Ximu toon. Notably, 2-mercapto-3,4-dimethyl-2,3-dihydrothiophene, 3,4-dimethyl thiophene, methyl thiirane, isocaryophyllene and hexanal were the major VOCs in both the leaves and shoots of T. sinensis. By constructing a weighted correlation network model, 5 modules and 11 hub VOCs were identified in the leaf samples of all cultivars. The data indicate that differential intracellular metabolic responses are responsible for the aroma formation of seven T. sinensis cultivars.

Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.

Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing Using a Linear Programming Method: A Conceptual Framework

At present, the selection of optimal technological parameters for laser powder bed fusion (LPBF) is determined by the requirements of the fusion process. The main parameters that are commonly varied include laser power (P), scanning speed (v), hatch spacing (h), and layer thickness (t). The productivity of the LPBF process (the increment in the fused volume of the material) is equal to the product of the last three parameters, and the mechanical properties are largely determined by the volumetric fusion energy density, which is equal to the ratio of laser power to productivity. While ensuring maximum process productivity, it is possible to obtain acceptable quality characteristics—mechanical properties, surface roughness, etc.—for a certain range of LPBF technological parameters. In these cases, several quality characteristics act as constraints on the optimization process, and productivity and the key quality characteristics become components of the objective function. Therefore, this article proposes a formalized representation of the optimization problem for the LPBF process, including the derivation of the objective function with the constraint matrix, and provides a solution to the problem using the linear programming (LP) method. The advantages of the proposed method include the guaranteed convergence of the solution with an unlimited number of constraints; the disadvantage concerns the adequacy of the solution, which is limited by a relatively narrow range of parameter changes. The proposed method was tested in determining the optimal LPBF parameters for an HN58MBYu powder LP model that included 13 constraints and an objective function with two target parameters. The obtained results made it possible to increase the productivity by 15% relative to the basic technological parameters.

A decomposition-based multi-objective particle swarm optimization algorithm with a local search strategy for key quality characteristic identification in production processes

Identifying the key quality characteristics (KQCs) (including part and process parameters) in production processes is essential for quality control. In this paper, we propose a data-driven KQC identification method based on production process data. We model KQC identification as a multi-objective feature selection problem of maximizing the geometric mean (GM) and minimizing the number of selected QCs (features). GM can evaluate the importance of a QC subset by measuring its predictive ability for product quality. To solve this optimization model, we propose a multi-objective optimization algorithm called MOPSO-LS that combines particle swarm optimization (PSO) with a local search strategy. MOPSO-LS adopts a decomposition approach, i.e., Tchebycheff approach (TA), to update personal best positions (pbests) during the iterations. Thus, diversified and high quality solutions can be maintained by the pbests of particles. Moreover, the local search strategy aims to update the non-dominated set found by MOPSO-LS during the iterations with two basic local search steps, i.e., a) adding and b) removing a feature, which can improve the convergence performance of MOPSO-LS. We have verified the proposed method on four production datasets. The experimental results indicate that MOPSO-LS can select a few KQCs with a good capability for predicting product quality, which shows the effectiveness of MOPSO-LS for KQC identification. Further comparisons show that MOPSO-LS obtains better search performance than four typical multi-objective optimization algorithms.

Meta-action-oriented collaborative allocation optimization for accuracy-related key quality characteristics of CNC machine tools

Meta-action-oriented collaborative allocation optimization for accuracy-related key quality characteristics of CNC machine tools