Quality Evaluation Of Products Research Articles

Analysis and Research on the Marketing Strategy of Agricultural Products Based on Artificial Intelligence

With the gradual development of artificial intelligence (AI), the traditional production, marketing, and management methods for agricultural products have undergone dramatic changes, necessitating a greater optimization of these methods. Agricultural product operators have begun incorporating AI technology into product production, marketing, and distribution processes. This article examines the current state of agricultural product management and then investigates the integration of production, marketing, and distribution using artificial intelligence. In addition, given the limitations of conventional methods for classifying agricultural products, this article presents a classification model that combines factor analysis with an enhanced support vector machine (SVM) based on genetic algorithms (GAs). The results of the experiments indicate that the improved method is capable of distinguishing agricultural product quality categories rapidly and precisely, significantly improving the classification accuracy of agricultural product quality, and being broadly applicable to the evaluation of agricultural product quality.

Intelligent pH-sensing film based on polyvinyl alcohol/cellulose nanocrystal with purple cabbage anthocyanins for visually monitoring shrimp freshness

We aimed to prepare a new pH-sensing film based on the immobilization of purple cabbage anthocyanins (PCA) into Polyvinyl alcohol (PVA) reinforced by cellulose nanocrystals (CNC). FT-IR, XRD and TGA were used to assess the intermolecular interactions and thermo-stability of films. The addition of CNC and PCA resulted in an enhancement in UV–vis barrier, mechanical properties and moisture resistance. Inclusion of PCA imparted intelligent properties to the films. PCA-loaded films displayed strong visually detectable colorimetric responses to pH (2−13) and volatile ammonia. When applied to monitor shrimp freshness at 4 °C, PVA/CNC films containing 0.6 % PCA exhibited conspicuous color fluctuations from purple to gray blue upon deterioration. As a result, PVA/CNC-PCA colorimetric films were considered as intelligent packaging labels with significant mechanical, water vapor barrier properties and pH-sensing qualities for visual quality evaluation of fresh seafood products.

Design of product quality scales for conveying information by infomediaries

Infomediaries, such as movie critics, often evaluate product quality using discrete grades, such as thumbs-up and thumbs-down. Their reports are used by potential consumers to decide whether to purchase the product. Our aim is to examine how the grading can be improved by answering research questions such as: What is the quality cutoff for each grade? How many grades should the grading scale use? How should grading be done for other goals than informativeness, such as maximizing reviewer’s traffic? How should grading be done to encourage producers to improve quality? Using an analytical model, we show that optimal grade cutoffs depend on the distributions of product quality and consumer taste, and the resulting scale may have unequal intervals. As few as five grades can be near-optimal for consumer welfare and grades should be made more difficult to achieve when producers can invest in quality improvement. These results should help improve the design of scales for reporting quality.

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Consumer demand for high-quality healthy food is increasing; therefore, meat processors require the means toassess their products rapidly, accurately, and inexpensively. Traditional methods for quality assessments are time-consum-ing, expensive, and invasive and have potential to negatively impact the environment. Consequently, emphasis has been puton finding nondestructive, fast, and accurate technologies for product composition and quality evaluation. Research in thisarea is advancing rapidly through recent developments in the areas of portability, accuracy, and machine learning.Therefore, the present review critically evaluates and summarizes developments of popular noninvasive technologies(i.e., from imaging to spectroscopic sensing technologies) for estimating beef, pork, and lamb composition and quality,which will hopefully assist in the implementation of these technologies for rapid evaluation/real-time grading of livestockproducts in the near future.

Fuzzy based poultry production quality evaluation

Poultry production is agriculture-based subsidiary business for farmers. Different factors like any kind of disease symptoms, toxic agents, viral infections and many more factors affect the quality of poultry production. The study reveals the systematic framework for analysis of quality of poultry production. This framework involves fuzzy based method for classification, prioritization of poultry quality decisions against measures to define and validate the novel method and results in data mining. The researcher aims to value work of fiction of poultry production quality evaluation in data mining.

Product quality evaluation by confidence intervals of process yield index

Statistical techniques have a beneficial effect on measuring process variability, analyzing the variability concerning product requirements, and eliminating the variability in product manufacturing. Process capability indices (PCIs) are not only easy to understand but also able to be directly employed by the manufacturing industry. The process yield index offers accurate measurement of the process yield, and it is a function of two unilateral six sigma quality indices. This paper initiates to develop the confidence intervals of the process yield index by using joint confidence regions of two unilateral six sigma quality indices for all quality characteristics of a product. Then integrate these joint confidence regions to find the confidence intervals of the product yield index. All manufacturing industries can use these confidence intervals to make statistical inferences to assess whether the process capability of the product and all quality characteristics has reached the required level, and to grasp the opportunities for improvement. An illustrated example on driver integrated circuit of micro hard disk is provided.

FUZZY COMPREHENSIVE EVALUATION OF LAND COVER CLASSIFICATION DATA

Abstract. The demarcation of the evaluation grade of surveying and mapping products is too rigid, and the evaluation results are often distorted due to the abnormality of an evaluation index. This paper takes the land cover classification data quality evaluation as an example and analyses the quality model, which includes quality factors and weight coefficients. In this paper, a multilevel fuzzy comprehensive evaluation method is introduced, a reasonable and efficient membership function is designed, and the evaluation results are evaluated by using the relative index of effectiveness and the probability of a single value distribution. The results show the fuzzy comprehensive evaluation method based on the premise of suitability weighting can make the evaluation results retain rich information, and the evaluation results are more reasonable and the improved fuzzy comprehensive evaluation method can be applied to the quality evaluation of other natural resource surveys and monitoring products.

Rapid Approach for Pharmaceutical Quality Evaluation and Comparison

AbstractA rapid approach, with the association of Fourier‐transform infrared spectroscopy (FTIR) and nuclear magnetic resonance spectroscopy (NMR), is proposed in order to provide a fast quality evaluation of pharmaceutical products. It offers an analytical screening tool also useful to compare drug products including generics. The approach requires a minimal amount of solvents and little sample preparation. In this paper characterization of pharmaceutical product samples containing telmisartan, allopurinol, anastrozole, metformin hydrochloride and lamivudine, active pharmaceutical ingredients (APIs) in solid dosage forms, are reported. The results show how this approach can provide useful data for a rapid screening of pharmaceutical composition and comparison of drugs with useful information also in terms of equivalence risk analysis.

Quality Evaluation of Ranunculaceae Essential Oil with the Artificial Neural Network

The goal of this project is to use ANN to build a quality evaluation method for Ranunculaceae essential oils in order to assist businesses or individuals in setting up an automated essential oil quality evaluation system and reducing human resource consumption. Using computer software to evaluate the quality of Ranunculaceae essential oils, quantitatively evaluate product quality, and improve the automation of quality evaluation. The main research contents in this paper are as follows: (1) In-depth research on PSO and its development. The purpose of studying the PSO is to use PSO to replace gradient descent algorithm in BP network, so that the neural network model has a better network structure. This paper improves it on the basis of PSO. The basic improvement idea is to add a dynamic random position transformation to the particle, so that global optimization ability can be enhanced. The improved algorithm and the standard algorithm are compared and analyzed in the simulation experiments. The results verify that improved PSO has better optimization ability. (2) Replace training algorithm in the BP network with the IPSO and construct the BP network algorithm model of IPSO. The final implementation of the Ranunculaceae plant essential oil quality assessment model based on the IPSO-BP network was achieved by combining the Ranunculaceae plant essential oil quality evaluation index system with the Ranunculaceae plant essential oil quality evaluation index system. Finally, a simulation experiment was designed to compare the presentation of PSO-BP and IPSO-BP, when they were applied to the evaluation of Ranunculaceae vital oils. The experimental results show IPSO-BP has the highest estimate accuracy and the best performance.

A Nitrite Excipient Database: A Useful Tool to Support N-Nitrosamine Risk Assessments for Drug Products

N-Nitrosamine risk assessment and control have become an integral part of pharmaceutical drug product development and quality evaluation. Initial reports of nitrosamine contamination were linked with the drug substance and its manufacturing process. Subsequently, the drug product and aspects of the formulation process have shown to be relevant. Regarding specific formulation contributions to nitrosamine content in a product, one risk lies in possible interactions between nitrosating agents, derived from nitrite in excipients, and vulnerable amines, either present as moieties of the active molecule or as impurities / degradants. However, the limited validated information on nitrite levels in excipients available until now, has been an obstacle for scientists to assess the risk of nitrosamine formation in pharmaceutical products. This has driven the creation of a database to store and share such validated information. The database, maintained by Lhasa Limited, constitutes a central platform to hold the data donated by the pharmaceutical company members on the nitrite concentrations in common excipients measured with validated analytical procedures. The goal of this data sharing initiative is to provide a common framework to contextualize and estimate the risk posed by presence of nitrites to contribute to the formation of nitrosamines in drug products. The major findings from the database analyses are: (1) average nitrite content and batch to batch variance differ among excipients, (2) for solid dosage forms, the nitrite contribution is dominated by the highest formula % excipients, e.g., the fillers (diluents), which are typically used in larger proportion, and are characterized by low nitrite levels and low variability, leading to an average value of 1 µg/g nitrite in a typical formulation, (3) substantial differences in average nitrite content in batches from different excipient vendors potentially reflecting differences in source materials or processing methods for excipient manufacturing. That final point suggests that future selection of raw materials or processing by excipient manufacturers may help reduce nitrite levels in finished drug product formulations, and thus the overall risk of nitrosamine formation in cases where the product contains vulnerable amines.

Rapid comparative evaluation of SARS-CoV-2 rapid point-of-care antigen tests

PurposeThe objective of this study was to develop a scalable approach for direct comparison of the analytical sensitivities of commercially available SARS-CoV-2 antigen point-of-care tests (AgPOCTs) to rapidly identify poor-performing products.MethodsWe present a methodology for quick assessment of the sensitivity of SARS-CoV-2 AgPOCTs suitable for quality evaluation of many different products. We established reference samples with high, medium, and low SARS-CoV-2 viral loads along with a SARS-CoV-2 negative control sample. Test samples were used to semi-quantitatively assess the analytical sensitivities of 32 different commercial AgPOCTs in a head-to-head comparison.ResultsAmong 32 SARS-CoV-2 AgPOCTs tested, we observe sensitivity differences across a broad range of viral loads (9.8 × 108 to 1.8 × 105 SARS-CoV-2 genome copies per ml). 23 AgPOCTs detected the Ct25 test sample (1.6 × 106 copies/ml), while only five tests detected the Ct28 test sample (1.8 × 105 copies/ml). In the low-range of analytical sensitivity, we found three saliva spit tests only delivering positive results for the Ct21 sample (2.7 × 107 copies/ml). Comparison with published data supports our AgPOCT ranking. Importantly, we identified an AgPOCT widely offered, which did not reliably recognize the sample with the highest viral load (Ct16 test sample with 9.8 × 108 copies/ml) leading to serious doubts about its usefulness in SARS-CoV-2 diagnostics.ConclusionThe results show that the rapid sensitivity assessment procedure presented here provides useful estimations on the analytical sensitivities of 32 AgPOCTs and identified a widely-spread AgPOCT with concerningly low sensitivity.

Temperature control in a horticultural produce supply chain in Thailand and its influence on product quality

The present study was conducted in order to investigate the temperature conditions in the horticultural produce cold chain in Thailand, and fresh-cut baby corn was chosen for a case study. The field investigation together with in-person interviews were performed at 4 growers' premises and one packing house. The information collected provided details on current postharvest conditions at individual stages from the growers’ premises to an export distribution center. Temperature measurements were carried out to explore the time-temperature profiles throughout these stages. The results showed that the total duration of these stages was almost 32 h, and the temperature variations were between 6 and 33 °C. Precooling was delayed for at least 9 h, since baby corn cobs were primarily processed by dehusking and bulk packing. The baby corn was maintained at a temperature below 6.0 °C in a cold room for almost half of the total duration. The effect of the postharvest temperature conditions on the product quality evaluation was assessed by measuring various physiological quality attributes during storage. It was found that the marketability of the baby corn subjected to such postharvest temperature conditions remained possible with a salable period of up to two weeks. The result made it possible to identify the stages during which temperature control was inadequate, and recommendations were proposed to the stakeholder partner in order to modify the postharvest conditions in order to extend the product shelf life.

AI-driven customer relationship management for sustainable enterprise performance

Today, Artificial Intelligence (AI) is poised to take on Industry 4.0. Machines have conquered the human interface in complex board games like chess and Chinese games. AIcan now writes poetry, make predictions about decisions, interact with humans in real-time, analyze trillions of data points, and offer solutions in milliseconds. Customer Relationship Management (CRM) is one sector benefiting the most from AI. AI-driven customer relationship management (AI-CRM) technology is proposed in this article. Scholars developed a research method to evaluate product quality, performance, and commitment to AI-enabled technologies. The proposed model is designed with system-level architecture, process-level architecture, and mathematical models to correlate the input and output. A dataset was utilized to obtain primary data acquired by investigators using a survey technique. Data assessment revealed that customers are aware of AI-enabled technologies, that the technique under investigation is successful, and that customers are loyal to them.

Variation in physico-chemical, organoleptic and microbial qualities of intermediate moisture pineapple (Ananas comosus (L.) Merr.) slices during storage

Pineapple is a commercial tropical fruit ranking sixth in global fruit production. Seasonal market gluts result in huge postharvest losses in all pineapple-growing nations. To reduce postharvest losses of fruits, viable, cost-effective and market-oriented value-added products are needed urgently. The present study was carried out to determine the suitability of two commercial varieties, Kew and Mauritius, for the development of intermediate moisture (IM) fruits and to evaluate product quality during storage. Pineapple slices were steam blanched for 2 min, followed by immersion in osmotic solution containing sucrose and sorbitol (fruit: solution ratio of 1:4), in combination with 0.5% ascorbic acid and 0.25% potassium metabisulfite, for 6 h and were subsequently dehydrated in a tray drier at 50 °C until the moisture content of the product attained equilibrium. The initial moisture contents of IM Kew and Mauritius pineapple slices were 26.53 and 21.65 g/100 g, respectively, while the water activity ranged between 0.671 and 0.666. The L* and b* values showed a decreasing trend, while a* value increased throughout storage, irrespective of variety and storage conditions. The initial TSS of IM pineapple from both varieties was 72 0 Brix, which declined during storage. Though titratable acidity of the product decreased during storage, the decline was insignificant. The total carotenoids of the product from Mauritius was significantly higher (0.809 mg 100 g− 1) compared to the one from Kew (0.215 mg 100 g− 1). Total phenolics contents in IM slices showed an increasing trend during storage, irrespective of varieties and storage conditions. IM pineapple slices from the variety Mauritius had significantly higher flavonoids (40.50 mg) than the product from Kew (30.0 mg 100 g− 1). DPPH radical scavenging activity was highest (3.0 μl ml− 1) in IM slices from the variety Mauritius as compared to those from Kew (5.0 μl ml− 1). IM pineapple from Mauritius recorded higher organoleptic scores throughout storage. Fungi were not detected in any of the samples throughout the storage period while the population of bacteria and yeast was negligible.Graphical abstract

Research and Application of Quantitative Evaluation Method based on Product Quality Characteristics

The current situation of enterprise product quality evaluation is analyzed in this paper, and a set of quantitative evaluation methods based on product quality characteristics in response to the existing problems is proposed. The specific evaluation process is defined, and the steps such as determining product category, identifying quality characteristics, developing evaluation Indicators, carrying out evaluation work, overall quality evaluation and application of evaluation results are introduced in detail. Practice has proved that this method can scientifically analyze and evaluate the individual products and overall quality level of enterprises, can be applied in enterprise quality assessment, quality improvement and macro decision-making, and effectively improve the quality management ability of enterprises.

Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation

Tool condition monitoring (TCM) is an important guarantee for quality evaluation of products and parameter optimization of machining operations. The direct methods of TCM have made significant progress in condition recognition and wear measurement. However, these methods based on a single image that reflects the tool condition inevitably bring downtime to the machine tool. Moreover, a single image cannot reflect the tool wear characteristics integrity because the morphology of tool wear is complex. Regarding the issue above, the aim of this paper was to adaptively online monitoring for milling cutters. Firstly, tool condition image sequence (TCIS) is proposed in successive images to express and enhance tool wear characteristics from multiple angles. Secondly, the time-sequential gradient map between adjacent images is constructed based on histograms of oriented gradient. It is used to capture the initial frame of TCIS. Then, the subsequent images are encoded into the classification model. A logistic regression algorithm is applied to train the classification model to capture the end frame of TCIS. Finally, the tool wear area is located by balancing the rectangular box of wear area and benchmarks of wear measurement and is tracked based on the motion model and the local search. In the experiment of accelerating milling cutter life and three different failure phenomena, the recognition accuracy in the initial and end frame of TCIS is 100%. The average measurement accuracy of flank wear based on the proposed method in two experiments is up to 97.02% and 94.71%, respectively. These operations are automated online and provide complete data support for TCM.

Collection, characterizaton, product quality evaluation, and conservation of genetic resources of yam (Dioscorea spp.) cultivars from Ekiti State, Nigeria

Collection, characterizaton, product quality evaluation, and conservation of genetic resources of yam (Dioscorea spp.) cultivars from Ekiti State, Nigeria

Statistical Inference of Inverted Exponentiated Rayleigh Distribution under Joint Progressively Type-II Censoring.

Inverted exponentiated Rayleigh distribution is a widely used and important continuous lifetime distribution, which plays a key role in lifetime research. The joint progressively type-II censoring scheme is an effective method used in the quality evaluation of products from different assembly lines. In this paper, we study the statistical inference of inverted exponentiated Rayleigh distribution based on joint progressively type-II censored data. The likelihood function and maximum likelihood estimates are obtained firstly by adopting Expectation-Maximization algorithm. Then, we calculate the observed information matrix based on the missing value principle. Bootstrap-p and Bootstrap-t methods are applied to get confidence intervals. Bayesian approaches under square loss function and linex loss function are provided respectively to derive the estimates, during which the importance sampling method is introduced. Finally, the Monte Carlo simulation and real data analysis are performed for further study.

Evaluating the Efficiency of the Classifier Method When Analysing the Sales Data of Agricultural Products

Data classification as a method of input analysis is of the greatest interest and necessity for proper distribution and quality evaluation of agricultural products. The use of classification methods allows predicting whether a selected sample from the data set will fit into a particular class or group, which is necessary for the process of sorting products. This study presents the results of a comparative analysis of high-performance classifiers for assessing the effectiveness of further use in the sorting of agricultural products. The study was carried out utilising the classifiers of k-nearest neighbours, naive Bayesian classifiers, and artificial neural networks for data analysis during apple fruit sorting. It has been established that the greatest accuracy 99% of the results is demonstrated by the classifiers of k-nearest neighbours, but, at the same time, they show the lowest calculation speed (0.47 s). The best performance at any data size (65-100%) is shown by the neural network. A comprehensive review of the features and restrictions of the studied classification algorithms, as well as their applications in various areas of agriculture, has been performed.

Development and quality evaluation of a micronutrient rich traditional Indian product by utilization of locally available food resources

ABSTRACT Malnutrition is a severe matter of concern among various countries due to modern lifestyles, especially the consumption of rapidly processed fast food. In 2018, a WHO and UNICEF study concluded that the SDG objective of eradicating all forms of malnutrition by 2030 was aspirational but unattainable and set targets for malnutrition indicators up to 2030 based on current trends. The present situation demands products rich in micronutrients, meet growth and development requirements, and are readily acceptable. A critical reason for such use of rice flour is its nonallergenic nature. A small proportion of the population is allergic to wheat gluten. Rice is widely used in baking for such wheat-intolerant people, i.e., those suffering from the coeliac disorder. Its nonallergenic property also makes rice one of the first cereals to be used in infant feeding. In this scenario, rice flour is preferred as the foremost choice for required sensory acceptability and a low-cost diet. Food-based approaches are recognised as an essential part of an urgently needed, more comprehensive strategy for improving nutrition by increasing the availability and consumption to combat calcium, iron, phosphorus and other micronutrient deficiencies. The combination of rice flour, beaten rice, banana, jaggery contains a high amount of energy protein with calcium and iron content. Such a combination of foods can enhance the nutritive value of homemade products, which can be beneficial for the malnourished population. The present study was undertaken to develop anarsa by combining banana, jaggery and rice flour using the air frying method. The developed product was evaluated for sensory attributes by a panel of 10 judges on 9 points hedonic rating scale. The calculated nutritive value from Indian Food Composition Tables (2017) showed that the product (100g) was not only had a high level of macronutrients, i.e. energy (3123.6 Kcal), carbohydrate (172.5g), protein (12.24g), fat (4.52g) but also rich in micronutrients like calcium (262.8mg), phosphorus (294.4mg) and iron (6.52mg). The product was packed and stored for 15 days at ambient temperature in an airtight container. At pre-evaluation, all the sensory parameters at the 9-point hedonic rating scale were found to be highly acceptable between 'liked moderately' (rating 8) to 'liked very much. After 15 days of storage, all sensory parameters changed and were lying in-between 'liked slightly' (rating 6) to 'liked moderately' (rating 7). The changes in sensory parameters may be due to the darkening of jaggery and changes in the flavour of the product. Thus, it can be concluded that the development and consumption of novel foods from rice flour in combination with other foods like may banana, jaggery and poppy seeds may provide cost-effective and acceptable options for combating macro and micronutrient deficiencies. Keywords: Rice flour, jaggery, banana, energy dense, mineral profile, sensory evaluation, malnutrition