Pub Date : 2024-03-21DOI: 10.21014/actaimeko.v13i1.1625
Lucrezia Forte, P. De Palo, Alessandra Aloia, A. Maggiolino
Predicting and, consequently, controlling oxidative phenomena is a big challenge for meat industry. Since color modifications and volatile compounds synthesis are processes closely linked to the oxidative reactions occurring in meat, oxidation could be measured through colorimetric parameters or some volatile compounds. A Spearman’s correlation analysis of oxidative parameters and enzymatic activity with colorimetric parameters and volatile profile was performed on horse steaks subjected to short (14 days) and long aging time (56 days). In long aging, TBARS were negatively correlated with redness (rs = -0.76, p < 0.001). Some volatile compound families, such as thiols, carboxylic acids, lactones, hydrocarbons and sulphur compounds, were well correlated with enzymatic activity (p < 0.001), contrary to aldehydes, alcohols and ketones. Higher correlation indexes were recorded in short aging, if compared to those found in long aging.
{"title":"Could colour and volatile compounds be measurements of oxidation in horse meat?","authors":"Lucrezia Forte, P. De Palo, Alessandra Aloia, A. Maggiolino","doi":"10.21014/actaimeko.v13i1.1625","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1625","url":null,"abstract":"Predicting and, consequently, controlling oxidative phenomena is a big challenge for meat industry. Since color modifications and volatile compounds synthesis are processes closely linked to the oxidative reactions occurring in meat, oxidation could be measured through colorimetric parameters or some volatile compounds. A Spearman’s correlation analysis of oxidative parameters and enzymatic activity with colorimetric parameters and volatile profile was performed on horse steaks subjected to short (14 days) and long aging time (56 days). In long aging, TBARS were negatively correlated with redness (rs = -0.76, p < 0.001). Some volatile compound families, such as thiols, carboxylic acids, lactones, hydrocarbons and sulphur compounds, were well correlated with enzymatic activity (p < 0.001), contrary to aldehydes, alcohols and ketones. Higher correlation indexes were recorded in short aging, if compared to those found in long aging.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.21014/actaimeko.v13i1.1750
Cihan Kuzu, Kürşat Kazmanlı
TÜBİTAK UME Hardness Laboratory has been working on instrumentation in the field of hardness metrology since 2005 and three generations of hardness standardizing machines were developed since then to be used as reference (calibration/standardizing) machines in Türkiye. In former designs conventional hardness methods such as Rockwell, Brinell and Vickers scales were the main scope of the projects. In the final Project that was supported and funded by TÜBİTAK UME to develop three hardness standard machines to be used as national standards for the conventional hardness scales mentioned, the Instrumented Indentation Test (IIT) was also aimed at and some parameters like Martens hardness, creep, indentation hardness, (elastic and plastic) indentation work, etc. were also implemented onto the machines developed. It was a good occasion that the measurands in Rockwell hardness and IIT were the same, force and indentation depth besides time and this made it easier to realize the IIT on the same machine with a more suitable design to achieve the highest accuracy in terms of the measurands mentioned. In this paper the new design of the Rockwell-Brinell-Vickers hardness standard machine developed also for Martens hardness in macro range (3 kgf – 150 kgf) and preliminary Martens hardness measurements are explained.
{"title":"Realization of Martens hardness method in macro range with high accuracy force and indentation depth","authors":"Cihan Kuzu, Kürşat Kazmanlı","doi":"10.21014/actaimeko.v13i1.1750","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1750","url":null,"abstract":"TÜBİTAK UME Hardness Laboratory has been working on instrumentation in the field of hardness metrology since 2005 and three generations of hardness standardizing machines were developed since then to be used as reference (calibration/standardizing) machines in Türkiye. In former designs conventional hardness methods such as Rockwell, Brinell and Vickers scales were the main scope of the projects. In the final Project that was supported and funded by TÜBİTAK UME to develop three hardness standard machines to be used as national standards for the conventional hardness scales mentioned, the Instrumented Indentation Test (IIT) was also aimed at and some parameters like Martens hardness, creep, indentation hardness, (elastic and plastic) indentation work, etc. were also implemented onto the machines developed. It was a good occasion that the measurands in Rockwell hardness and IIT were the same, force and indentation depth besides time and this made it easier to realize the IIT on the same machine with a more suitable design to achieve the highest accuracy in terms of the measurands mentioned. In this paper the new design of the Rockwell-Brinell-Vickers hardness standard machine developed also for Martens hardness in macro range (3 kgf – 150 kgf) and preliminary Martens hardness measurements are explained.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140387970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.21014/actaimeko.v13i1.1742
Yousif Alaraji, Sina Alp
This study presents a novel fault detection method in car gear steering systems, employing MSC Adams and MATLAB simulations to analyze angular acceleration from the outer tie rod. The approach closely mimics real accelerometer data to differentiate between normal and faulty conditions, including wear and obstacle navigation. Emphasis is on noise robustness, utilizing advanced noise injection and denoising techniques. The efficacy of wavelet scattering, discrete wavelet transform (DWT) methods, and classifiers like Support Vector Machines (SVM) and Neural Networks (NN) is extensively evaluated. Among fifteen fault detection methods, the combination of wavelet scattering with Long Short-Term Memory (LSTM) Neural Networks, optimized with Adam tuning, is notably stable across four scenarios. The research highlights the importance of precise feature selection, employing techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE). This research significantly advances the reliability of autonomous driving systems and provides essential insights into fault detection in gear steering systems.
{"title":"investigation into vibration analysis for detecting faults in vehicle steering outer tie-rod","authors":"Yousif Alaraji, Sina Alp","doi":"10.21014/actaimeko.v13i1.1742","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1742","url":null,"abstract":"This study presents a novel fault detection method in car gear steering systems, employing MSC Adams and MATLAB simulations to analyze angular acceleration from the outer tie rod. The approach closely mimics real accelerometer data to differentiate between normal and faulty conditions, including wear and obstacle navigation. Emphasis is on noise robustness, utilizing advanced noise injection and denoising techniques. The efficacy of wavelet scattering, discrete wavelet transform (DWT) methods, and classifiers like Support Vector Machines (SVM) and Neural Networks (NN) is extensively evaluated. Among fifteen fault detection methods, the combination of wavelet scattering with Long Short-Term Memory (LSTM) Neural Networks, optimized with Adam tuning, is notably stable across four scenarios. The research highlights the importance of precise feature selection, employing techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE). This research significantly advances the reliability of autonomous driving systems and provides essential insights into fault detection in gear steering systems.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140390015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.
{"title":"Short review of current limits and challenges of application of machine learning algorithms in the dairy sector","authors":"Lucia Trapanese, Miel Hostens, Angela Salzano, Nicola Pasquino","doi":"10.21014/actaimeko.v13i1.1725","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1725","url":null,"abstract":"In the last years, the livestock sector is moving towards a more sustainable animal-based industry, mitigating the environmental impact of livestock while meeting the demand for high-quality food. To achieve these goals, farms are using a more technological approach, adopting algorithms to manipulate the vast amount of data from sensors and routine operations. The results will be useful for making more objective decisions. In this context, machine learning - a branch of Artificial Intelligence applied to the study of prediction, inference, and clustering algorithms - can be successfully employed. Nowadays, machine learning algorithms are successfully used to solve many issues in the livestock sector, such as early disease detection, and they are expected to be employed in the future for welfare monitoring. This brief review gives an overview of the current state of the art of the most popular applications for dairy science and the most widely used and best-performing algorithms, highlighting the challenges and obstacles for broad acceptance of these techniques in the dairy sector.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140391330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.21014/actaimeko.v13i1.1619
Alessandra Aloia, A. Maggiolino, Lucrezia Forte, P. De Palo
The most widely used predictor to assess the incidence of thermal stress in livestock is THI, the temperature humidity index. However, it is an indicator that disregards the individual animal and the specific farm conditions. This review aims to list and summarize other thermal stress predictor factors, by using non-invasive and cost-effective strategies, in particular with the aid of Precision Livestock Farming technologies. When it comes to dairy animals the metabolic load is already increased by milk production, so the effect of heat stress can exacerbate the overall welfare of the cow. Therefore, the animals enact copying mechanisms that may result in physiological, behavioral and productive alterations. Those animal-based parameters can be used as early predictors of heat stress, allowing the farmer to collect real time data and address the condition operating management strategies in order to prevent further detrimental effect on the performance and consequent economic losses.
{"title":"Heat stress measuring methods in dairy cows","authors":"Alessandra Aloia, A. Maggiolino, Lucrezia Forte, P. De Palo","doi":"10.21014/actaimeko.v13i1.1619","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1619","url":null,"abstract":"The most widely used predictor to assess the incidence of thermal stress in livestock is THI, the temperature humidity index. However, it is an indicator that disregards the individual animal and the specific farm conditions. This review aims to list and summarize other thermal stress predictor factors, by using non-invasive and cost-effective strategies, in particular with the aid of Precision Livestock Farming technologies. When it comes to dairy animals the metabolic load is already increased by milk production, so the effect of heat stress can exacerbate the overall welfare of the cow. Therefore, the animals enact copying mechanisms that may result in physiological, behavioral and productive alterations. Those animal-based parameters can be used as early predictors of heat stress, allowing the farmer to collect real time data and address the condition operating management strategies in order to prevent further detrimental effect on the performance and consequent economic losses.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140392329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.21014/actaimeko.v13i1.1704
Behnam Karim Sarmadi, Ahmad Salehi Dobakhshari
This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.
{"title":"A parallel approach for ultra-fast state estimation in large power system using graph partitioning theory","authors":"Behnam Karim Sarmadi, Ahmad Salehi Dobakhshari","doi":"10.21014/actaimeko.v13i1.1704","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1704","url":null,"abstract":"This paper introduces a novel approach for multi-area state estimation in large transmission networks through the application of graph partitioning theory. By harnessing the eigenvalues and eigenvectors of the Laplacian matrix, a large-scale transmission network is partitioned into manageable sections. Within these partitions, state estimation processes run in parallel, markedly improving efficiency compared to conventional methods. Linear state estimation is employed within each area, expediting computations and making it adaptable to large-scale networks, which traditionally pose computational challenges. The method's efficacy is demonstrated through comprehensive validation, commencing with small networks and extending to real-world applications on the IEEE 118-bus test system and the 9241-bus European high-voltage transmission network. In comparison to the integrated network method, our approach has achieved state estimation answers with reduced computation time. The partitioning of the integrated network into multi areas has effectively mitigated computational loads, showcasing its potential for enhancing operational efficiency and reliability in complex power transmission systems. This approach not only offers a robust solution for state estimation but also represents a significant stride toward advancing the field of state estimation, promising to bolster the stability and performance of modern power grids.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140392121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.21014/actaimeko.v13i1.1682
Simona Maria Carmela Porto, Marco Bonfanti, Dominga Mancuso, Giovanni Cascone
Monitoring daily cow behavioural activities of cows in livestock farms is strategic for improving the herd management. For this reason, IoT techniques and smart sensors are become the most common technological support in barns. The aim of this paper is to validate the use of predefined accelerometer thresholds in timely detecting of cow behavioural activities through the statistical analysis of the data acquired from accelerometers housed in collars. Applying ANOVA and TUKEY tests to the median of the accelerations measured with 4 Hz sampling, the behavioural activities analysed in this study, i.e., feeding, lying, rumination, were found to be discriminable along one or more axes. This could allow the implementation of threshold-based algorithms in the firmware of devices housed in the cow collars.
{"title":"Assessing accelerometer thresholds for cow behaviour detection in free stall barns: a statistical analysissis","authors":"Simona Maria Carmela Porto, Marco Bonfanti, Dominga Mancuso, Giovanni Cascone","doi":"10.21014/actaimeko.v13i1.1682","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1682","url":null,"abstract":"Monitoring daily cow behavioural activities of cows in livestock farms is strategic for improving the herd management. For this reason, IoT techniques and smart sensors are become the most common technological support in barns.\u0000The aim of this paper is to validate the use of predefined accelerometer thresholds in timely detecting of cow behavioural activities through the statistical analysis of the data acquired from accelerometers housed in collars. Applying ANOVA and TUKEY tests to the median of the accelerations measured with 4 Hz sampling, the behavioural activities analysed in this study, i.e., feeding, lying, rumination, were found to be discriminable along one or more axes. This could allow the implementation of threshold-based algorithms in the firmware of devices housed in the cow collars.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140392885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-12DOI: 10.21014/actaimeko.v13i1.1679
Mukhriddin Arabboev, Shohruh Begmatov, Mokhirjon Rikhsivoev, K. Nosirov, Saidakmal Saydiakbarov
Image super-resolution is a process that aims to enhance the quality and resolution of images using various techniques and algorithms. The process aims to reconstruct a high-resolution image from a given low-resolution input. To determine the effectiveness of these algorithms, it's crucial to evaluate those using specific metrics. In this paper, we take a closer look at the most commonly used image super-resolution metrics, including classical approaches like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). We also discuss advanced metrics like Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), Inception Score (IS), and Multi-Scale Structural Similarity Index (MS-SSIM). Furthermore, we provide an overview of classical and AI-based super-resolution techniques and methods. Finally, we discuss potential challenges and future research directions in the field and present our experimental results by applying image super-resolution metrics. In the result and discussion section, we have practiced some given metrics and proposed our image super-resolution results.
{"title":"comprehensive review of image super-resolution metrics: classical and AI-based approaches","authors":"Mukhriddin Arabboev, Shohruh Begmatov, Mokhirjon Rikhsivoev, K. Nosirov, Saidakmal Saydiakbarov","doi":"10.21014/actaimeko.v13i1.1679","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1679","url":null,"abstract":"Image super-resolution is a process that aims to enhance the quality and resolution of images using various techniques and algorithms. The process aims to reconstruct a high-resolution image from a given low-resolution input. To determine the effectiveness of these algorithms, it's crucial to evaluate those using specific metrics. In this paper, we take a closer look at the most commonly used image super-resolution metrics, including classical approaches like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM). We also discuss advanced metrics like Learned Perceptual Image Patch Similarity (LPIPS), Fréchet Inception Distance (FID), Inception Score (IS), and Multi-Scale Structural Similarity Index (MS-SSIM). Furthermore, we provide an overview of classical and AI-based super-resolution techniques and methods. Finally, we discuss potential challenges and future research directions in the field and present our experimental results by applying image super-resolution metrics. In the result and discussion section, we have practiced some given metrics and proposed our image super-resolution results.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140395100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.21014/actaimeko.v13i1.1322
Catarina Ferreira Simões, Álvaro Silva Ribeiro, Maria Céu Almeida, D. Covas, Luís Dias, Gustavo Coelho
This study aims to develop a laboratory method for the traceability of a rainfall weighing gauge, including an evaluation of the measurement uncertainty. The adopted procedure is similar to the one used for the non-automatic weighing instruments. A static approach is followed to achieve the calibration deviation of the precipitation scale. The method used to evaluate the measurement uncertainty is based on a nonlinear mathematical model. The Monte Carlo method is used to calculate uncertainties and validate estimates following the conventional Guide to the Expression of Uncertainty in Measurement (GUM) approach. Measurement uncertainty contributions of input quantities to the mathematical model used to calculate rainfall also require specific calibration procedures. Results show the accuracy level achievable with rainfall weighing gauges commonly used as a reference for meteorological monitoring networks and data modelling.
{"title":"Validation of a laboratory method for the traceability of a rainfall weighing gauge","authors":"Catarina Ferreira Simões, Álvaro Silva Ribeiro, Maria Céu Almeida, D. Covas, Luís Dias, Gustavo Coelho","doi":"10.21014/actaimeko.v13i1.1322","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1322","url":null,"abstract":"This study aims to develop a laboratory method for the traceability of a rainfall weighing gauge, including an evaluation of the measurement uncertainty. The adopted procedure is similar to the one used for the non-automatic weighing instruments. A static approach is followed to achieve the calibration deviation of the precipitation scale. The method used to evaluate the measurement uncertainty is based on a nonlinear mathematical model. The Monte Carlo method is used to calculate uncertainties and validate estimates following the conventional Guide to the Expression of Uncertainty in Measurement (GUM) approach. Measurement uncertainty contributions of input quantities to the mathematical model used to calculate rainfall also require specific calibration procedures. Results show the accuracy level achievable with rainfall weighing gauges commonly used as a reference for meteorological monitoring networks and data modelling.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-04DOI: 10.21014/actaimeko.v13i1.1721
Carlo Boselli, A. Guerra, A. Costa, M. De Marchi
The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.
{"title":"Prediction of sheep bulk milk coagulation properties from mid-infrared spectral data","authors":"Carlo Boselli, A. Guerra, A. Costa, M. De Marchi","doi":"10.21014/actaimeko.v13i1.1721","DOIUrl":"https://doi.org/10.21014/actaimeko.v13i1.1721","url":null,"abstract":"The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.","PeriodicalId":37987,"journal":{"name":"Acta IMEKO","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140397965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：