Theoretical Basis and Application for Measuring Pork Loin Drip Loss Using Microwave Spectroscopy

During cutting and processing of meat, the loss of water is critical in determining both product quality and value. From the point of slaughter until packaging, water is lost due to the hanging, movement, handling, and cutting of the carcass, with every 1% of lost water having the potential to cost a large meat processing plant somewhere in the region of  50,000 per day. Currently the options for monitoring the loss of water from meat, or determining its drip loss, are limited to destructive tests which take 24–72 h to complete. This paper presents results from work which has led to the development of a novel microwave cavity sensor capable of providing an indication of drip loss within 6 min, while demonstrating good correlation with the well-known EZ-Driploss method (R2 = 0.896)

Detectability of the degree of freeze damage in meat analytic-tool depends on selection

Novel freezing solutions are constantly being developed to reduce quality loss in meat production chains. However, there is limited focus on identifying the sensitive analytical tools needed to directly validate product changes that result from potential improvements in freezing technology. To benchmark analytical tools relevant to meat research and production, we froze pork samples using traditional (−25 °C, −35 °C) and cryogenic freezing (−196 °C). Three classes of analyses were tested for their capacity to separate different freeze treatments: thaw loss testing, bioelectrical spectroscopy (nuclear magnetic resonance, microwave, bioimpedance) and low-temperature microscopy (cryo-SEM). A general effect of freeze treatment was detected with all bioelectrical methods. Yet, only cryo-SEM resolved quality differences between all freeze treatments, not only between cryogenic and traditional freezing. The detection sensitivity with cryo-SEM may be explained by testing meat directly in the frozen state without prior defrosting. We discuss advantages, shortcomings and cost factors in using analytical tools for quality monitoring in the meat sector

Prediction of Water Activity in Cured Meat using Microwave Spectroscopy

This work addresses the use of microwave techniques to determine quality parameters in cured meat. The first approach is online monitoring of weight loss in the meat curing process, which is a significant measurement for the meat industry because the weight loss is used as a method of tracking the curing process. Currently, weight loss is measured by using ordinary weighing scales, which is a time-consuming and impractical technique. Thus, a novel method is required to simplify the process by implementing an online monitoring technique. In this work, a set of microwave sensors were modelled using High Frequency Structure Simulation Software and then constructed and tested. Weight loss of the sample and change in the S11-parameter illustrated a strong linear relationship (R2 > 0.98). The prediction model then was developed using the Partial Least Squares method, which exhibited a good capability of microwave sensors to predict weight loss, with R2p (prediction) = 0.99 and root mean square error of prediction (RMSEP) = 0.41. The second approach is to determine water activity (aw) in cured meat, which is the parameter that describes available water for microorganisms and influences different chemical reactions in the product. For the cured meat industry, aw is the only moisture related measurement that is an accepted Hazard Analysis and Critical Control Point plan. This is important for safety reasons, but also for energy optimisation since curing requires controlled continuous temperature and humidity. Currently, aw is being measured by the meat industry using commercially available instruments, which have limitations, namely being destructive, expensive and time-consuming. Few attempts to develop non-destructive methods to predict aw have used X-ray systems (namely Computed Tomography), Near Infrared (NIR) and Hyperspectral Imaging (HSI). Although the techniques provided promising results, they are expensive, impractical and not commercially available for the meat industry. The results from the microwave sensors demonstrated a linear relationship (R2 = 0.75, R2 = 0.86 and R2 = 0.91) between the S11 and aw at 2.4 GHz, 5 GHz and 7 GHz, respectively. The prediction model exhibited a good capability of the sensors to predict aw (R2p = 0.91 and RMSEP = 0.0173

Estimasi Kadar Air Daging Sapi Berdasarkan Luas Area Jejak Air Daging Fresh Meat Water Estimate Based On Meat Water Stain Area

Daging sapi mengandung protein tinggi, zat besi, seng, selenium, riboflavin, vitamin B6, vitamin B12, niasin, fosfor, dan asam amino esensial yang dibutuhkan manusia. Penelitian ini bertujuan untuk  mengestimasi kadar air daging sapi berdasarkan jejak air daging pada kertas. Penelitian ini dimaksudkan untuk mengantisipasi pemalsuan daging (gelonggongan). Sebanyak 10 sampel daging bagian silrloin sberat 250gram diambil dari individu berbeda yang telah dipastikan dalam kondisi sehat dan normal dan dipotong di RPH. Daging dibawa dengan kotak pendingin dari ke laboratorium. Sebanyak 100gram daging diuji proksimat dan lima (5) gram untuk uji tekan dengan berat beban sebagai perlakuan yaitu 0,5 kg dan 2 kg selama 5 menit di atas kertas di kertas saring Whatmann no 1. Luas area jejak air daging pada kertas diukur menggunakan planimeter (Planix-5, Tamaya®, Jepang). Data luas area jejak air daging hasil uji tekan dan kadar  air hasil uji proksimat dianalisis regresi linier sederhana. Hasil uji proksimat menunjukkan kandungan nutrisi daging yaitu kadar air -rata 74,16±1,11%, kadar abu 0,98 ± 0,09%, kadar protein 19,38±1,47%, dan kadar lemak 3,98±2,86%. Rerata luas jejak air daging menggunakan beban 0,5 kg adalah 27,03±14,3 cm2, dan persamaan linier yang dihasilkan kadar air daging (Y)= 72,925+0,046 (P>0,05), sedangakan dengan beban 2 kg menghasilkan luas rata-rata 43,37±15,67 cm2, dan persamaan linier Y = 71,573 + 0,059X (P<0,05). Berdasarkan persamaan linier dengan beban 2 kg maka kisaran luas jejak air untuk daging normal diperoleh dari 1-143 cm2 dengan perkiraan kadar air 71,63-80,01%. 

Rapid Non-Destructive Prediction of Water Activity in Dry-Cured Meat

Water activity (aw) describes the amount of free water available in a matrix for growth of microbiological pathogens and spoilage flora. It is used to predict the safety of food products, and has particular importance for dry-cured meat manufacturers. Results from tests on dry-cured pork (n = 83) demonstrate a high degree of correlation (R2 = 0.909) with current industry standard equipment. System accuracy at the 95% confidence interval (0.0125) is comparable with existing equipment available to industry. However, the added advantage of the microwave sensor to enable rapid and non-destructive measurement means that it could be used for day-to-day monitoring and optimization of products within the dry-cured meat value chain. This would reduce per-product operating costs and waste, in addition to facilitating recipe development (e.g., reduced salt)

Role of freezing-induced myofibrillar protein denaturation in the generation of thaw loss : A review

Formation of thaw loss cannot generally be avoided when meat is frozen and then thawed. Explanations have mainly focused on the damage to muscle fibers resulting from ice crystallization and the freezing-induced denaturation of myofibrillar proteins, the latter of which has, however, not received much research focus. This review discusses the relationship between myofibrillar protein denaturation and water-holding capacity of meat in freezing-thawing with the aim to improve the understanding the relative importance of protein denaturation in the formation of thaw loss. The contribution of decreased pH and high ionic strength in the unfrozen water in freezing is emphasized and we hypothesize that these two factors are causing protein denaturation and conformational changes within muscle fibers, and consequently loss of water-holding capacity. Slow freezing produces more thaw loss than fast freezing, and this is discussed here in relation to the impacts on myofibrillar protein denaturation induced by the freezing rate.Peer reviewe

Real-Time Water Quality Monitoring with Chemical Sensors

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described

Smart Knife: Integrated Intelligence for Robotic Meat Cutting

Automation is a key technology for a sustainable and secure meat sector in the future, both in terms of productivity and work environment. New robotic technologies, such as the so-called “meat factory cell,” (MFC) aim to contribute to this goal, but they require new “smart” tools that provide sensor feedback, which enable robots to perform complex tasks. This article presents one such tool: the smart knife, which gives real-time feedback on its contact status with meat, as well as cutting depth. The tool and the system are described, and its operation evidenced via electromagnetic (EM) simulation using the Ansys High-Frequency Structure Simulator. Furthermore, the performance of the knife is validated using pork loin meat: in the worst case, knife is shown to have an error of 1.78% for contact detection, and a mean error of 7.66 mm (±1.45 mm) for depth detection. This article also presents brief discussion regarding eventual use of the knife as part of the MFC control system, in addition to future work to be performed.publishedVersio

Rapid determination of selected meat components using near infrared reflectance spectroscopy

A new and rapid multicomponent analysis of foods has been developed by using near infrared reflectance spectrometry. The spectroscopic technique of Near Infrared Reflectance Analysis (NIRA) has required the use of transformation of the correlation matrix, a mathematical technique of multiple regression analysis in order to generate the empirical calibration required for the success of the technique. Preliminary calibration curves for beef, pork, and beef and pork mixture have been developed using an InfraAlyzer 400(\u27TM) (Technicon Industrial Systems, Tarrytown, N.Y.) and a Hewlett-Packard 9815\u27A\u27 calculator (Hewlett-Packard Company, Santa Clara, CA). The accuracy of these preliminary prediction regression equations was not satisfactory for the use of in-process control in meat processing. In subsequent experiments, a Vita Mix blender (Vita Mix Corp., Cleveland, Ohio) was introduced to improve the homogeneity of the samples and a Hewlett-Packard 85 personal computer (Hewlett-Packard Company, Santa Clara, CA) replaced the less powerful Hewlett-Packard 9815\u27A\u27. Nineteen beef samples with varying amounts of collagen were prepared and measured via the InfraAlyzer 400(\u27TM) and hydroxyproline. The multiple correlation coefficients (R(\u272)) of the prediction equations for hydroxyproline, protein, fat, and moisture were 0.9389, 0.7745, 0.9329, and 0.9425, respectively. Correlations of 0.2754, 0.5279, 0.8020, and 0.8745 were found between near infrared values and chemical analysis values for hydroxyproline, protein, fat, and moisture. The R(\u272) values for emulsified pork were 0.9469 for protein, 0.9662 for fat, and 0.9524 for moisture. Correlations between near infrared values and chemical analysis values were 0.8857 for protein, 0.9725 for fat, and 0.9628 for moisture. With the improved accuracy of prediction equations through the improved sampling procedure and the most capable computer programming, the near infrared reflectance analysis can be an alternative technique for rapid determination of selected meat components

A Proof of Concept Study on Utilising a Non-invasive Microwave Analysis Technique to Characterise Silver Based Materials in Aqueous Solution

This paper reports on the feasibility of using a novel and robust microwave sensing technique to analyse and detect silver materials in an aqueous solution. Two products are tested, namely: silver chloride and silver oxide. The study mainly focused on indicating the difference between them and also any change in the size/size distribution of the sample. A microwave sensor designed previously is utilised to identify the potential of the technique to carry out the analysis. The results are presented as microwave spectrums that are the material response to microwaves. The results have shown that the technique has reasonably indicated the change in material type as well as size distribution. The results also show that these curves are distinguishable and can be related to the material and the change in its size. It is concluded that there is a potential of extending this technique to determine various other properties of silver products. The study suggests a design and development of a bespoke unit as a dedicated analysis tool and to address any anomalies arising from the current feasibility. This will have a huge industrial benefit in terms of cost reduction and time associated with the industrial analysis of silver material