Meat inspection in the Australian red-meat industries: past, present and future

Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models

Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.

Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400-900 nm) and short-wave infrared (900-1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.

Semi-quantitative food safety risk profile of the Australian red meat industry

In 2017-18, the Australian red meat (beef, sheep and goat species) industry generated more than $AUD 13 billion in export trade alone and is therefore of substantial importance to the Australian GDP. With both relatively high amounts of domestic red meat consumption and dependence on international markets, food safety risk is constantly reassessed so as to maintain a resilient industry sector. The current study aimed to conduct a food safety risk rating for the Australian red meat industry. In 2002, a food safety risk profile was developed for the Australian red meat industry. It included raw and processed meat products of cattle, sheep and goats and considered microbiological, chemical and physical hazards. The current risk rating was undertaken during 2017 and 2018. The first step was to conduct a hazard characterization, which involved a review of literature and data on foodborne outbreaks, pathogen surveillance and product recalls, and an expert elicitation process with 15 Australian food safety experts. This process identified the Hazard:Product:Process combinations to be considered and the likelihood of contamination at the point of consumption. These likelihood ratings were then combined with hazard severity ratings to qualitatively estimate the relative risk posed by each combination. Combinations with a moderate-to-high risk were included in the semi-quantitative risk rating using Risk Ranger v2, a tool that allows an estimation of the public health risk of hazard: product combinations and a ranking of this risk. The Risk Ranger tool provides a risk ranking (RR), ranging from 0 (no risk) to 100 (every member of the population eats a meal that contains a lethal dose of the hazard every day). STEC E. coli O157 (RR 35-39) and Salmonella spp. (RR 33-37) in undercooked hamburgers and Listeria monocytogenes in ready-to-eat products (RR 35-38) were combinations which had the highest (moderate) risk for the general and susceptible populations. In addition, Toxoplasma gondii in undercooked lamb was identified as posing a high risk among pregnant women (RR 49). The study provides an updated food safety risk profile for the Australian red meat industry which, considering the available information, suggests red meat products do not pose a high food safety risk. The methodology developed in this study provides an easy to implement approach to profile and prioritise food safety risk and relies on data that can generated in most situations.

Comparison of postmortem inspection procedures for detecting caseous lymphadenitis of Australian sheep and goats

Alternative postmortem inspection procedures for the detection of gross abnormalities due to Caseous Lymphadenitis (CLA) of sheep and goats were compared quantitatively against the current Australian Standard (AS4696). Studies on sheep and goats in Australia during 2016 addressed data gaps regarding current prevalence, combinations of multiple lesions within affected carcases and sensitivity of inspection procedures enabling a comparison of alternative with current procedures. Using these contemporary inspection data from 54 915 sheep and 48 577 goats a desktop study estimated the effect of implementing alternative procedures of reduced palpation from eleven carcase sites to the four sites most commonly affected. Under current procedures it was estimated that 86 sheep and 34 goat carcases with CLA lesions are missed per 10,000 carcases. Under alternative procedures it is estimated that an additional 48.4 sheep and 10.5 goat carcases with CLA lesions would be missed per 10 000 carcases. Of these, 38.2 sheep and 5.6 goat per 10 000 carcases would contain CLA only in routinely discarded, non-edible tissue sites. Hence, only an additional 10.2 sheep and 4.9 goat carcases per 10 000 inspected, with CLA in edible tissue sites are estimated to be missed. These alternative procedures have now been officially implemented in the Australian domestic standard.

Beef quality attributes: A systematic review of consumer perspectives

Informed by quality theory, this systematic literature review seeks to determine the relative importance of beef quality attributes from a consumer perspective, considering search, experience and credence quality attributes. While little change is anticipated in consumer ranking of search and experience attributes in the future, movement is expected in terms of ranking within the credence category and also in terms of the ranking of credence attributes overall. This highlights an opportunity for quality assurance schemes (QAS) to become more consumer focused through including a wider range of credence attributes. To capitalise on this opportunity, the meat industry should actively anticipate new relevant credence attributes and researchers need to develop new or better methods to measure them. This review attempts to identify the most relevant quality attributes in beef that may be considered in future iterations of QAS, to increase consumer satisfaction and, potentially, to increase returns to industry.

Bovine cysticercosis--development of a real-time PCR to enhance classification of suspect cysts identified at meat inspection

Laboratory confirmation methods are important in bovine cysticerosis diagnosis as other pathologies can result in morphologically similar lesions resulting in false identifications. We developed a probe-based real-time PCR assay to identify Taenia saginata in suspect cysts encountered at meat inspection and compared its use with the traditional method of identification, histology, as well as a published nested PCR. The assay simultaneously detects T. saginata DNA and a bovine internal control using the cytochrome c oxidase subunit 1 gene of each species and shows specificity against parasites causing lesions morphologically similar to those of T. saginata. The assay was sufficiently sensitive to detect 1 fg (Ct 35.09 ± 0.95) of target DNA using serially-diluted plasmid DNA in reactions spiked with bovine DNA as well as in all viable and caseated positive control cysts. A loss in PCR sensitivity was observed with increasing cyst degeneration as seen in other molecular methods. In comparison to histology, the assay offered greater sensitivity and accuracy with 10/19 (53%) T. saginata positives detected by real-time PCR and none by histology. When the results were compared with the reference PCR, the assay was less sensitive but offered advantages of faster turnaround times and reduced contamination risk. Estimates of the assay's repeatability and reproducibility showed the assay is highly reliable with reliability coefficients greater than 0.94.