Classification of Southern Australian Grass- and Grain-Fed Beef

Logan, Bridgette G.; Hopkins, David L.; Schmidtke, Leigh; Morris, Stephen; Fowler, Stephanie M.

doi:10.1007/s12161-021-02010-7

Cited by 4 publications

(6 citation statements)

References 46 publications

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“…The PLS-DA accuracy of Raman in the present study was comparable to recent findings scanning fat (85–96%), 22 although lean was less accurate for discrimination purposes, being 69.5% compared to 96.5%–98.5%. 13,21 Similarly, precision for fat using PLS-DA in both the present study and Logan et al 22 was 100%. For future work with Raman, care would need to be taken when scanning to reduce analytical variability because Raman spectral scans recorded in the OPUS software are averages of the total number of scans and therefore one being incorrect may compromise the entire sample.…”

Section: Discussionsupporting

confidence: 74%

“…For future work with Raman, care would need to be taken when scanning to reduce analytical variability because Raman spectral scans recorded in the OPUS software are averages of the total number of scans and therefore one being incorrect may compromise the entire sample. This was also reflected in the high number of outliers, which were not detected in recent studies by Logan et al 13,21 This finding shows that operator experience and usage may affect the results and training operators on using Raman spectroscopy correctly would be needed to avoid fluorescence reflection from raw samples, which in turn affects the amount of sub-samples. 63…”

Section: Discussionmentioning

confidence: 79%

“…54 Additionally, measurements of vitamin E, fatty acid profile, β-carotene and fat colour could further enhance grass-fed and grain-fed discrimination. 9,21,55,56…”

Section: Discussionmentioning

confidence: 99%

“…16–20 However, no evidence of studies using NIR spectroscopy of intact beef for purposes of discriminating grass-fed and grain-fed feeding regime have been found. Recent studies have shown successful discrimination analysis of feeding regime (grass-fed and grain-fed) on intact beef brisket muscle (accuracy 97.1–98.5%) 13,21 and on subcutaneous fat (accuracy 85–96%) 22 using a handheld Raman spectrometer. This evidence presents an opportunity for future use of both NIR and Raman spectroscopy in meat processing or retail sectors for authentication.…”

Section: Introductionmentioning

confidence: 99%

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Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

Coombs

Liddle

González

2021

Journal of Near Infrared Spectroscopy

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The present study analysed the ability for portable near infrared reflectance (NIR) and Raman spectroscopy sensors to differentiate between grass-fed and grain-fed beef. Scans were made on lean and fat surfaces of 108 beef steak samples labelled as grass-fed ( n = 54) and grain-fed ( n = 54), with partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) used to develop discrimination models which were tested on independent datasets. Furthermore, PLS-DA was used to predict visual marbling score and days on feed (DOF). The NIR spectra accurately discriminated between grass- and grain-fed beef on both fat (91.7%, n = 92) and lean (88.5%, n = 96), as did Raman (fat 95.2%, n = 82; lean 69.6%, n = 68). Fat scanning using NIR spectroscopy moderately predicted DOF (r2val = 0.53), though Raman and NIR spectroscopy lean prediction models for DOF and marbling were less precise (r2val < 0.50). It can be concluded that portable NIR and Raman spectrometers can be used successfully to differentiate grass-fed from grain-fed beef and therefore aid retail and consumer confidence.

show abstract

Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 79%

“…54 Additionally, measurements of vitamin E, fatty acid profile, β-carotene and fat colour could further enhance grass-fed and grain-fed discrimination. 9,21,55,56…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

Coombs

Liddle

González

2021

Journal of Near Infrared Spectroscopy

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“…The slaughtering process may further disturb or even eliminate the traceability (and thus its origin) of the beef product as received by the consumer, despite all these efforts for identification of beef cattle and even tracing individual animals up to their origin of birth. In this sense, the chemical characteristics of beef have been used to discriminate the geographical origin [6,7] and production system [8,9]. In this paper, we describe the performance of five mathematical models to assess if multielement data on the composition of beef can be used to discriminate its origin from any of the producing countries in Latin America (Argentina, Brazil, Paraguay, and Uruguay) and Australia.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Beef Producing Countries by Multi-Element Analysis and Machine Learning

Fernandes¹,

Mazola²,

Sarriés³

et al. 2021

AAIML

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The growing awareness of the environmental impact of beef production is greatly influencing the consumption decision. Beef production is strongly criticized due to the remarkable environmental impact of this activity, associated with problems of deforestation, water consumption, global warming, and climate change. Despite this, livestock food products play an important role in food security, accounting for 33% of global protein consumption. Enhancing the transparency of the beef production chain is essential to increase consumer perception about its origin, safety for consumption, environmental and human aspects.A study was undertaken to assess if beef samples from different producing countries can be distinguished from another on basis of their contents of chemical elements. Beef samples from some of the top world exporters, Brazil (1 st ), Australia (2 nd ), Argentina (5 th ), Uruguay (8 th ), and Paraguay (9 th ), were analyzed by neutron activation analysis for multi-element determination. Five machine learning algorithms, Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF), and Sequential Minimal Optimization (SMO), were used to analyze the measurement results and classify the beef producing countries. MLP model provided the best classification performance, with an accuracy of 100%, 98%, 98%, 96%, and 82% respectively for Paraguay, Uruguay, Australia, Argentina, and Brazil. Reducing the number of classes (each country against the remaining countries), the accuracy achieved for the Brazilian beef samples was improved to 94% without changing the performance for other countries. Multi-element compositional data and machine learning algorithms allowed for discriminating beef producing countries, providing an outlook of becoming a valuable tool for geographical origin traceability and transparency.

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Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems

Fowler,

Kerr,

Hopkins

et al. 2024

Meat Science

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Classification of Southern Australian Grass- and Grain-Fed Beef

Cited by 4 publications

References 46 publications

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef

Discriminating Beef Producing Countries by Multi-Element Analysis and Machine Learning

Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems

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