Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef
Cassius E. O. Coombs
A B , Mario Fajardo A and Luciano A. González A
+ Author Affiliations
- Author Affiliations
A Sydney Institute of Agriculture and School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia.
B Corresponding author. Email: cassius.coombs@gmail.com
Animal Production Science 61(16) 1723-1733 https://doi.org/10.1071/AN21069
Submitted: 10 February 2021 Accepted: 5 August 2021 Published: 21 September 2021
Abstract
Context: Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models.
Aims: To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors.
Methods: Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors.
Key results: The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 >0.75; RPD >1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry.
Conclusion: The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications.
Implications: Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.
Keywords: prediction models, fat, protein, water, near-infrared spectroscopy, pH.
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