Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat

Robert, Chima; Jessep, William; Sutton, Joshua J.; Hicks, Talia; Loeffen, M.P.F.; Farouk, M.M.; Ward, J.F.; Bain, W. E.; Craigie, C.R.; Fraser‐Miller, Sara J.; Gordon, Keith C.

doi:10.1016/j.foodchem.2021.130154

Cited by 29 publications

(10 citation statements)

References 31 publications

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“…The preprocessed Raman and IR spectral data were concatenated into a single matrix prior to undertaking the chemometric analysis. The details of this data fusion method have been described in greater detail elsewhere [ 14 , 27 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

et al. 2022

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This study uses Raman and IR spectroscopic methods for the detection of adulterants in marine oils. These techniques are used individually and as low-level fused spectroscopic data sets. We used cod liver oil (CLO) and salmon oil (SO) as the valuable marine oils mixed with common adulterants, such as palm oil (PO), omega-3 concentrates in ethyl ester form (O3C), and generic fish oil (FO). We showed that support vector machines (SVM) can classify the adulterant present in both CLO and SO samples. Furthermore, partial least squares regression (PLSR) may be used to quantify the adulterants present. For example, PO and O3C adulterated samples could be detected with a RMSEP value less than 4%. However, the FO adulterant was more difficult to quantify because of its compositional similarity to CLO and SO. In general, data fusion improved the RMSEP for PO and O3C detection. This shows that Raman and IR spectroscopy can be used in concert to provide a useful analytical test for common adulterants in CLO and SO.

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Section: Methodsmentioning

confidence: 99%

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

et al. 2022

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“…The quality of meat and aquatic products is susceptible to nutrient loss and spoilage; therefore, it is necessary to develop high-performance nondestructive detection techniques for meat and aquatic products (Qu et al, 2022). Robert et al (2021) compared data fusion strategies based on Raman and NIR for the prediction of the pH and intra-muscular fat content of venison, lamb, and beef. The established partial least squares regression (PLSR) models based on HLDF had the best performance in predicting pH value, while LLDF exhibited the best performance in predicting intra-muscular fat content.…”

Section: Meat and Aquatic Productsmentioning

confidence: 99%

“…Robert et al. (2021) compared data fusion strategies based on Raman and NIR for the prediction of the pH and intra‐muscular fat content of venison, lamb, and beef. The established partial least squares regression (PLSR) models based on HLDF had the best performance in predicting pH value, while LLDF exhibited the best performance in predicting intra‐muscular fat content.…”

Section: Recent Advances In Spectral Data Fusion In Food Analysismentioning

confidence: 99%

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Guo,

Wang,

Lin

et al. 2024

Comp Rev Food Sci Food Safe

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In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low‐level, mid‐level, and high‐level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra‐to‐spectra, spectra‐to‐machine vision, spectra‐to‐electronic nose, and spectra‐to‐nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real‐world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone‐based multi‐sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

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“…More diagnostic information may be uncovered by creating these stitched biofluid datasets, leading to a potential increase in diagnostic ability of our platform. This type of low-level data combination has been utilized before in the context of assessing pollutants in oils 39 , clay minerals 40 , or analyzing the purity of red meats to uncover food fraud 41 , 42 . Some initial work applying Raman spectra concatenation has been applied to biological samples, but this work was done by combining different types of data (in this case Raman and MALDI spectrometric imaging) 43 .…”

Section: Introductionmentioning

confidence: 99%

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

Koster

Guillén-Pérez²,

Gómez‐Díaz³

et al. 2022

Sci Rep

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As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

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Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat

Cited by 29 publications

References 31 publications

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

Rapid Quantitation of Adulterants in Premium Marine Oils by Raman and IR Spectroscopy: A Data Fusion Approach

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

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