Prediction of meat spectral patterns based on optical properties and concentrations of the major constituents

Abstract

A simulation method for approximating spectral signatures of minced meat samples was developed depending on concentrations and optical properties of the major chemical constituents. Minced beef samples of different compositions scanned on a near‐infrared spectroscopy and on a hyperspectral imaging system were examined. Chemical composition determined heuristically and optical properties collected from authenticated references were simulated to approximate samples' spectral signatures. In short‐wave infrared range, the resulting spectrum equals the sum of the absorption of three individual absorbers, that is, water, protein, and fat. By assuming homogeneous distributions of the main chromophores in the mince samples, the obtained absorption spectra are found to be a linear combination of the absorption spectra of the major chromophores present in the sample. Results revealed that developed models were good enough to derive spectral signatures of minced meat samples with a reasonable level of robustness of a high agreement index value more than 0.90 and ratio of performance to deviation more than 1.4.

Near‐infrared spectroscopy for metabolite quantification and species identification

“…The peaks at 920 nm, 1,040 nm, 1,140 nm, and 1,370 nm are characteristic of the 2nd overtone and the combination of C-H stretching. Notably, similar absorbance coefficient peaks around 920 nm and 1,040 nm were also observed in tissue samples with high-fat content (ElMasry & Nakauchi, 2016;Wilson, Nadeau, Jaworski, Tromberg, & Durkin, 2015). The peak at 1,820 nm shows the 1st overtone of C-H stretching, whereas peak on 1,900 nm corresponds to the absorption of COOC functional groups (Williams & Norris, 2001).…”

Prediction of beef color using time‐domain nuclear magnetic resonance (TD‐NMR) relaxometry data and multivariate analyses

“…These methods are based on infrared (mainly near infrared) and NMR spectroscopy and imaging. Near infrared signals processed with chemometric methods have been used to predicted luminosity (L*), ranging from 43.5 up to 67.5, for chicken meat with square error of cross‐validation equal to 1.99 and fat, moisture, and protein content …”

“…Near infrared signals processed with chemometric methods have been used to predicted luminosity (L*), ranging from 43.5 up to 67.5, for chicken meat with square error of cross-validation equal to 1.99 [5] and fat, moisture, and protein content. [6,7] 1 H-high resolution magic angle spinning-NMR spectroscopy has also been used as important tool for the characterization of intact biological tissues, including beef samples. [8,9] However, it is a very expensive method and is applied to very small sample mass, normally, less than 50 mg. For larger and intact food samples (up to kilogram), the NMR analyses have been performed in low-cost, lowfield, benchtop NMR spectrometer.…”

Avaliação da qualidade da carne bovina utilizando imagem hiperespectral no infravermelho próximo

“…Em relação à composição, picos de absorção da gordura foram reportados em 930 e 1.040 nm (estiramento e dobramento dos grupos metil e metileno -CH2 -em ácidos graxos), 1.200 nm (segundo sobretom do estiramento C -H em ácidos graxos), 1.715 e 1.750 nm (primeiro sobretom do estiramento C -H) e em 2.125, 2.300 e 2.340 nm (tons da combinação C-H) (PARK et al, 2001a;COZZOLINO et al, 2002;HOVING-BOLINK et al, 2005;NAKAUCHI, 2016 MATOBA, 1994;BEAUCHÊNE, 1997;CEN;HE, 2007;SUN;ALLEN, , 2012.…”

“…Mesmo após aplicação da seleção de variáveis ou utilização apenas das bandas de absorção atribuídas a gordura e proteína, conforme recomendado por Macdougall;Martens (1985) e Nakauchi (2016), no presente trabalho, a espectroscopia NIR associada a PLSR não produziu bons modelos para predição dessas características (Tabela 11).…”