A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products

Abstract

Consumer demand for quality and healthfulness has led to a higher need for quality assurance in meat production. This requirement has increased interest in near-infrared (NIR) spectroscopy due to the ability for rapid, environmentally friendly, and noninvasive prediction of meat quality or authentication of added-value meat products. This review includes the principles of NIR spectroscopy, pre-processing methods, and multivariate analyses used for quantitative and qualitative purposes in the meat sector. Recent advances in portable NIR spectrometers that enable new online applications in the meat industry are shown and their performance evaluated. Discrepancies between published studies and potential sources of variability are discussed, and further research is encouraged to face the challenges of using NIRS technology in commercial applications, so that its full potential can be achieved.

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

“…NIR spectroscopy had been developed for the prediction of chemical composition, technological parameters, sensory attributes, and microorganisms count in food. [2,3] As a rapid analysis tool, it was used to predict the physicochemical composition of samples of different meat, [4][5][6] determine total volatile basic nitrogen (TVB-N) content and Warner-Bratzler shear force (WBSF) in pork, [7,8] discriminate the adult steers (oxen) and young cattle ground beef samples, [9] evaluate tenderness of pork loins, [10] evaluate freshness, [11][12][13] detect spoilage in sliced pork meat, [14] predict sensory characteristics of lamb meat, [15] and meat adulteration. [16] NIR spectroscopy technology had also been applied for detection of IMF content in meat.…”

Gravimetric and Spectrophotometric Determination of Surface Wax Content in Maize Kernels

“…In addition, samples have to be milled in order to reduce undesirable variations in spectral data, such as noise and baseline shift. In fact, earlier studies yielded unsuccessful results in determining the biochemical content of kernels when calibration models were developed based on the spectra taken on intact bulk seeds (Prieto et al, 2017). In our study, the surface wax content was analyzed using intact seeds, while the spectral data were collected on milled samples.…”

Novel applications of infrared technologies in dairy industry

“…The calibration is a regression model that uses spectral data as independent variables to obtain prediction of some chemical properties. Since spectra can show overlapping bands due to the physical or chemical complexity of the matrix and due to the fact that there is no a direct correlation between a single wavelength and a single property, a multivariate calibration is generally used to include several wavenumbers as predictors [9].…”

“…Software used to perform prediction models can automatically detect spectral outliers using different criteria. Before calibration, water absorption regions present in the spectra are removed to avoid interferences [9]. In particular, multiplicative scatter correction, standard normal variate, and detrending are widely used pre-processing techniques to drop noises, reduce variability between samples due to scatter and remove artifacts or imperfections from the data matrix before data modeling.…”

“…The capacity of infrared spectroscopy to predict compounds is based on its response to rotational and vibrational energies of the hydrogen bonds; for instance, the most important absorption bands which occur in the nearinfrared region are related to C-H, O-H, N-H and S-H functional groups [8]. In other words, the electromagnetic radiation absorbed from molecular bonds of a substrate produces a 'fingerprint' in the form of spectra [9].…”