Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

Abstract

Hyperspectral imaging in the visible and near infrared spectral range (450-1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and nonbrined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957-1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450-960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSE p 1.9%, R 2 p 0.92) and cooked (RMSE p 2.6%, R 2 p 0.83) samples used the 957-1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics

“…Moreover, a threshold of 0.5 was set between each group, and samples within the range of ±0.5 from any group were considered as classified in that group. More details of the PLS‐DA algorithm can be found in the literature …”

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

“…The use of a portable FT-NIR instrument coupled to a 5-m fibre optic sensor head for the accurate prediction of moisture, protein and fat in Iberian pig pork loins [175] and the authentication of Iberian ham quality were reported [176]. Other significant reported studies on the quality assessment of pork meat included the classification and prediction of nutritional parameters for different iberian pork meat products (e.g., dry-cured ham) [177], characterization of sous vide pork loin [178], and classification of brined pork samples and the prediction of brining salt concentrations [179]. Other related published studies include the rapid detection of total volatile basic nitrogen content in frozen pork [180] and the detection of bacterial foodborne pathogens in fresh pork muscles [181].…”

Feasibility of the Detection of Carrageenan Adulteration in Chicken Meat Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging

“…In the literature, there were three main approaches in constructing the visualization maps of the hyperspectral images. The simplest way was to directly visualize the grayscale image in the selected wavelengths or suitable PC score images [37,38]. As for the second way, this was to form a false color image composed of three different band images of selected wavelengths which acted as the RGB channels, respectively [39].…”